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Learn JavaScript - Full Course for Beginners

Learn JavaScript - Full Course for Beginners

This complete 134-part JavaScript tutorial for beginners will teach you everything you need to know to get started with the JavaScript programming language. ✏️ Course created by @beau Click the ⚙️ to change to a dub track in Hindi (Dubbed via Melt Labs - https://www.withmelt.com/) ⭐️Curriculum⭐️ This is a stand-alone video but it follows the JavaScript curriculum at freecodecamp.org. Access the curriculum here: 🔗 Basic JavaScript: https://learn.freecodecamp.org/javascript-algorithms-and-data-structures/basic-javascript 🔗 ES6 JavaScript: https://learn.freecodecamp.org/javascript-algorithms-and-data-structures/es6 ⭐️Code⭐️ This course was created using scrimba.com. Access the course there along with the code: 🔗 Basic JavaScript: https://scrimba.com/playlist/pny4ghw 🔗 ES6 JavaScript: https://scrimba.com/playlist/p7v3gCd ⭐️Course Contents⭐️ 0:00:00 Introduction 0:01:24 Running JavaScript 0:04:23 Comment Your Code 0:05:56 Declare Variables 0:06:15 Storing Values with the Assignment Operator 0:11:31 Initializing Variables with the Assignment Operator 0:11:58 Uninitialized Variables 0:12:40 Case Sensitivity in Variables 0:14:05 Basic Math 0:15:30 Increment and Decrement 0:16:22 Decimal Numbers 0:16:48 Multiply Two Decimals 0:17:18 Divide Decimals 0:17:33 Finding a Remainder 0:18:22 Augmented Math Operations 0:21:19 Declare String Variables 0:22:01 Escaping Literal Quotes 0:23:44 Quoting Strings with Single Quotes 0:25:18 Escape Sequences 0:26:46 Plus Operator 0:27:49 Plus Equals Operator 0:29:01 Constructing Strings with Variables 0:30:14 Appending Variables to Strings 0:31:11 Length of a String 0:32:01 Bracket Notation 0:33:27 Understand String Immutability 0:34:23 Find the Nth Character 0:36:28 Word Blanks 0:40:44 Arrays 0:41:43 Nest Arrays 0:42:33 Access Array Data 0:43:34 Modify Array Data 0:44:48 Access Multi-Dimensional Arrays 0:46:30 push() 0:47:29 pop() 0:48:33 shift() 0:49:23 unshift() 0:50:36 Shopping List 0:51:41 Write Reusable with Functions 0:53:41 Arguments 0:55:43 Global Scope 0:59:31 Local Scope 1:00:46 Global vs Local Scope in Functions 1:02:40 Return a Value from a Function 1:03:55 Undefined Value returned 1:04:52 Assignment with a Returned Value 1:05:52 Stand in Line 1:08:41 Boolean Values 1:09:24 If Statements 1:11:51 Equality Operators 1:19:17 And / Or Operators 1:21:37 Else Statements 1:22:27 Else If Statements 1:23:30 Logical Order in If Else Statements 1:24:45 Chaining If Else Statements 1:27:45 Golf Code 1:32:15 Switch Statements 1:41:11 Returning Boolean Values from Functions 1:42:20 Return Early Pattern for Functions 1:43:38 Counting Cards 1:49:11 Build Objects 1:50:46 Dot Notation 1:51:33 Bracket Notation 1:52:47 Variables 1:53:34 Updating Object Properties 1:54:30 Add New Properties to Object 1:55:19 Delete Properties from Object 1:55:54 Objects for Lookups 1:57:43 Testing Objects for Properties 1:59:15 Manipulating Complex Objects 2:01:00 Nested Objects 2:01:53 Nested Arrays 2:03:06 Record Collection 2:10:15 While Loops 2:11:35 For Loops 2:13:56 Odd Numbers With a For Loop 2:15:28 Count Backwards With a For Loop 2:17:08 Iterate Through an Array with a For Loop 2:19:43 Nesting For Loops 2:22:45 Do...While Loops 2:24:12 Profile Lookup 2:28:18 Random Fractions and Whole Numbers 2:31:46 parseInt Function 2:33:29 Ternary Operator 2:34:57 Multiple Ternary Operators 2:36:57 var vs let 2:41:32 const Keyword 2:43:40 Mutate an Array Declared with const 2:44:52 Prevent Object Mutation 2:47:17 Arrow Functions 2:53:04 Default Parameters 2:54:00 Rest Operator 2:55:31 Spread Operator 2:57:18 Destructuring Assignment 3:06:39 Template Literals 3:10:43 Simple Fields 3:12:24 Declarative Functions 3:12:56 class Syntax 3:15:11 getters and setters 3:20:25 import and export 🎥 Want something shorter? Here's a 63-second JavaScript course: https://www.youtube.com/watch?v=OXiyLaNo3NE #javascript tutorial #javascript #javascript tutorial for beginners #programming tutorial #learn javascript for beginners #javascript course #java script tutorial #js tutorial #java scripting tutorial for beginners #javascript tutorials #javascript for beginners #java script #javascript for beginners 2018 #programming tutorial javascript #javascript (programming language) #javascript course for beginners #javascript crash course #learn to code #learn to program #coding tutorial
2018年12月10日
00:00:00 - 03:26:43
Data Science Internship Program | Why Become a Data Scientist | Edureka

Data Science Internship Program | Why Become a Data Scientist | Edureka

🔴 𝐋𝐞𝐚𝐫𝐧 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐅𝐨𝐫 𝐅𝐫𝐞𝐞! 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐂𝐡𝐚𝐧𝐧𝐞𝐥: https://edrk.in/DKQQ4Py 🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚'𝐬 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐅𝐫𝐞𝐞 𝐃𝐞𝐦𝐨 𝐒𝐞𝐬𝐬𝐢𝐨𝐧: https://www.edureka.co/internship/data-science-and-machine-learning-program In this video on Data Science Internship Program, we take you through some facts which will help you understand why Data Science is the job of the century, and Why you should Become a Data Scientist. 📝Feel free to leave any queries or comments in the comment section below, we will be happy to answer📝 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐎𝐧𝐥𝐢𝐧𝐞 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠 𝐚𝐧𝐝 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐜𝐚𝐭𝐢𝐨𝐧 🔵 DevOps Online Training: https://bit.ly/38Fx6Cx 🌕 AWS Online Training: https://bit.ly/3DZkXDR 🔵 Azure DevOps Online Training: https://bit.ly/37s448u 🌕 Tableau Online Training: https://bit.ly/37oHjCy 🔵 Power BI Online Training: https://bit.ly/3rbpI8e 🌕 Selenium Online Training: https://bit.ly/38uou1b 🔵 PMP Online Training: https://bit.ly/3LJywtI 🌕 Salesforce Online Training: https://bit.ly/3LQzEvS 🔵 Cybersecurity Online Training: https://bit.ly/3uZkDRz 🌕 Java Online Training: https://bit.ly/3LRDN2i 🔵 Big Data Online Training: https://bit.ly/3JfoleQ 🌕 RPA Online Training: https://bit.ly/35QtmwH 🔵 Python Online Training: https://bit.ly/3DS1dC4 🌕 Azure Online Training: https://bit.ly/3v0cKLD 🔵 GCP Online Training: https://bit.ly/3jiQLKw 🌕 Microservices Online Training: https://bit.ly/37n4GMN 🔵 Data Science Online Training: https://bit.ly/3Jsy9T2 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐑𝐨𝐥𝐞-𝐁𝐚𝐬𝐞𝐝 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 🔵 DevOps Engineer Masters Program: https://bit.ly/3NUTAPS 🌕 Cloud Architect Masters Program: https://bit.ly/3DUVvzy 🔵 Data Scientist Masters Program: https://bit.ly/3v6RTq9 🌕 Big Data Architect Masters Program: https://bit.ly/3uiEJXO 🔵 Machine Learning Engineer Masters Program: https://bit.ly/3Km3Iz9 🌕 Business Intelligence Masters Program: https://bit.ly/35RQ7R3 🔵 Python Developer Masters Program: https://bit.ly/3rxAPIX 🌕 RPA Developer Masters Program: https://bit.ly/3LNiHSJ 🔵 Web Development Masters Program: https://bit.ly/3ukSpBy 🌕 Computer Science Bootcamp Program : https://bit.ly/38FA9KZ 🔵 Cyber Security Masters Program: https://bit.ly/3NVqYGh 🌕 Full Stack Developer Masters Program : https://bit.ly/3LTxV93 🔵 Automation Testing Engineer Masters Program : https://bit.ly/3rxAPIX 🌕 Python Developer Masters Program : https://bit.ly/3ucD3yX 🔵 Azure Cloud Engineer Masters Program: https://bit.ly/3LTxZ8N 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐏𝗼𝘀𝘁 𝗚𝗿𝗮𝗱𝘂𝗮𝘁𝗲 𝐂𝐨𝐮𝐫𝐬𝐞𝐬 🌕 Post Graduate Program in DevOps with Purdue University: https://bit.ly/3LP7h0Z 📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates 📌𝐓𝐰𝐢𝐭𝐭𝐞𝐫: https://twitter.com/edurekain 📌𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: https://www.linkedin.com/company/edureka 📌𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦: https://www.instagram.com/edureka_learning/ 📌𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: https://www.facebook.com/edurekaIN/ 📌𝐒𝐥𝐢𝐝𝐞𝐒𝐡𝐚𝐫𝐞: https://www.slideshare.net/EdurekaIN 📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: https://castbox.fm/networks/505?country=IN 📌𝐌𝐞𝐞𝐭𝐮𝐩: https://www.meetup.com/edureka/ 📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/ What are the prerequisites to enroll in this course? No pre-requisites are required to enroll in this Data Science & Machine Learning program. You will be taught Python programming from the very basics, then Machine learning, then Deep Learning and Tableau. All you need is to have basic computer skills, dedication and proactiveness to learn. Can I master Data Science and Machine Learning in such a short duration? Yes, the internship programs by Edureka are specially designed to make experts in the most trending fields and technologies in a short period of time. The content for this course is created by Technology Specialists, Data Science & Machine Learning Experts and Engineering Managers from top tech companies. Based on their years of experience and domain knowledge, they have chosen the most important concepts in an easily understandable format in this single course. These key concepts are fundamental to becoming a top Data Scientist and cracking interviews in top tech companies. This program will follow a similar training structure as followed by top tech companies and will also cover important details from their interviews. #data science internship #why become a data scientist #data science #edureka
2022年04月08日
00:00:00 - 00:01:27
Stanford CS25: V4 I Hyung Won Chung of OpenAI

Stanford CS25: V4 I Hyung Won Chung of OpenAI

April 11, 2024 Speaker: Hyung Won Chung, OpenAI Shaping the Future of AI from the History of Transformer AI is developing at such an overwhelming pace that it is hard to keep up. Instead of spending all our energy catching up with the latest development, I argue that we should study the change itself. First step is to identify and understand the driving force behind the change. For AI, it is the exponentially cheaper compute and associated scaling. I will provide a highly-opinionated view on the early history of Transformer architectures, focusing on what motivated each development and how each became less relevant with more compute. This analysis will help us connect the past and present in a unified perspective, which in turn makes it more manageable to project where the field is heading. Slides here: https://docs.google.com/presentation/d/1u05yQQaw4QXLVYGLI6o3YoFHv6eC3YN8GvWD8JMumpE/edit#slide=id.g2885e521b53_0_0 0:00 Introduction 2:05 Identifying and understanding the dominant driving force behind AI. 15:18 Overview of Transformer architectures: encoder-decoder, encoder-only and decoder-only 23:29 Differences between encoder-decoder and decoder-only, and rationale for encoder-decoder’s additional structures from the perspective of scaling. About the speaker: Hyung Won Chung is a research scientist at OpenAI ChatGPT team. He has worked on various aspects of Large Language Models: pre-training, instruction fine-tuning, reinforcement learning with human feedback, reasoning, multilinguality, parallelism strategies, etc. Some of the notable work includes scaling Flan paper (Flan-T5, Flan-PaLM) and T5X, the training framework used to train the PaLM language model. Before OpenAI, he was at Google Brain and before that he received a PhD from MIT. More about the course can be found here: https://web.stanford.edu/class/cs25/ View the entire CS25 Transformers United playlist: https://www.youtube.com/playlist?list=PLoROMvodv4rNiJRchCzutFw5ItR_Z27CM #Stanford #Stanford Online
2024年06月12日
00:00:00 - 00:36:31
Computer & Technology Basics Course for Absolute Beginners

Computer & Technology Basics Course for Absolute Beginners

Learn basic computer and technology skills. This course is for people new to working with computers or people that want to fill in some gaps about their computer knowledge. ✏️ This course was developed by GCF Global. Check out their YouTube channel: https://www.youtube.com/c/GcflearnfreeOrgplus 🔗 GCF Global offers more free learning resources at their website: https://www.GCFLearnFree.org ⭐️ Course Contents ⭐️ ⌨️ (00:00:00) Introduction ⌨️ (00:00:55) What Is a Computer? ⌨️ (00:03:37) Buttons and Ports on a Computer ⌨️ (00:06:01) Basic Parts of a Computer ⌨️ (00:08:47) Inside a Computer ⌨️ (00:10:58) Getting to Know Laptop Computers ⌨️ (00:12:55) Understanding Operating Systems ⌨️ (00:14:21) Understanding Applications ⌨️ (00:15:53) Setting Up a Desktop Computer ⌨️ (00:18:47) Connecting to the Internet ⌨️ (00:22:41) What Is the Cloud? ⌨️ (00:25:06) Cleaning Your Computer ⌨️ (00:29:02) Protecting Your Computer ⌨️ (00:32:17) Creating a Safe Workspace ⌨️ (00:36:25) Internet Safety: Your Browser's Security Features ⌨️ (00:38:36) Understanding Spam and Phishing ⌨️ (00:43:27) Understanding Digital Tracking ⌨️ (00:45:39) Windows Basics: Getting Started with the Desktop ⌨️ (00:47:40) Mac OS X Basics: Getting Started with the Desktop ⌨️ (00:52:26) Browser Basics English This video has been dubbed using an artificial voice via https://aloud.area120.google.com to increase accessibility. You can change the audio track language in the Settings menu. Spanish Este video ha sido doblado al español con voz artificial con https://aloud.area120.google.com para aumentar la accesibilidad. Puede cambiar el idioma de la pista de audio en el menú Configuración. Portuguese Este vídeo foi dublado para o português usando uma voz artificial via https://aloud.area120.google.com para melhorar sua acessibilidade. Você pode alterar o idioma do áudio no menu Configurações. 🎉 Thanks to our Champion and Sponsor supporters: 👾 Nattira Maneerat 👾 Heather Wcislo 👾 Serhiy Kalinets 👾 Erdeniz Unvan 👾 Justin Hual 👾 Agustín Kussrow 👾 Otis Morgan -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news
2023年02月03日
00:00:00 - 00:55:04
PyTorch for Deep Learning & Machine Learning – Full Course

PyTorch for Deep Learning & Machine Learning – Full Course

Learn PyTorch for deep learning in this comprehensive course for beginners. PyTorch is a machine learning framework written in Python. ✏️ Daniel Bourke developed this course. Check out his channel: https://www.youtube.com/channel/UCr8O8l5cCX85Oem1d18EezQ 🔗 Code: https://github.com/mrdbourke/pytorch-deep-learning 🔗 Ask a question: https://github.com/mrdbourke/pytorch-deep-learning/discussions 🔗 Course materials online: https://learnpytorch.io 🔗 Full course on Zero to Mastery (20+ hours more video): https://dbourke.link/ZTMPyTorch Some sections below have been left out because of the YouTube limit for timestamps. 0:00:00 Introduction 🛠 Chapter 0 – PyTorch Fundamentals 0:01:45 0. Welcome and "what is deep learning?" 0:07:41 1. Why use machine/deep learning? 0:11:15 2. The number one rule of ML 0:16:55 3. Machine learning vs deep learning 0:23:02 4. Anatomy of neural networks 0:32:24 5. Different learning paradigms 0:36:56 6. What can deep learning be used for? 0:43:18 7. What is/why PyTorch? 0:53:33 8. What are tensors? 0:57:52 9. Outline 1:03:56 10. How to (and how not to) approach this course 1:09:05 11. Important resources 1:14:28 12. Getting setup 1:22:08 13. Introduction to tensors 1:35:35 14. Creating tensors 1:54:01 17. Tensor datatypes 2:03:26 18. Tensor attributes (information about tensors) 2:11:50 19. Manipulating tensors 2:17:50 20. Matrix multiplication 2:48:18 23. Finding the min, max, mean & sum 2:57:48 25. Reshaping, viewing and stacking 3:11:31 26. Squeezing, unsqueezing and permuting 3:23:28 27. Selecting data (indexing) 3:33:01 28. PyTorch and NumPy 3:42:10 29. Reproducibility 3:52:58 30. Accessing a GPU 4:04:49 31. Setting up device agnostic code 🗺 Chapter 1 – PyTorch Workflow 4:17:27 33. Introduction to PyTorch Workflow 4:20:14 34. Getting setup 4:27:30 35. Creating a dataset with linear regression 4:37:12 36. Creating training and test sets (the most important concept in ML) 4:53:18 38. Creating our first PyTorch model 5:13:41 40. Discussing important model building classes 5:20:09 41. Checking out the internals of our model 5:30:01 42. Making predictions with our model 5:41:15 43. Training a model with PyTorch (intuition building) 5:49:31 44. Setting up a loss function and optimizer 6:02:24 45. PyTorch training loop intuition 6:40:05 48. Running our training loop epoch by epoch 6:49:31 49. Writing testing loop code 7:15:53 51. Saving/loading a model 7:44:28 54. Putting everything together 🤨 Chapter 2 – Neural Network Classification 8:32:00 60. Introduction to machine learning classification 8:41:42 61. Classification input and outputs 8:50:50 62. Architecture of a classification neural network 9:09:41 64. Turing our data into tensors 9:25:58 66. Coding a neural network for classification data 9:43:55 68. Using torch.nn.Sequential 9:57:13 69. Loss, optimizer and evaluation functions for classification 10:12:05 70. From model logits to prediction probabilities to prediction labels 10:28:13 71. Train and test loops 10:57:55 73. Discussing options to improve a model 11:27:52 76. Creating a straight line dataset 11:46:02 78. Evaluating our model's predictions 11:51:26 79. The missing piece – non-linearity 12:42:32 84. Putting it all together with a multiclass problem 13:24:09 88. Troubleshooting a mutli-class model 😎 Chapter 3 – Computer Vision 14:00:48 92. Introduction to computer vision 14:12:36 93. Computer vision input and outputs 14:22:46 94. What is a convolutional neural network? 14:27:49 95. TorchVision 14:37:10 96. Getting a computer vision dataset 15:01:34 98. Mini-batches 15:08:52 99. Creating DataLoaders 15:52:01 103. Training and testing loops for batched data 16:26:27 105. Running experiments on the GPU 16:30:14 106. Creating a model with non-linear functions 16:42:23 108. Creating a train/test loop 17:13:32 112. Convolutional neural networks (overview) 17:21:57 113. Coding a CNN 17:41:46 114. Breaking down nn.Conv2d/nn.MaxPool2d 18:29:02 118. Training our first CNN 18:44:22 120. Making predictions on random test samples 18:56:01 121. Plotting our best model predictions 19:19:34 123. Evaluating model predictions with a confusion matrix 🗃 Chapter 4 – Custom Datasets 19:44:05 126. Introduction to custom datasets 19:59:54 128. Downloading a custom dataset of pizza, steak and sushi images 20:13:59 129. Becoming one with the data 20:39:11 132. Turning images into tensors 21:16:16 136. Creating image DataLoaders 21:25:20 137. Creating a custom dataset class (overview) 21:42:29 139. Writing a custom dataset class from scratch 22:21:50 142. Turning custom datasets into DataLoaders 22:28:50 143. Data augmentation 22:43:14 144. Building a baseline model 23:11:07 147. Getting a summary of our model with torchinfo 23:17:46 148. Creating training and testing loop functions 23:50:59 151. Plotting model 0 loss curves 24:00:02 152. Overfitting and underfitting 24:32:31 155. Plotting model 1 loss curves 24:35:53 156. Plotting all the loss curves 24:46:50 157. Predicting on custom data
2022年10月06日
00:00:00 - 25:37:26
The Best LEARNING Book in History - 40 Years AHEAD of its Time

The Best LEARNING Book in History - 40 Years AHEAD of its Time

Visit https://brilliant.org/PythonProgrammer/ to get started for free (and if you're one of the first 200 people to click the link you'll get an extra 20% off too) 😃 William Zinsser wrote many articles and books. You can buy the 2 mentioned in the video: US Writing to Learn - https://amzn.to/3wuACLP On Writing Well - https://amzn.to/3OZ9Xx8 UK Writing to Learn (kindle) - https://amzn.to/3SR832A On Writing Well - https://amzn.to/49OOPRU Join this channel to get access to perks: https://www.youtube.com/channel/UC68KSmHePPePCjW4v57VPQg/join 🍿 WATCH NEXT: Want to Read Faster? Watch this - https://youtu.be/5RfMMBTLDms Become a Super Learner using science - https://youtu.be/TDYa2pPMx0k ⌚️Timestamps: 00:00 Introduction 00:24 Who is William Zinsser? 00:58 What's the Book About? 01:39 Hate Writing? You're not Alone 02:14 What's in the Book? 03:23 Criticisms of the Book 03:45 The Insight of Zinsser's Intuition 04:07 Scientific Learning Techniques 04:25 On Writing Well 04:47 Thanks Brilliant! Learn Data Science (affiliate link) 🎓 Data Quest - https://bit.ly/3yClqbZ Learn Python with Giles 🎓 Exploratory Data Analysis with Python and Pandas - https://bit.ly/2QXMpxJ 🎓 Complete Python Programmer Bootcamp - http://bit.ly/2OwUA09 📚 My favourite python books for beginners (affiliate links) 📗 Python Crash Course 2nd Edition https://amzn.to/33tATAE 📘 Automate the Boring Stuff with Python https://amzn.to/3qM1DFl 📙 Python Basics - A Practical Introduction to Python 3 https://amzn.to/3fHRMdb 📕 Python Programming An Introduction to Computer Science https://amzn.to/33VeQCr 📗 Invent Your Own Computer Games with Python https://amzn.to/3FM3H4b 🆓 Free Python Resource https://python-programming.quantecon.org/intro.html (This is a great introduction to python) ⚙ My Gear 💡 BenQ Screen Bar Desk Light - https://amzn.to/3tH6ysL 🎧 Sony Noise Cancelling Headphones - https://amzn.to/3tLl82G 📱 Social Media https://www.instagram.com/gilesmcmullen/ https://twitter.com/GilesMcMullen 👌 SUBSCRIBE to ME!👌 https://www.youtube.com/channel/UC68KSmHePPePCjW4v57VPQg?sub_confirmation=1 I am an Amazon, Coursera and DataQuest affiliate program member, this means I earn a commission from qualifying purchases on the some of the above links. It costs you nothing but helps me with content creation.
2024年02月27日
00:00:00 - 00:06:05
Data Structures Easy to Advanced Course - Full Tutorial from a Google Engineer

Data Structures Easy to Advanced Course - Full Tutorial from a Google Engineer

Learn and master the most common data structures in this full course from Google engineer William Fiset. This course teaches data structures to beginners using high quality animations to represent the data structures visually. You will learn how to code various data structures together with simple to follow step-by-step instructions. Every data structure presented will be accompanied by some working source code (in Java) to solidify your understanding. 💻 Code: https://github.com/williamfiset/data-structures 🎥 Course created by William Fiset. Check out his YouTube channel: https://www.youtube.com/channel/UCD8yeTczadqdARzQUp29PJw ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Abstract data types ⌨️ (0:04:28) Introduction to Big-O ⌨️ (0:17:00) Dynamic and Static Arrays ⌨️ (0:27:40) Dynamic Array Code ⌨️ (0:35:03) Linked Lists Introduction ⌨️ (0:49:16) Doubly Linked List Code ⌨️ (0:58:26) Stack Introduction ⌨️ (1:09:40) Stack Implementation ⌨️ (1:12:49) Stack Code ⌨️ (1:15:58) Queue Introduction ⌨️ (1:22:03) Queue Implementation ⌨️ (1:27:26) Queue Code ⌨️ (1:31:32) Priority Queue Introduction ⌨️ (1:44:16) Priority Queue Min Heaps and Max Heaps ⌨️ (1:49:55) Priority Queue Inserting Elements ⌨️ (1:59:27) Priority Queue Removing Elements ⌨️ (2:13:00) Priority Queue Code ⌨️ (2:28:26) Union Find Introduction ⌨️ (2:33:57) Union Find Kruskal's Algorithm ⌨️ (2:40:04) Union Find - Union and Find Operations ⌨️ (2:50:30) Union Find Path Compression ⌨️ (2:56:37) Union Find Code ⌨️ (3:03:54) Binary Search Tree Introduction ⌨️ (3:15:57) Binary Search Tree Insertion ⌨️ (3:21:20) Binary Search Tree Removal ⌨️ (3:34:47) Binary Search Tree Traversals ⌨️ (3:46:17) Binary Search Tree Code ⌨️ (3:59:26) Hash table hash function ⌨️ (4:16:25) Hash table separate chaining ⌨️ (4:24:10) Hash table separate chaining source code ⌨️ (4:35:44) Hash table open addressing ⌨️ (4:46:36) Hash table linear probing ⌨️ (5:00:21) Hash table quadratic probing ⌨️ (5:09:32) Hash table double hashing ⌨️ (5:23:56) Hash table open addressing removing ⌨️ (5:31:02) Hash table open addressing code ⌨️ (5:45:36) Fenwick Tree range queries ⌨️ (5:58:46) Fenwick Tree point updates ⌨️ (6:03:09) Fenwick Tree construction ⌨️ (6:09:21) Fenwick tree source code ⌨️ (6:14:47) Suffix Array introduction ⌨️ (6:17:54) Longest Common Prefix (LCP) array ⌨️ (6:21:07) Suffix array finding unique substrings ⌨️ (6:25:36) Longest common substring problem suffix array ⌨️ (6:37:04) Longest common substring problem suffix array part 2 ⌨️ (6:43:41) Longest Repeated Substring suffix array ⌨️ (6:48:13) Balanced binary search tree rotations ⌨️ (6:56:43) AVL tree insertion ⌨️ (7:05:42) AVL tree removals ⌨️ (7:14:12) AVL tree source code ⌨️ (7:30:49) Indexed Priority Queue | Data Structure ⌨️ (7:55:10) Indexed Priority Queue | Data Structure | Source Code -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://www.freecodecamp.org/news #data structures and algorithms #data structures tutorial #algorithms tutorial #linked lists #arrays #hash maps #hash tables #java #data structures course #data structues #java data structures #programming #programmer #whiteboard interview #cracking the coding interview #java algorithms #google engineer
2019年09月19日
00:00:00 - 08:03:17
Learn RAG From Scratch – Python AI Tutorial from a LangChain Engineer

Learn RAG From Scratch – Python AI Tutorial from a LangChain Engineer

Learn how to implement RAG (Retrieval Augmented Generation) from scratch, straight from a LangChain software engineer. This Python course teaches you how to use RAG to combine your own custom data with the power of Large Language Models (LLMs). 💻 Code: https://github.com/langchain-ai/rag-from-scratch If you're completely new to LangChain and want to learn about some fundamentals, check out our guide for beginners: https://www.freecodecamp.org/news/beginners-guide-to-langchain/ ✏️ Course created by Lance Martin, PhD. Lance on X: https://twitter.com/rlancemartin ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Overview ⌨️ (0:05:53) Indexing ⌨️ (0:10:40) Retrieval ⌨️ (0:15:52) Generation ⌨️ (0:22:14) Query Translation (Multi-Query) ⌨️ (0:28:20) Query Translation (RAG Fusion) ⌨️ (0:33:57) Query Translation (Decomposition) ⌨️ (0:40:31) Query Translation (Step Back) ⌨️ (0:47:24) Query Translation (HyDE) ⌨️ (0:52:07) Routing ⌨️ (0:59:08) Query Construction ⌨️ (1:05:05) Indexing (Multi Representation) ⌨️ (1:11:39) Indexing (RAPTOR) ⌨️ (1:19:19) Indexing (ColBERT) ⌨️ (1:26:32) CRAG ⌨️ (1:44:09) Adaptive RAG ⌨️ (2:12:02) The future of RAG 🎉 Thanks to our Champion and Sponsor supporters: 👾 davthecoder 👾 jedi-or-sith 👾 南宮千影 👾 Agustín Kussrow 👾 Nattira Maneerat 👾 Heather Wcislo 👾 Serhiy Kalinets 👾 Justin Hual 👾 Otis Morgan 👾 Oscar Rahnama -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news
2024年04月17日
00:00:00 - 02:33:11
Full Ethical Hacking Course - Network Penetration Testing for Beginners (2019)

Full Ethical Hacking Course - Network Penetration Testing for Beginners (2019)

Learn network penetration testing / ethical hacking in this full tutorial course for beginners. This course teaches everything you need to know to get started with ethical hacking and penetration testing. You will learn the practical skills necessary to work in the field. Throughout the course, we will develop our own Active Directory lab in Windows, make it vulnerable, hack it, and patch it. We'll cover the red and blue sides. We'll also cover some of the boring stuff like report writing :). This course was originally live streamed weekly on Twitch and built from lessons learned in the previous week. 💻 GitHub repo (for homework): https://github.com/hmaverickadams/Beginner-Network-Pentesting 🎥 Course created by The Cyber Mentor. Check out his YouTube channel: https://www.youtube.com/channel/UC0ArlFuFYMpEewyRBzdLHiw 🐦 The Cyber Mentor on Twitter: https://twitter.com/thecybermentor ⭐️ Course Contents ⭐️ ⌨️ (0:00) - Course Introduction/whoami ⌨️ (6:12) - Part 1: Introduction, Notekeeping, and Introductory Linux ⌨️ (1:43:45) - Part 2: Python 101 ⌨️ (3:10:05) - Part 3: Python 102 (Building a Terrible Port Scanner) ⌨️ (4:23:14) - Part 4: Passive OSINT ⌨️ (5:41:41) - Part 5: Scanning Tools & Tactics ⌨️ (6:56:42) - Part 6: Enumeration ⌨️ (8:31:22) - Part 7: Exploitation, Shells, and Some Credential Stuffing ⌨️ (9:57:15) - Part 8: Building an AD Lab, LLMNR Poisoning, and NTLMv2 Cracking with Hashcat ⌨️ (11:13:20) - Part 9: NTLM Relay, Token Impersonation, Pass the Hash, PsExec, and more ⌨️ (12:40:46) - Part 10: MS17-010, GPP/cPasswords, and Kerberoasting ⌨️ (13:32:33) - Part 11: File Transfers, Pivoting, Report Writing, and Career Advice -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://www.freecodecamp.org/news #twitch #hacker #hacking #ethical hacker #penetration tester #tester #testing #pentest #pentester #zero to hero #enumeration #hackthebox #OSCP #nmap #nikto #security #cybersecurity #infosec #information security #m4v3r1ck #pivoting #report writing #notekeeping #python #port scanning #burp suite #shells #reverse shell #ntlm relay #llmnr poisoning #smb relay #pass the hash #kerberoasting #gpp #cpasswords #token impersonation #psexec #metasploit #hacking tutorial #hacking course #hacking for beginners
2019年07月30日
00:00:00 - 14:51:14
Create a Large Language Model from Scratch with Python – Tutorial

Create a Large Language Model from Scratch with Python – Tutorial

Learn how to build your own large language model, from scratch. This course goes into the data handling, math, and transformers behind large language models. You will use Python. ✏️ Course developed by @elliotarledge 💻 Code and course resources: https://github.com/Infatoshi/fcc-intro-to-llms Join Elliot's Discord server: https://discord.gg/pV7ByF9VNm Elliot on X: https://twitter.com/elliotarledge ⭐️ Contents ⭐️ (0:00:00) Intro (0:03:25) Install Libraries (0:06:24) Pylzma build tools (0:08:58) Jupyter Notebook (0:12:11) Download wizard of oz (0:14:51) Experimenting with text file (0:17:58) Character-level tokenizer (0:19:44) Types of tokenizers (0:20:58) Tensors instead of Arrays (0:22:37) Linear Algebra heads up (0:23:29) Train and validation splits (0:25:30) Premise of Bigram Model (0:26:41) Inputs and Targets (0:29:29) Inputs and Targets Implementation (0:30:10) Batch size hyperparameter (0:32:13) Switching from CPU to CUDA (0:33:28) PyTorch Overview (0:42:49) CPU vs GPU performance in PyTorch (0:47:49) More PyTorch Functions (1:06:03) Embedding Vectors (1:11:33) Embedding Implementation (1:13:06) Dot Product and Matrix Multiplication (1:25:42) Matmul Implementation (1:26:56) Int vs Float (1:29:52) Recap and get_batch (1:35:07) nnModule subclass (1:37:05) Gradient Descent (1:50:53) Logits and Reshaping (1:59:28) Generate function and giving the model some context (2:03:58) Logits Dimensionality (2:05:17) Training loop + Optimizer + Zerograd explanation (2:13:56) Optimizers Overview (2:17:04) Applications of Optimizers (2:18:11) Loss reporting + Train VS Eval mode (2:32:54) Normalization Overview (2:35:45) ReLU, Sigmoid, Tanh Activations (2:45:15) Transformer and Self-Attention (2:46:55) Transformer Architecture (3:17:54) Building a GPT, not Transformer model (3:19:46) Self-Attention Deep Dive (3:25:05) GPT architecture (3:27:07) Switching to Macbook (3:31:42) Implementing Positional Encoding (3:36:57) GPTLanguageModel initalization (3:40:52) GPTLanguageModel forward pass (3:46:56) Standard Deviation for model parameters (4:00:50) Transformer Blocks (4:04:54) FeedForward network (4:07:53) Multi-head Attention (4:12:49) Dot product attention (4:19:43) Why we scale by 1/sqrt(dk) (4:26:45) Sequential VS ModuleList Processing (4:30:47) Overview Hyperparameters (4:32:14) Fixing errors, refining (4:34:01) Begin training (4:35:46) OpenWebText download and Survey of LLMs paper (4:37:56) How the dataloader/batch getter will have to change (4:41:20) Extract corpus with winrar (4:43:44) Python data extractor (4:49:23) Adjusting for train and val splits (4:57:55) Adding dataloader (4:59:04) Training on OpenWebText (5:02:22) Training works well, model loading/saving (5:04:18) Pickling (5:05:32) Fixing errors + GPU Memory in task manager (5:14:05) Command line argument parsing (5:18:11) Porting code to script (5:22:04) Prompt: Completion feature + more errors (5:24:23) nnModule inheritance + generation cropping (5:27:54) Pretraining vs Finetuning (5:33:07) R&D pointers (5:44:38) Outro 🎉 Thanks to our Champion and Sponsor supporters: 👾 davthecoder 👾 jedi-or-sith 👾 南宮千影 👾 Agustín Kussrow 👾 Nattira Maneerat 👾 Heather Wcislo 👾 Serhiy Kalinets 👾 Justin Hual 👾 Otis Morgan -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news
2023年08月26日
00:00:00 - 05:43:41
Reinforcement Learning Course - Full Machine Learning Tutorial

Reinforcement Learning Course - Full Machine Learning Tutorial

Reinforcement learning is an area of machine learning that involves taking right action to maximize reward in a particular situation. In this full tutorial course, you will get a solid foundation in reinforcement learning core topics. The course covers Q learning, SARSA, double Q learning, deep Q learning, and policy gradient methods. These algorithms are employed in a number of environments from the open AI gym, including space invaders, breakout, and others. The deep learning portion uses Tensorflow and PyTorch. The course begins with more modern algorithms, such as deep q learning and policy gradient methods, and demonstrates the power of reinforcement learning. Then the course teaches some of the fundamental concepts that power all reinforcement learning algorithms. These are illustrated by coding up some algorithms that predate deep learning, but are still foundational to the cutting edge. These are studied in some of the more traditional environments from the OpenAI gym, like the cart pole problem. 💻Code: https://github.com/philtabor/Youtube-Code-Repository/tree/master/ReinforcementLearning ⭐️ Course Contents ⭐️ ⌨️ (00:00:00) Intro ⌨️ (00:01:30) Intro to Deep Q Learning ⌨️ (00:08:56) How to Code Deep Q Learning in Tensorflow ⌨️ (00:52:03) Deep Q Learning with Pytorch Part 1: The Q Network ⌨️ (01:06:21) Deep Q Learning with Pytorch part 2: Coding the Agent ⌨️ (01:28:54) Deep Q Learning with Pytorch part ⌨️ (01:46:39) Intro to Policy Gradients 3: Coding the main loop ⌨️ (01:55:01) How to Beat Lunar Lander with Policy Gradients ⌨️ (02:21:32) How to Beat Space Invaders with Policy Gradients ⌨️ (02:34:41) How to Create Your Own Reinforcement Learning Environment Part 1 ⌨️ (02:55:39) How to Create Your Own Reinforcement Learning Environment Part 2 ⌨️ (03:08:20) Fundamentals of Reinforcement Learning ⌨️ (03:17:09) Markov Decision Processes ⌨️ (03:23:02) The Explore Exploit Dilemma ⌨️ (03:29:19) Reinforcement Learning in the Open AI Gym: SARSA ⌨️ (03:39:56) Reinforcement Learning in the Open AI Gym: Double Q Learning ⌨️ (03:54:07) Conclusion Course from Machine Learning with Phil. Check out his YouTube channel: https://www.youtube.com/channel/UC58v9cLitc8VaCjrcKyAbrw -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://medium.freecodecamp.org #reinforcement learning #machine learning #machine learning course #reinforcement learning course #deep q network #deep q-learning algorithm #deep learning game #deep q learning tutorial #q-learning #deep reinforcement learning #q learning #neural network #python #pytorch #python tutorial #deep learning #reinforcement learning python #artificial intelligence
2019年05月14日
00:00:00 - 03:55:27
Please Master These 10 Python Functions…

Please Master These 10 Python Functions…

Get started with Mailtrap today! https://l.rw.rw/tech_with_tim In this video, I will dive into 10 Python Functions that you NEED to master. Some of them may seem simple, but they have a lot of additional parameters that you need to understand and mastering all of the functions can save you a ton of time when writing any type of code in Python. If you want to land a developer job check out my program with CourseCareers: https://techwithtim.net/dev ⏳ Timestamps ⏳ 00:00 | Overview 00:16 | Function #1 - print 02:23 | Function #2 - help 04:28 | Function #3 - range 06:38 | Function #4 - map 09:12 | Function #5 - filter 10:57 | Function #6 - sum 11:41 | Function #7 - sorted 13:31 | Function #8 - enumerate 15:10 | Function #9 - zip 18:00 | Function #10 - open Hashtags #techwithtim #python #pythonprogramming #coding #codingtips #tech with tim #python functions examples #python functions tutorial #python functions and procedures #python functions #python function tutorial #python functions and methods #python function #python define function #functions in python #python programming #coding #coding tips #python #python function parameters
2024年06月07日
00:00:00 - 00:22:17
Intermediate Python Programming Course

Intermediate Python Programming Course

Take your Python skills to the next level with this intermediate Python course. First, you will get a review of basic concepts such as lists, strings, and dictionaries, but with an emphasis on some lesser known capabilities. Then, you will learn more advanced topics such as threading, multiprocessing, context managers, generators, and more. 💻 Code: https://github.com/python-engineer/python-engineer-notebooks/tree/master/advanced-python 🎥 Course from Patrick Loeber. Check out his channel: https://www.youtube.com/channel/UCbXgNpp0jedKWcQiULLbDTA 🔗 Written Tutorials from Patrick: https://www.python-engineer.com/courses/advancedpython/ ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Intro ⌨️ (0:00:56) Lists ⌨️ (0:16:30) Tuples ⌨️ (0:29:49) Dictionaries ⌨️ (0:42:40) Sets ⌨️ (0:58:44) Strings ⌨️ (1:22:50) Collections ⌨️ (1:36:43) Itertools ⌨️ (1:51:50) Lambda Functions ⌨️ (2:04:03) Exceptions and Errors ⌨️ (2:20:10) Logging ⌨️ (2:42:20) JSON ⌨️ (2:59:42) Random Numbers ⌨️ (3:14:23) Decorators ⌨️ (3:35:32) Generators ⌨️ (3:53:29) Threading vs Multiprocessing ⌨️ (4:07:59) Multithreading ⌨️ (4:31:05) Multiprocessing ⌨️ (4:53:26) Function Arguments ⌨️ (5:17:28) The Asterisk (*) Operator ⌨️ (5:30:19) Shallow vs Deep Copying ⌨️ (5:40:07) Context Managers -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news #python course #python tutorial #python #intermediate python #python nprogramming
2020年08月28日
00:00:00 - 05:55:47
Java Full Course in 10 Hours | Java Tutorial for Beginners [2024] | Java Online Training | Edureka

Java Full Course in 10 Hours | Java Tutorial for Beginners [2024] | Java Online Training | Edureka

🔥Edureka Java Training (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎"): https://www.edureka.co/java-j2ee-training-course This Edureka Java Full Course will help you understand the various fundamentals of Java programming in detail with examples. This extensive Java Full course is designed to take you from a complete beginner to a confident Java developer. Whether you're a student, a career changer, or simply someone who wants to explore the exciting world of software development, this Java course is your gateway! 🔴 𝐋𝐞𝐚𝐫𝐧 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐅𝐨𝐫 𝐅𝐫𝐞𝐞! 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐂𝐡𝐚𝐧𝐧𝐞𝐥: https://edrk.in/DKQQ4Py Check out the Java Tutorial Playlist: https://goo.gl/ES3dI3 This Java tutorial for beginners covers the following topics: 00:00 Agenda of Java Full Course 3:36 - Introduction to Java 23:46 - Java Environmental SetUp 28:43 - Java Internals 35:05 - Java Working 38:21 - First Java Program 53:27 - Modifiers in Java 54:34 - Access Control Modifiers 1:00:12 - Non Access Modifiers 1:10:05 - Variables in Java 1:14:59 - Data types in Java 1:25:52 - Operators 1:28:56 - Operators Types and Examples 1:33:11 - Control Statements in Java 1:35:51 - Selection Statements 1:37:17 - Iteration Statements 1:44:04 - Jump Statements 1:46:06 - Methods in Java 2:09:00 - Arrays in Java 2:27:50 - Strings 2:52:45: Classes and Objects 2:57:17 - Java Naming Conventions 2:57:30 - Types of variables 3:01:30 - Constructor 3:19:09 - Java Static Keyword 3:24:32 - Java this keyword 3:29:27 - Object-Oriented Programming Concepts 4:29:44 - Interface 4:50:55 - What is a Package? 4:58:12 - Access Modifiers 5:05:00 - Demo - Access package from another package 5:08:55 - Regular Expression 5:21:50 - Exception 5:31:45 - Exception handling 5:50:27 - XML in Java 6:36:00 - Serialization in Java 6:54:00 - Wrapper Classes 6:58:06 - Generics in Java 🔴 Edureka Java Training 🔵 Java Certification Training: http://bit.ly/3a5wPG1 🔵 Selenium Certification Training: http://bit.ly/3r1XBpF 🔵 Microservices Certification Training: http://bit.ly/2MjLnJK 🔵 Spring Certification Training: http://bit.ly/3sTulTB 🔵 Test Automation Engineer Masters Program: http://bit.ly/369W6xE Subscribe to our channel to get video updates. 📢📢 𝐓𝐨𝐩 𝟏𝟎 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐢𝐧 𝟐𝟎𝟐𝟒 𝐒𝐞𝐫𝐢𝐞𝐬 📢📢 ⏩ NEW Top 10 Technologies To Learn In 2024 - https://www.youtube.com/watch?v=vaLXPv0ewHU #edureka #javaedureka #JavaFullCourse #Javatutorial #Javaonlinetraining #Javaforbeginners Instagram: https://www.instagram.com/edureka_learning/ Facebook: https://www.facebook.com/edurekaIN/ LinkedIn: https://www.linkedin.com/company/edureka Telegram: https://t.me/edurekaupdates For Java Training & Certification, please write back to us at [email protected] or call us at IND: 9606058406 / US: +18338555775 (toll free). #Edureka java #complete java course #edureka #java #java (programming language) #java 8 tutorial #java basics #java beginner tutorial #java certification #java certification training #java course #java data types #java edureka #java for beginners #java full course #java online training #java programming #java programming for beginners #java programming tutorial #java training #java tutorial #java tutorial for beginners #java video tutorial #learn java #what is java #yt:cc=on
2019年06月23日
00:00:00 - 10:10:58
Artificial Intelligence in 2 Minutes | What is Artificial Intelligence? | Edureka

Artificial Intelligence in 2 Minutes | What is Artificial Intelligence? | Edureka

🔥 Edureka AI Courses: https://www.edureka.co/artificial-intelligence-certification-courses This Edureka video gives you a brief overview of AI and how it has been exponentially impacting our lives. In this quick guide, the following topics will be covered: 1.  What Is Artificial Intelligence? 2. Popular Examples of AI 3. Future in AI ---------------------------------------------- Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV Edureka Community: https://bit.ly/EdurekaCommunity Instagram: https://www.instagram.com/edureka_learning Facebook: https://www.facebook.com/edurekaIN/ Twitter: https://twitter.com/edurekain LinkedIn: https://www.linkedin.com/company/edureka Telegram: https://t.me/edurekaupdates SlideShare: https://www.slideshare.net/EdurekaIN Become an expert in the exciting new world of AI & Machine Learning, get trained in cutting edge technologies and work on real-life industry grade projects. ---------------------------------------------- Why Machine Learning & AI? Because of the increasing need for intelligent and accurate decision making, there is an exponential growth in the adoption of AI and ML technologies. Hence these are poised to remain the most important technologies in the years to come. ----------------------------------------------- PG Program in Machine Learning & AI 1. Ranked 4th among NITs by NIRF 2. Ranked among Top 50 Institutes in India 3. Designated as Institute of National Importance ----------------------------------------------- Program Features 1. Mentorship from NITW faculty 2. Placement Assistance 3. Alumni Status 4. Industry Networking ----------------------------------------------- Industry Projects 1. Building a Conversational ChatBot 2. Predictive Model for Auto Insurance 3. E-commerce Website - Sales Prediction ----------------------------------------------- Mentors & Instructors Dr. RBV Subramaanyam Professor NITW Dr. DVLN Somayajulu Professor NITW Dr. P. Radha Krishna Professor NITW Dr. V. Ravindranath Professor JNTU Kakinada -------------------------------------------- Is this program for me? If you’re passionate about AI & ML and want to pursue a career in this field, this program is for you. Whether you’re a fresher or a professional, this program is designed to equip you with the skills you need to rise to the top in a career in AI & ML. Is there any eligibility criteria for this program? A potential candidate must have one of the following prerequisites: Degrees like BCA, MCA, and B.Tech or Programming experience Should have studied PCM in 10+2 Will I get any certificate at the end of the course? Yes, you will receive a Post-Graduate industry-recognized certificate from E & ICT Academy, NIT Warangal upon successful completion of the course. For more information, Please write back to us at [email protected] or call us at IND: +91-9606058418 / US: 18338555775 (toll-free). #yt:cc=on #artificial intelligence #artificial intelligence explained #what is artificial intelligence #artificial intelligence in 2 minutes #what is ai #artificial intelligence edureka #introduction to artificial intelligence #what is ai technology #artificial intelligence applications #future of ai #artificial intelligence for beginners #ai applications #what is artificial intelligence and machine learning #artificial intelligence 2020 #edureka #machine learning edureka #ai easy
2020年03月12日
00:00:00 - 00:02:20
Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

Complete Python Pandas Data Science Tutorial! (Reading CSV/Excel files, Sorting, Filtering, Groupby)

Practice your Python Pandas data science skills with problems on StrataScratch! https://stratascratch.com/?via=keith Data & code used in this Tutorial: https://github.com/KeithGalli/pandas Python Pandas Documentation: http://pandas.pydata.org/pandas-docs/stable/ Let me know if you have any questions! In this video we walk through many of the fundamental concepts to use the Python Pandas Data Science Library. We start off by installing pandas and loading in an example csv. We then look at different ways to read the data. Read a column, rows, specific cell, etc. Also ways to read data based on conditioning. We then move into some more advanced ways to sort & filter data. We look at making conditional changes to our data. We also start doing aggregate stats using the groupby function. We finished the video talking about how you would work with a very large dataset (many gigabytes) I realized as I upload this video there are some additional things I want to talk about in a later video. The first thing that comes to mind immediately is using the apply() function on a dataframe to alter the data using a custom or lambda function. If you have questions on this or anything else before I get around to making a part 2, feel free to write me a note in the comments. If you enjoyed this video, be sure to throw it a like and make sure to subscribe to not miss any future videos! Thanks for watching friends! Happy coding! :) Join the Python Army to get access to perks! YouTube - https://www.youtube.com/channel/UCq6XkhO5SZ66N04IcPbqNcw/join Patreon - https://www.patreon.com/keithgalli --------------------------------------------- Follow me on social media! Instagram | https://www.instagram.com/keithgalli/ Twitter | https://twitter.com/keithgalli --------------------------------------------- Link to original source of data from Kaggle: https://www.kaggle.com/abcsds/pokemon --------------------------------------------- Video Outline! 0:00 - Why Pandas? 1:46 - Installing Pandas 2:03 - Getting the data used in this video 3:50 - Loading the data into Pandas (CSVs, Excel, TXTs, etc.) 8:49 - Reading Data (Getting Rows, Columns, Cells, Headers, etc.) 13:10 - Iterate through each Row 14:11 - Getting rows based on a specific condition 15:47 - High Level description of your data (min, max, mean, std dev, etc.) 16:24 - Sorting Values (Alphabetically, Numerically) 18:19 - Making Changes to the DataFrame 18:56 - Adding a column 21:22 - Deleting a column 22:14 - Summing Multiple Columns to Create new Column. 24:14 - Rearranging columns 28:06 - Saving our Data (CSV, Excel, TXT, etc.) 31:47 - Filtering Data (based on multiple conditions) 35:40 - Reset Index 37:41 - Regex Filtering (filter based on textual patterns) 43:08 - Conditional Changes 47:57 - Aggregate Statistics using Groupby (Sum, Mean, Counting) 54:53 - Working with large amounts of data (setting chunksize) ------------------------- If you are curious to learn how I make my tutorials, check out this video: https://youtu.be/LEO4igyXbLs *I use affiliate links on the products that I recommend. I may earn a purchase commission or a referral bonus from the usage of these links. #KGMIT #Keith Galli #MIT #Python 3 #Python Programming #Data Science #Python Pandas #Pandas #Pandas Library #pd #python data science tutorial #Excel #Csv #Reading CSV in Python #Data Science in Python #Python Pandas tutorial #Data analysis in python #how to do data analysis using python #Numpy #Reading excel files with python #sorting data #pandas library tutorial #simple #easy #best #groupby in python pandas #reset index pandas #index python pandas #stats in python #python statistics
2018年10月26日
00:00:00 - 01:00:27
Web Development with HTML & CSS – Full Course for Beginners

Web Development with HTML & CSS – Full Course for Beginners

This course is an in-depth introduction to web development with HTML and CSS for complete beginners. We'll start with HTML and CSS basics, dive into some advanced concepts, and learn about Git, GitHub, and cloud deployment using Vercel. We'll also explore mobile-first responsive design, the Bootstrap CSS framework, and the Express web application framework. You'll solve coding assignments on building a web page from a mockup, creating a responsive mobile-first website, and crafting a scientific calculator. For the final project, you will build your personal portfolio website and deploy it to the cloud! ✏️ This course is designed and taught by Aakash N S, CEO and co-founder of Jovian. Check out their YouTube channel here: https://youtube.com/@jovianhq ⭐️ Contents & Code ⭐️ ⌨️ (00:00:00) Introduction ⌨️ (00:00:34) Lesson 1 - HTML and CSS Basics https://jovian.com/sydney/html-and-css-basics ⌨️ (02:14:15) Lesson 2 - Advanced HTML and CSS https://jovian.com/aakashns/advanced-html-and-css ⌨️ (04:42:24) Assignment 1 - Design Mockup to Web Page https://jovian.com/aakashns/design-mockup-to-web-page ⌨️ (06:47:59) Lesson 3 - Version Control and Cloud Deployment https://jovian.com/sydney/version-control-and-cloud-deployment ⌨️ (08:59:42) Lesson 4 - Responsive Design and CSS Flexbox https://jovian.com/aakashns/responsive-design-and-css-frameworks ⌨️ (11:32:24) Assignment 2 - Mobile-First Responsive Web Design https://jovian.com/aakashns/mobile-first-responsive-design ⌨️ (12:31:43) Lesson 5 - Bootstrap CSS Framework https://jovian.com/aakashns/bootstrap-css-framework ⌨️ (14:56:51) Assignment 3 - Build a Scientific Calculator https://jovian.com/aakashns/build-a-scientific-calculator ⌨️ (16:34:34) Lesson 6 - Express Web Application Framework https://jovian.com/sydney/express-web-application-framework ⌨️ (18:30:21) Project - Build Your Personal Website https://jovian.com/aakashns/build-your-personal-website ⌨️ (19:00:35) Course Recap 🎉 Thanks to our Champion and Sponsor supporters: 👾 davthecoder 👾 jedi-or-sith 👾 南宮千影 👾 Agustín Kussrow 👾 Nattira Maneerat 👾 Heather Wcislo 👾 Serhiy Kalinets 👾 Justin Hual 👾 Otis Morgan 👾 Oscar Rahnama -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news
2023年11月02日
00:00:00 - 19:42:13
Solving 100 Python Pandas Problems! (from easy to very difficult)

Solving 100 Python Pandas Problems! (from easy to very difficult)

In this tutorial, you'll gain hands-on experience with the python pandas library, building experience with data manipulation and analysis skills important for data science. You'll learn how to create, modify, and analyze DataFrames, handle missing data (NaNs), clean messy data, and generate some visualizations. By tackling a variety of problems, from basic data handling to advanced DataFrame techniques, you'll build a solid foundation in managing and interpreting real-world data sets using pandas. Repo we're working off of (credit to Alex Riley who put repo together): https://github.com/ajcr/100-pandas-puzzles My code solutions (use repo above for blank starting template): https://github.com/KeithGalli/100-pandas-puzzles Hope that you enjoy this video. If you do, make sure to like it and subscribe to not miss future videos like this! Video Timeline! 0:00 - Intro & Setup 2:14 - Problems (1-3) Initial pandas setup 4:42 - Problems (4-10) DataFrame operations 4:52 - 4) Create a dataframe from dictionary 5:24 - 5) Display dataframe summary 5:41 - 6) First 3 rows of the dataframe 6:02 - 7) Select ‘animal’ and ‘age’ columns 7:42 - 8) Data in specific rows and columns 9:06 - 9) Rows with visits greater than 3 9:57 - 10) Rows with NaN in age 10:56 - 11) Cats younger than 3 years 11:35 - 12) Age between 2 and 4 12:45 - 13) Change age in row ‘f’ 15:56 - 14) Sum of all visits 16:41 - 15) Average age by animal 20:21 - 16) Modify and revert rows 24:06 - 17) Count by animal type 25:28 - Quick review 26:17 - 18) Sort by age and visits 28:07 - 19) Convert 'priority' to boolean 29:42 - 20) Replace 'snake' with 'python' 30:53 - 21) Mean age by animal and visits 33:49 - Advanced DataFrame techniques 33:57 - 22) Filter duplicate integers 43:18 - 23) Subtract row mean 45:42 - 24) Column with smallest sum 50:39 - 25) Count unique rows 53:17 - 26) Column with third NaN 1:10:27 - Solution review for 26 1:17:13 - 27) Sum of top three values 1:24:01 - 28) Sum by column condition 1:40:11 - Recent problem review 1:42:53 - 29) Count differences since last zero 1:56:19 - 30) Locate largest values 2:08:38 - 31) Replace negatives with mean 2:17:43 - 32) Rolling mean over groups 2:23:10 - Series and DatetimeIndex 2:23:12 - 33) DatetimeIndex for 2015 2:27:56 - 34) Sum values on Wednesdays 2:45:04 - 35) Monthly mean values 2:46:16 - 36) Best value in four-month groups 2:50:26 - 37) DatetimeIndex of third Thursdays 2:59:03 - Cleaning Data 2:59:40 - 38) Fill missing FlightNumber 3:02:45 - 39) Split column by delimiter 3:06:47 - 40) Fix city name capitalization 3:08:30 - 41) Reattach columns 3:13:11 - 42) Fix airline name punctuation 3:17:45 - 43) Expand RecentDelays into columns 3:27:31 - MultiIndexes in Pandas 3:27:34 - 44) Construct a MultiIndex 3:30:37 - Solution review 3:32:44 - 45) Lexicographically sorted check 3:32:58 - 46) Select specific MultiIndex labels 3:34:23 - 47) Slice Series with MultiIndex 3:35:24 - 48) Sum by first level 3:37:47 - 49) Alternative sum method 3:40:08 - Additional solution insights 3:41:22 - 50) Swap MultiIndex levels 3:45:27 - Minesweeper problems 3:45:44 - 51) Generate coordinate grid 4:00:28 - 52) Add 'safe' or 'mine' column 4:03:04 - 53) Count adjacent mines 4:27:33 - Review solution to 53 4:33:02 - Skipped problems 54 & 55 4:33:11 - Plotting 4:33:12 - 56) Scatter plot with black x markers 4:41:26 - 57) Plot four data types 4:52:50 - 58) Overlay multiple graphs 5:03:11 - 59) Hourly stock data summary 5:14:12 - 60) Candlestick plot ------------------ Practice your Python Pandas data science skills with problems on StrataScratch! https://stratascratch.com/?via=keith #Keith Galli #python #programming #python 3 #data science #data analysis #python programming #pandas #loc #iloc #python pandas #pd #numpy #np #learn data science #python3 #pandas practice problems #polars #python polars #learn programming #sql #data #data engineering #dataframe #data cleaning #datetimeindex #datetime #multiindex #series #machine learning #artificial intelligence #data scientist #leetcode problems #data structures #pandas library #real world #python interview #nan #clean data
2024年04月14日
00:00:00 - 05:20:18
CRUD API Tutorial – Node, Express, MongoDB

CRUD API Tutorial – Node, Express, MongoDB

This is a great beginners course to learn the basics of backend development by building a CRUD API with Node.js, Express, and MongoDB. Basically the MERN Stack without the R. Also, learn how to use Postman/ThunderClient and Insomnia for testing purposes. Code: https://github.com/haris-bit/simple-crud-app-backend ✏️ Course developed by @CodingCleverly ⭐️ Chapters ⭐️ ⌨️ (0:00:00) Introduction ⌨️ (0:01:34) package.json ⌨️ (0:04:32) express framework ⌨️ (0:09:19) npm run server ⌨️ (0:11:19) API testing tools ⌨️ (0:15:04) git bash ⌨️ (0:17:19) nodemon ⌨️ (0:20:40) mongodb setup ⌨️ (0:31:24) product model ⌨️ (0:41:19) create api ⌨️ (0:48:19) read api all ⌨️ (0:50:19) read api id ⌨️ (0:56:19) update api ⌨️ (1:00:19) delete api ⌨️ (1:07:49) Form URL Encoded ⌨️ (1:12:19) routes ⌨️ (1:15:19) controllers ⌨️ (1:26:19) checking and testing ⌨️ (1:30:19) pushing to github 🎉 Thanks to our Champion and Sponsor supporters: 👾 davthecoder 👾 jedi-or-sith 👾 南宮千影 👾 Agustín Kussrow 👾 Nattira Maneerat 👾 Heather Wcislo 👾 Serhiy Kalinets 👾 Justin Hual 👾 Otis Morgan 👾 Oscar Rahnama -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news
2024年02月14日
00:00:00 - 01:33:14
MERN Stack Tutorial - Book Store Project

MERN Stack Tutorial - Book Store Project

Learn the MERN stack (MongoDB, Express, React, Node.js) in this crash course for beginners. Here are some of the topics you will learn about: - Backend CRUD - Backend Router - CORS Policy - MongoDB operations - Frontend CRUD - Frontend Router 💻 Code: https://github.com/mohammad-taheri1/Book-Store-MERN-Stack ✏️ Course developed by @DevEmpower ⭐️ Contents ⭐️ ⌨️ (00:00) Intro ⌨️ (01:10) Create Node.js project from scratch ⌨️ (03:39) Create our first Http Route ⌨️ (06:01) Add MongoDB and mongoose to node js ⌨️ (08:52) Create Book model with mongoose ⌨️ (10:53) Save a new Book with mongoose ⌨️ (13:31) Get All Books with mongoose ⌨️ (15:08) Get One Book by id with mongoose ⌨️ (16:29) Update a Book with mongoose ⌨️ (18:36) Delete a book with mongoose ⌨️ (20:09) Refactor Node js with express router ⌨️ (22:23) CORS policy in Node js and Express js ⌨️ (25:21) Create React project, Vite, Tailwind CSS ⌨️ (27:41) SPA and Add react router dom ⌨️ (29:52) Show Books List in React ⌨️ (35:39) Show Book Details in React ⌨️ (39:02) Create Book in React ⌨️ (42:02) Edit Book in React ⌨️ (44:27) Delete Book in React ⌨️ (46:35) Show Books List as Card ⌨️ (52:30) Make Book Card a single component ⌨️ (54:08) Add Book Modal ⌨️ (58:06) Improve User Experience (UX) with beautiful alert ⌨️ (1:01:43) Outro 🎉 Thanks to our Champion and Sponsor supporters: 👾 davthecoder 👾 jedi-or-sith 👾 南宮千影 👾 Agustín Kussrow 👾 Nattira Maneerat 👾 Heather Wcislo 👾 Serhiy Kalinets 👾 Justin Hual 👾 Otis Morgan 👾 Oscar Rahnama -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news
2023年09月01日
00:00:00 - 01:02:00
HTML Full Course - Build a Website Tutorial

HTML Full Course - Build a Website Tutorial

Learn the basics of HTML5 and web development in this awesome course for beginners. Want more from Mike? He's starting a coding RPG/Bootcamp - https://simulator.dev/ ⭐️ Contents ⭐️ ⌨️ (0:00:00) Introduction ⌨️ (0:01:54) Choosing a Text Editor ⌨️ (0:08:13) Creating an HTML file ⌨️ (0:20:31) Basic Tags ⌨️ (0:36:47) Comments ⌨️ (0:42:13) Style & Color ⌨️ (0:48:07) Formatting a Page ⌨️ (0:59:16) Links ⌨️ (1:07:33) Images ⌨️ (1:16:12) Videos & Youtube iFrames ⌨️ (1:23:00) Lists ⌨️ (1:28:53) Tables ⌨️ (1:37:21) Divs & Spans ⌨️ (1:44:54) Input & Forms ⌨️ (1:53:44) iFrames ⌨️ (1:57:21) Meta Tags Course developed by Mike Dane. Check out his YouTube channel for more great programming courses: https://www.youtube.com/channel/UCvmINlrza7JHB1zkIOuXEbw 🐦Follow Mike on Twitter - https://twitter.com/mike_dane 🔗The Mike's website: https://www.mikedane.com/ ⭐️Other full courses by Mike Dane on our channel ⭐️ 💻Python: https://youtu.be/rfscVS0vtbw 💻C: https://youtu.be/KJgsSFOSQv0 💻C++: https://youtu.be/vLnPwxZdW4Y 💻SQL: https://youtu.be/HXV3zeQKqGY 💻Ruby: https://youtu.be/t_ispmWmdjY 💻PHP: https://youtu.be/OK_JCtrrv-c 💻C#: https://youtu.be/GhQdlIFylQ8 -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://medium.freecodecamp.org #html #html for beginners #html tutorial #html tutorial for beginners #learn html #html crash course #html5 #html css #html course #html5 tutorial #html5 course
2018年09月19日
00:00:00 - 02:02:32
I've Read Over 100 Books on Python. Here are the Top 3

I've Read Over 100 Books on Python. Here are the Top 3

Visit https://brilliant.org/PythonProgrammer/ to get started for free (and if you're one of the first 200 people to click the link you'll get an extra 20% off too) 😃 Over the last few years I have read more than 100 boos on python, There are some books that stand out as the best. I have a python course on Udemy, it's one of the highest rated. Here it is: 🎓 Complete Python Programmer Bootcamp - http://bit.ly/2OwUA09 Buy the books UK Book 1 - https://amzn.to/3SdPOEg Book 2 - https://amzn.to/3OpDv6A US Book 1 - https://amzn.to/3uq1oEe Book 2 - https://amzn.to/49fW5pT Join this channel to get access to perks: https://www.youtube.com/channel/UC68KSmHePPePCjW4v57VPQg/join ⌚️Timestamps: 00:00 - Introduction 00:25 - Shop 00:38 - Bag 00:54 - What does it have to do with Python? 01:00 - Learn English Analogy 02:00 - Books to Avoid 02:28 - Book 1 03:44 - Book 2 05:29 - Book 3 05:45 - 1st Book 3 06:12 - 2nd Book 3 06: 27 - Books for Data Analysis 07:27 - Best Book for Pandas 08:00 - Don't forget libraries 08:11 - Thanks Brilliant Learn Data Science (affiliate link) 🎓 Data Quest - https://bit.ly/3yClqbZ Learn Python with Giles 🎓 Exploratory Data Analysis with Python and Pandas - https://bit.ly/2QXMpxJ 🎓 Complete Python Programmer Bootcamp - http://bit.ly/2OwUA09 📚 My favourite python books for beginners (affiliate links) 📗 Python Crash Course 2nd Edition https://amzn.to/33tATAE 📘 Automate the Boring Stuff with Python https://amzn.to/3qM1DFl 📙 Python Basics - A Practical Introduction to Python 3 https://amzn.to/3fHRMdb 📕 Python Programming An Introduction to Computer Science https://amzn.to/33VeQCr 📗 Invent Your Own Computer Games with Python https://amzn.to/3FM3H4b 🆓 Free Python Resource https://python-programming.quantecon.org/intro.html (This is a great introduction to python) ⚙ My Gear 💡 BenQ Screen Bar Desk Light - https://amzn.to/3tH6ysL 🎧 Sony Noise Cancelling Headphones - https://amzn.to/3tLl82G 📱 Social Media https://www.instagram.com/gilesmcmullen/ https://twitter.com/GilesMcMullen 👌 SUBSCRIBE to ME!👌 https://www.youtube.com/channel/UC68KSmHePPePCjW4v57VPQg?sub_confirmation=1
2024年02月01日
00:00:00 - 00:09:26
A Science based System for Learning ANYTHING quickly

A Science based System for Learning ANYTHING quickly

Visit https://brilliant.org/PythonProgrammer/ to get started for free and get 20% off your annual subscription. Thanks to Brilliant for sponsoring this video :-) Want to Read Faster? Watch this - https://youtu.be/5RfMMBTLDms Free monthly learning resources and insights https://gilesknowledge.substack.com/ 🍿 WATCH NEXT: 3 Steps to Achieve Any Goal (the science of habit) - https://youtu.be/5R6AAVPEz-4 Here are the links: 1. Teaching the Science of Learning - https://gilesm.me/scienceoflearning 2. Learning Scientists - https://www.learningscientists.org/ 3. Coursera How to Learn Course - https://gilesm.me/learninghowtolearn (affiliate) 4. Make it Stick - https://amzn.to/3PWyLqc (affiliate link) 0:00 Introduction 0:31 Don't do this 1:30 Method 1 3:53 Method 2 5:52 Method 3 8:14 Method 4 9:07 Learning materials 9:35 Thanks Brilliant! Learn Data Science 🎓 Data Quest - https://bit.ly/3yClqbZ Learn Python with Giles 🎓 Exploratory Data Analysis with Python and Pandas - https://bit.ly/2QXMpxJ 🎓 Complete Python Programmer Bootcamp - http://bit.ly/2OwUA09 📚 My favourite python books for beginners (affiliate links) 📗 Python Crash Course 2nd Edition https://amzn.to/33tATAE 📘 Automate the Boring Stuff with Python https://amzn.to/3qM1DFl 📙 Python Basics - A Practical Introduction to Python 3 https://amzn.to/3fHRMdb 📕 Python Programming An Introduction to Computer Science https://amzn.to/33VeQCr 📗 Invent Your Own Computer Games with Python https://amzn.to/3FM3H4b 🆓 Free Python Resource https://python-programming.quantecon.org/intro.html (This is a great introduction to python) ⚙ My Gear 💡 BenQ Screen Bar Desk Light - https://amzn.to/3tH6ysL 🎧 Sony Noise Cancelling Headphones - https://amzn.to/3tLl82G 📱 Social Media https://www.instagram.com/gilesmcmullen/ https://twitter.com/GilesMcMullen 👌 SUBSCRIBE to ME!👌 https://www.youtube.com/channel/UC68KSmHePPePCjW4v57VPQg?sub_confirmation=1 I am an Amazon, Coursera and DataQuest affiliate program member, this means I earn a commission from qualifying purchases on the some of the above links. It costs you nothing but helps me with content creation. #evidence based learning techniques
2023年10月31日
00:00:00 - 00:10:40
Visual Basic (VB.NET) – Full Course for Beginners

Visual Basic (VB.NET) – Full Course for Beginners

Lean the fundamentals of programming with Visual Basic (sometimes called Visual Basic .NET or VB.NET). In this tutorial, you will learn about the basic constructs of high level programming languages, including sequence, selection and iteration. You will learn how to build an event-driven, form-based, user interface to capture input, and you will learn how to write code to validate and process the data collected. 🔗 Get Visual Studio for free: https://visualstudio.microsoft.com/ ✏️ Kevin Drumm developed this course. Check out his YouTube channel: https://www.youtube.com/c/ComputerScienceLessons ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Introduction ⌨️ (0:00:46) Hello Visual Studio ⌨️ (0:13:44) Customise The Visual Studio IDE ⌨️ (0:19:13) Output and Variables ⌨️ (0:34:26) Variable Data Types ⌨️ (0:41:56) Input with Windows Forms ⌨️ (0:54:18) Debugging Code ⌨️ (1:02:47) Arithmetic Operators ⌨️ (1:14:24) Complex Arithmetic Expressions ⌨️ (1:22:50) Selection with If Statements ⌨️ (1:32:58) Logical and Relational Operators 1 ⌨️ (1:47:32) Logical and Relational Operators 2 ⌨️ (1:56:37) Select Case ⌨️ (2:02:57) For Next ⌨️ (2:08:17) Practice For Next Loops & If Blocks ⌨️ (2:12:39) Do While ⌨️ (2:21:24) Condition Controlled Loops ⌨️ (2:29:39) Array Variables ⌨️ (2:39:48) Practice Arrays & Loops ⌨️ (2:49:46) Linear Search ⌨️ (2:56:24) Two Dimensional Arrays ⌨️ (3:07:00) 2D Arrays & Nested Loops 🎉 Thanks to our Champion and Sponsor supporters: 👾 Raymond Odero 👾 Agustín Kussrow 👾 aldo ferretti 👾 Otis Morgan 👾 DeezMaster -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news
2022年06月13日
00:00:00 - 03:17:20
Non-Technical Intro to Generative AI

Non-Technical Intro to Generative AI

Learn about Generative AI from a non-technical perspective. This course examines the evolution of AI capabilities, analyzing the key technological breakthroughs that have enabled modern generative AI models to achieve remarkable performance. The course also covers some of the challenges of Generative AI. Further focusing on concept of decentralized AI, followed by LLM APIs. ✏️ Course developed by @1littlecoder ⭐️ Contents ⭐️ ⌨️ (0:00:00) Generative AI Quick Intro ⌨️ (0:00:47) AI back then vs AI Now ⌨️ (0:17:46) Why Gen AI is possible now? ⌨️ (0:22:46) The less spoken about Gen AI ⌨️ (0:38:33) What is Decentralized AI ⌨️ (0:54:50) LLM APIs ⌨️ (1:01:48) LLM App Framework ⌨️ (1:02:33) Text Completion ⌨️ (1:04:50) ChatBot ⌨️ (1:09:07) RAG - LLM with Knowledge ⌨️ (1:19:36) LLM for Downstream NLP Tasks ⌨️ (1:22:50) Agents based on LLMs ⌨️ (1:32:05) LLM OS 🎉 Thanks to our Champion and Sponsor supporters: 👾 davthecoder 👾 jedi-or-sith 👾 南宮千影 👾 Agustín Kussrow 👾 Nattira Maneerat 👾 Heather Wcislo 👾 Serhiy Kalinets 👾 Justin Hual 👾 Otis Morgan 👾 Oscar Rahnama -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news
2024年06月18日
00:00:00 - 01:35:34
Arduino Course for Beginners - Open-Source Electronics Platform

Arduino Course for Beginners - Open-Source Electronics Platform

Learn how to use Arduino hardware and software in this full course for beginners. Arduino is an easy-to-use, open-source electronics platform. Arduino boards are able to read inputs - light on a sensor, a finger on a button, or a Twitter message - and turn it into an output - activating a motor, turning on an LED, publishing something online. You can tell your board what to do by sending a set of instructions to the microcontroller on the board. No hardware is required for to follow along with this course! ✏️ Course developed by Ashish Bansal. 📸 Ashish on Instagram: https://www.instagram.com/ashish_things/ Tinker with the circuits used in the course : 🔗 custom blink function: https://www.tinkercad.com/things/ib4c08HDTAe 🔗 digitalRead & digitalWrite : https://www.tinkercad.com/things/bvTdKaqDvQc 🔗 analogRead : https://www.tinkercad.com/things/6kEEQR3GZC1 🔗 analogWrite : https://www.tinkercad.com/things/hDwWdu92kws ⭐️Course Contents ⭐️ Section 1: Objective of the course (0:00) Course Introduction (01:21) Section 2: Foundation of Electronics (01:36) Electricity (02:10) Static Electricity (03:37) Current Electricity (04:12) Voltage (06:09) Current (08:45) Resistance (10:05) Ohm’s Law (11:55) Ohm’s Law Example (13:46) Resistances in Series and Parallel (26:03) Resistance Color Coding (28:26) Section 3: Intro to Arduino Board (28:46) What is Microcontroller and Microprocessor (31:16) What category Arduino falls into? (31:33) Different Types of Arduino Boards (32:03) About Arduino (33:04) Parts of Arduino Uno (35:52) Technical Specifications of Arduino Uno Section 4: Intro to Arduino IDE (38:58) What is IDE? (40:14) Downloading and Installing the official IDE (41:51) Preparing your computer (43:08) Testing the Arduino. (44:22) What if you don’t have an Arduino board? (46:34) Section 5: Before we move ahead (47:04) What is breadboard? (49:16) How to make connections in breadboard? (1:00:10) Some safety instructions and Do’s and Don’ts (1:01:53) Input & Output (1:08:47) Analog & Digital (1:14:04) Bit & Byte (1:16:26) Section 6: Arduino Programming (1:16:46) Introduction (1:17:41) The First Step into Programming (1:19:37) Bare minimum structure of an Arduino Program (1:20:21) Comments (1:21:37) White Spaces and Case Sensitivity (1:24:06) pinMode (1:26:44) digitalWrite and delay (1:29:51) Camel casing Section 6.1 Introduction to Variables and Data Types (1:30:51) What are variables and data types (1:31:31) Int data type (1:35:11) Arithmetic operators (1:41:51) Incrementing and Decrementing our variables (1:44:14) Float data type (1:46:48) Bool/Boolean data type (1:49:24) Byte data type (1:50:27) Char data type (1:52:46) Conclusion Section 6.2 Variable Scope and Qualifiers (1:53:19) What is Scope? Global and Local Variables (1:57:59) What are Qualifiers, starting with const qualifier (1:59:51) Alternative to const qualifier: #define (2:01:55) Static Qualifier Section 6.2 Comparison and Logical Operators (2:04:25) What are comparison operators? (2:08:58) What are Logical Operators? (2:13:16) Section 6.3 Control Structures (2:14:21) if statement (2:20:47) else statement (2:24:24) A joke :P (2:25:10) if - else Simulation (2:29:27) Introduction to loop control structures (2:30:52) For loop (2:41:02) While loop (2:45:49) do…while loop (2:50:16) break (2:52:24) continue (2:55:05) return (2:56:41) switch..case Section 6.4 Remaining data types (3:01:30) Arrays (3:09:34) Strings Section 6.5 Functions (3:15:14) What are functions? (3:19:03) Create your own functions Section 6.6 Arduino Built-in Functions and related concepts (3:35:20) digitalRead & digitalWrite (3:41:49) analogRead and Analog to Digital Converter (ADC) (3:47:50) analogWrite and Pulse Width Modulation (PWM) Section 6.7 Libraries (3:56:25) What are Libraries? (3:59:22) How to add Libraries in Arduino IDE (4:02:30) What next? 🎉 Thanks to our Champion and Sponsor supporters: 👾 Wong Voon jinq 👾 hexploitation 👾 Katia Moran 👾 BlckPhantom 👾 Nick Raker 👾 Otis Morgan 👾 DeezMaster 👾 Treehouse -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news
2021年06月08日
00:00:00 - 04:04:22
What is DevOps? | DevOps in 2 Minutes | DevOps Tutorial for Beginners | Edureka

What is DevOps? | DevOps in 2 Minutes | DevOps Tutorial for Beginners | Edureka

🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐃𝐞𝐯𝐎𝐩𝐬 𝐏𝐨𝐬𝐭 𝐆𝐫𝐚𝐝𝐮𝐚𝐭𝐞 𝐏𝐫𝐨𝐠𝐫𝐚𝐦 𝐰𝐢𝐭𝐡 𝐏𝐮𝐫𝐝𝐮𝐞 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲: https://www.edureka.co/executive-programs/purdue-devops This Edureka "What is DevOps" video talks about DevOps basics and the goals of DevOps. This short DevOps tutorial will give you the best DevOps explanation with a facebook use case example to get started with it. 🔹Edureka DevOps Tutorial Playlist: https://bit.ly/3iJoJIP 🔹Edureka DevOps Tutorial Blog Series: https://goo.gl/05m82t 💥Subscribe to our channel to get video updates. Hit this link to subscribe us: https://goo.gl/6ohpTV ----------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐃𝐞𝐯𝐎𝐩𝐬 𝐓𝐫𝐚𝐢𝐧𝐢𝐧𝐠𝐬----------- 🔵DevOps Online Training: https://bit.ly/3GOAlD5 🔵Kubernetes Online Training: https://bit.ly/3q0zrg1 🔵Docker Online Training: https://bit.ly/3DYPCj9 🔵AWS Certified DevOps Engineer Online Training: https://bit.ly/3pXnB6y 🔵Azure DevOps (Az-400) Online Training: https://bit.ly/3m8WmVr ----------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐃𝐞𝐯𝐨𝐩𝐬 𝐌𝐚𝐬𝐭𝐞𝐫𝐬 𝐏𝐫𝐨𝐠𝐫𝐚𝐦---------- 🔵DevOps Engineer Masters Program: https://bit.ly/3pXp1Ou -----------𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐏𝐫𝐨𝐠𝐫𝐚𝐦---------- 🌕 Post Graduate Program in DevOps with Purdue University: https://bit.ly/3yqRlMS 📌𝐓𝐞𝐥𝐞𝐠𝐫𝐚𝐦: https://t.me/edurekaupdates 📌𝐓𝐰𝐢𝐭𝐭𝐞𝐫: https://twitter.com/edurekain 📌𝐋𝐢𝐧𝐤𝐞𝐝𝐈𝐧: https://www.linkedin.com/company/edureka 📌𝐈𝐧𝐬𝐭𝐚𝐠𝐫𝐚𝐦: https://www.instagram.com/edureka_learning/ 📌𝐅𝐚𝐜𝐞𝐛𝐨𝐨𝐤: https://www.facebook.com/edurekaIN/ 📌𝐒𝐥𝐢𝐝𝐞𝐒𝐡𝐚𝐫𝐞: https://www.slideshare.net/EdurekaIN 📌𝐂𝐚𝐬𝐭𝐛𝐨𝐱: https://castbox.fm/networks/505?country=in 📌𝐌𝐞𝐞𝐭𝐮𝐩: https://www.meetup.com/edureka/ 📌𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐭𝐲: https://www.edureka.co/community/ #edureka #DevOpsEdureka #DevOps #whatisdevops #DevOpsCertificationTraining #devopstutorialsforbeginners ------------------- About These Courses Edureka’s DevOps online training is designed to help you master key tools of DevOps lifecycle like Docker, Puppet, Jenkins, Nagios, GIT, Ansible, SaltStack, and Chef used by a DevOps Engineer for automating multiple steps in SDLC. During this course, our expert DevOps instructors will help you: 1. Understand the concepts and necessities of DevOps 2. Understand the need for DevOps and the day-to-day real-life problems it resolves 3. Learn installation and configuration of common infrastructure servers like Apache, and Nginx for the Enterprise 4. Learn popular DevOps tools like Jenkins, Puppet, Chef, Ansible, SaltStack, Nagios, and GIT 5. Implement automated system update, installations, and deployments 6. Learn Virtualization Concepts 7. Configuration deployment and packaging, continuous integration using GIT 8. Fine-tune Performance and set-up basic Security for Infrastructure 9. Manage server operations using Code which is popularly known as Infrastructure as a Code 10. Understand the need for and concepts of Monitoring and Logging. Along with the above-mentioned topics, to help you master the most popular DevOps tools, you will also receive 3 additional self-paced courses including presentations, class recordings, assignments, solutions for the following tools: 1: Ansible - Covers Introduction, Setup & Configuration, Ansible Playbooks, 37 Ansible Modules, Different Roles and Command Line usage. 2: Chef - Covers Introduction, Building the Cook Book, Node Object & Search, Data-bags, Chef environment, Roles, Deploying Nodes in Production and using the Open Source Chef Server. 3: Puppet - Covers Puppet Infrastructure & run-cycle, the Puppet Language, Environment defining Nodes and Modules, Provisioning a Web Server and Executing Modules Against A Puppet Master. ------------------- Who should go for this course? DevOps practitioners are among the highest paid IT professionals today, and the market demand for them is growing rapidly. With the emergence of new job roles around DevOps philosophy, anyone aspiring to get into these new roles can take up this DevOps course. Some of these roles are: 1. DevOps Architect 2. Automation Engineer 3. Software Tester 4. Security Engineer 5. Integration Specialist 6. Release Manager ------------------- Got a question on the topic? Please share it in the comment section below and our experts will answer it for you. For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). #yt:cc=on #devops #What is DevOps #DevOps in 2 minutes #devops tutorial for beginners #devops tutorial #dev ops #devops basics #devops explained #DevOps definition #devOps training #DevOps Overview #Introduction to DevOps #devops introduction #devops fundamentals #what is devops and how it works #devops introduction video #why devops #what is dev ops #devops video tutorial #devops edureka #devops for beginners #edureka #Edureka devops #devops training videos
2020年04月21日
00:00:00 - 00:01:56
Stanford CS224N: NLP with Deep Learning | Winter 2021 | Lecture 1 - Intro & Word Vectors

Stanford CS224N: NLP with Deep Learning | Winter 2021 | Lecture 1 - Intro & Word Vectors

For more information about Stanford's Artificial Intelligence professional and graduate programs visit: https://stanford.io/3w46jar This lecture covers: 1. The course (10min) 2. Human language and word meaning (15 min) 3. Word2vec algorithm introduction (15 min) 4. Word2vec objective function gradients (25 min) 5. Optimization basics (5min) 6. Looking at word vectors (10 min or less) Key learning: The (really surprising!) result that word meaning can be representing rather well by a large vector of real numbers. This course will teach: 1. The foundations of the effective modern methods for deep learning applied to NLP. Basics first, then key methods used in NLP: recurrent networks, attention, transformers, etc. 2. A big picture understanding of human languages and the difficulties in understanding and producing them 3. An understanding of an ability to build systems (in Pytorch) for some of the major problems in NLP. Word meaning, dependency parsing, machine translation, question answering. To learn more about this course visit: https://online.stanford.edu/courses/cs224n-natural-language-processing-deep-learning To follow along with the course schedule and syllabus visit: http://web.stanford.edu/class/cs224n/ Professor Christopher Manning Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science Director, Stanford Artificial Intelligence Laboratory (SAIL) 0:00 Introduction 1:43 Goals 3:10 Human Language 10:07 Google Translate 10:43 GPT 14:13 Meaning 16:19 Wordnet 19:11 Word Relationships 20:27 Distributional Semantics 23:33 Word Embeddings 27:31 Word tovec 37:55 How to minimize loss 39:55 Interactive whiteboard 41:10 Gradient 48:50 Chain Rule #Natural language #Natural Language Processing #Deep Learning #Stanford AI Lectures #Stanford Graduate courses #Computer science #language understanding #Stanford #Stanford Online
2021年10月29日
00:00:00 - 01:24:27
Linear Algebra - Full College Course

Linear Algebra - Full College Course

Learn Linear Algebra in this 20-hour college course. Watch the second half here: https://youtu.be/DJ6YwBN7Ya8 This course is taught by Dr. Jim Hefferon, a professor of mathematics at St Michael's College. 📔 The course follows along with Dr. Hefferon's Linear Algebra text book. The book is available for free: http://joshua.smcvt.edu/linearalgebra/book.pdf 📚 Access additional course resources at: https://hefferon.net/linearalgebra/ 🔗 Stephen Chew's Learning How to Learn series: https://www.youtube.com/watch?v=htv6eap1-_M&list=PL85708E6EA236E3DB 🔗 3Blue1Brown's Linear Algebra series: https://www.youtube.com/watch?v=fNk_zzaMoSs&list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Introduction to Linear Algebra by Hefferon ⌨️ (0:04:35) One.I.1 Solving Linear Systems, Part One ⌨️ (0:26:08) One.I.1 Solving Linear Systems, Part Two ⌨️ (0:40:56) One.I.2 Describing Solution Sets, Part One ⌨️ (0:54:21) One.I.2 Describing Solution Sets, Part Two ⌨️ (1:02:48) One.I.3 General = Particular + Homogeneous ⌨️ (1:18:33) One.II.1 Vectors in Space ⌨️ (1:35:08) One.II.2 Vector Length and Angle Measure ⌨️ (1:51:31) One.III.1 Gauss-Jordan Elimination ⌨️ (2:00:00) One.III.2 The Linear Combination Lemma ⌨️ (2:44:32) Two.I.1 Vector Spaces, Part One ⌨️ (3:08:12) Two.I.1 Vector Spaces, Part Two ⌨️ (3:33:01) Two.I.2 Subspaces, Part One ⌨️ (3:58:16) Two.I.2 Subspaces, Part Two ⌨️ (4:23:43) Two.II.1 Linear Independence, Part One ⌨️ (4:45:11) Two.II.1 Linear Independence, Part Two ⌨️ (5:03:57) Two.III.1 Basis, Part One ⌨️ (5:23:55) Two.III.1 Basis, Part Two ⌨️ (5:42:34) Two.III.2 Dimension ⌨️ (6:03:24) Two.III.3 Vector Spaces and Linear Systems ⌨️ (6:25:09) Three.I.1 Isomorphism, Part One ⌨️ (6:54:08) Three.I.1 Isomorphism, Part Two ⌨️ (7:21:47) Three.I.2 Dimension Characterizes Isomorphism ⌨️ (7:43:43) Three.II.1 Homomorphism, Part One ⌨️ (8:14:52) Three.II.1 Homomorphism, Part Two ⌨️ (8:30:24) Three.II.2 Range Space and Null Space, Part One ⌨️ (9:00:17) Three.II.2 Range Space and Null Space, Part Two. ⌨️ (9:20:57) Three.II Extra Transformations of the Plane ⌨️ (9:52:06) Three.III.1 Representing Linear Maps, Part One. ⌨️ (10:13:18) Three.III.1 Representing Linear Maps, Part Two ⌨️ (10:34:18) Three.III.2 Any Matrix Represents a Linear Map ⌨️ (10:58:32) Three.IV.1 Sums and Scalar Products of Matrices ⌨️ (11:19:14) Three.IV.2 Matrix Multiplication, Part One ⌨️ Three.IV.2 Matrix Multiplication, Part Two (We accidentally left this section out. Watch it here: https://youtu.be/aWubyx5bBn4) The following sections are in the second video: https://youtu.be/DJ6YwBN7Ya8 ⌨️ Three.IV.3 Mechanics of Matrix Multiplication ⌨️ Three.IV.4 Matrix Inverse, Part One ⌨️ Three.IV.4 Matrix Inverse, Part Two ⌨️ Three.V.1 Changing Vector Representations ⌨️ Three.V.2 Changing Map Representations, Part One ⌨️ Three.V.2 Changing Map Representations, Part Two ⌨️ Three.VI Projection ⌨️ Four.I.1 Determinants ⌨️ Four.I.3 Permutation Expansion, Part One ⌨️ Four.I.3 Permutation Expansion, Part Two ⌨️ Four.I.4 Determinants Exist (optional) ⌨️ Four.II.1 Geometry of Determinants ⌨️ Four.III.1 Laplace's formula for the determinant ⌨️ Five.I.1 Complex Vector Spaces ⌨️ Five.II.1 Similarity ⌨️ Five.II.2 Diagonalizability ⌨️ Five.II.3 Eigenvalues and Eigenvectors, Part One ⌨️ Five.II.3 Eigenvalues and Eigenvectors, Part Two ⌨️ Five.II.3 Geometry of Eigenvalues and Eigenvectors -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news
2020年11月19日
00:00:00 - 11:39:45
we all NOT gonna make it.. but you still CAN, and probably have 3 years left

we all NOT gonna make it.. but you still CAN, and probably have 3 years left

#bitcoin #crypto #stocks Consider dropping a like if you enjoyed the video and say hello to Russ! 👍 A free 500 USDT trade on ByBit as mentioned: https://partner.bybit.com/b/ddash500 To get access to Russ's Live Telegram Group, sign up for the DashReport 💹➡️ http://nicholasmerten.com/dashreport/ I will provide my broader trades and intraweek updates on Twitter : https://twitter.com/RSvinia 00:00 - What If There Is No WAGMI, But A Lost Decade 03:50 - What Gives Me This Feeling 07:20 - And How To Still Make It ---------------------------------------------------------------------------------------------------------- If you want me to be able to contact you in case of further issues, here's our email list: https://forms.gle/ADorNSFkkGbggJHY6 What are your thoughts on what we discussed? Feel free to leave a comment below! Thank you all so much for watching the video. If you enjoyed the video, please consider dropping a like, subscribing, and ringing the bell icon. ---------------------------------------------------------------------------------------------------------- 👥 For advertising, consulting, speaking, or other business inquiries reach out to us at: http://nicholasmerten.com/advertising/ Alternatively, feel free to reach us at: [email protected] ---------------------------------------------------------------------------------------------------------- WARNING: I WILL NEVER PURSUE PROJECTS THROUGH TELEGRAM OR OTHER SOCIAL MEDIA OUTLETS. CONTACT MY EMAIL LISTED BELOW FIRST AND THEN VERIFY MY IDENTITY THROUGH A VIDEO CALL BEFORE MOVING FORWARD WITH SPONSORSHIPS. THERE ARE MANY SCAMMERS IN CRYPTO. EMAIL SPOOFING IS RAMPANT, SO VERIFY MY IDENTITY THROUGH VIDEO BEWARE OF SCAM COMMENTS REQUESTING YOU TO SEND FUNDS. THESE ARE IMPERSONATORS, AS WE WILL NEVER REQUEST YOU TO SEND PAYMENTS Disclaimer: DataDash ("Company") and The Dash Report are not an investment advisory service, nor a registered investment advisor or broker-dealer and does not purport to tell or suggest which securities or currencies customers or viewers should buy or sell for themselves. All information is for discussion and educational purposes only. The independent contractors, employees or affiliates of Company may hold positions in the stocks, currencies or industries discussed here. You understand and acknowledge that there is a very high degree of risk involved in trading securities and cryptocurrencies. The Company assumes no responsibility or liability for your trading results. Factual statements on the Companies outlets are made as of the date stated and are subject to change without notice. It should not be assumed that the methods, techniques or indicators presented in these products will be profitable or that they will not result in losses. Past results of any individual trader or trading system published by the Company are not indicative of future returns by that trader or system and are not indicative of future returns which be realized by you. In addition, the indicators, strategies, columns, articles and all other features of the Company's products (collectively, the "information") are provided for informational and educational purposes only and should not be construed as investment advice. #bitcoin #crypto #cryptocurrency #investing
2024年06月21日
00:00:00 - 00:31:19