Learn Machine Learning in a way that is accessible to absolute beginners. You will learn the basics of Machine Learning and how to use TensorFlow to implement many different concepts. ✏️ Kylie Ying developed this course. Check out her channel: https://www.youtube.com/c/YCubed ⭐️ Code and Resources ⭐️ 🔗 Supervised learning (classification/MAGIC): https://colab.research.google.com/drive/16w3TDn_tAku17mum98EWTmjaLHAJcsk0?usp=sharing 🔗 Supervised learning (regression/bikes): https://colab.research.google.com/drive/1m3oQ9b0oYOT-DXEy0JCdgWPLGllHMb4V?usp=sharing 🔗 Unsupervised learning (seeds): https://colab.research.google.com/drive/1zw_6ZnFPCCh6mWDAd_VBMZB4VkC3ys2q?usp=sharing 🔗 Dataets (add a note that for the bikes dataset, they may have to open the downloaded csv file and remove special characters) 🔗 MAGIC dataset: https://archive.ics.uci.edu/ml/datasets/MAGIC+Gamma+Telescope 🔗 Bikes dataset: https://archive.ics.uci.edu/ml/datasets/Seoul+Bike+Sharing+Demand 🔗 Seeds/wheat dataset: https://archive.ics.uci.edu/ml/datasets/seeds 🏗 Google provided a grant to make this course possible. ⭐️ Contents ⭐️ ⌨️ (0:00:00) Intro ⌨️ (0:00:58) Data/Colab Intro ⌨️ (0:08:45) Intro to Machine Learning ⌨️ (0:12:26) Features ⌨️ (0:17:23) Classification/Regression ⌨️ (0:19:57) Training Model ⌨️ (0:30:57) Preparing Data ⌨️ (0:44:43) K-Nearest Neighbors ⌨️ (0:52:42) KNN Implementation ⌨️ (1:08:43) Naive Bayes ⌨️ (1:17:30) Naive Bayes Implementation ⌨️ (1:19:22) Logistic Regression ⌨️ (1:27:56) Log Regression Implementation ⌨️ (1:29:13) Support Vector Machine ⌨️ (1:37:54) SVM Implementation ⌨️ (1:39:44) Neural Networks ⌨️ (1:47:57) Tensorflow ⌨️ (1:49:50) Classification NN using Tensorflow ⌨️ (2:10:12) Linear Regression ⌨️ (2:34:54) Lin Regression Implementation ⌨️ (2:57:44) Lin Regression using a Neuron ⌨️ (3:00:15) Regression NN using Tensorflow ⌨️ (3:13:13) K-Means Clustering ⌨️ (3:23:46) Principal Component Analysis ⌨️ (3:33:54) K-Means and PCA Implementations 🎉 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

In this No Black Box Machine Learning Course in JavaScript, you will gain a deep understanding of machine learning systems by coding without relying on libraries. This unique approach not only demystifies the inner workings of machine learning but also significantly enhances software development skills. ✏️ Course created by @Radu (PhD in Computer Science) 🎥 Watch part two: https://youtu.be/3wwiOSxDAmg HOMEWORK 🏠 1st assignment spreadsheet: https://docs.google.com/spreadsheets/d/16wIddJ9jKAAvJOXPcF0gNRx39AOE9A2-mQeK6UR2qnY/edit?usp=sharing 🏠 Submit all other assignments to Radu's Discord Server: https://discord.com/invite/gJFcF5XVn9 GITHUB LINKS 💻 Drawing App: https://github.com/gniziemazity/drawing-app 💻 Data: https://github.com/gniziemazity/drawing-data 💻 Custom Chart Component: https://github.com/gniziemazity/javascript_chart 💻 Full Course Code (In Parts): https://github.com/gniziemazity/ml-course PREREQUISITES 🎥 Interpolation: https://youtu.be/J_puRs40GhM 🎥 Linear Algebra: https://youtu.be/nzyOCd9FcCA 🎥 Trigonometry: https://youtu.be/xK3vKWMFVgw LINKS 🔗 Check out the Recognizer we'll build in this course: https://radufromfinland.com/projects/ml/recognizer 🔗 Draw for Radu, Call for help video: https://youtu.be/Yw2QZ1vq2ek 🔗 Draw for Radu, Data collection tool: https://radufromfinland.com/projects/ml 🔗 Radu's Self-driving Car Course: https://www.youtube.com/playlist?list=PLB0Tybl0UNfYoJE7ZwsBQoDIG4YN9ptyY 🔗 Radu's older Machine Learning video: https://youtu.be/QXB1ytG95gs 🔗 CHART TUTORIAL (mentioned at 01:45:27): https://youtu.be/n8uCt1TSGKE 🔗 CHART CODE: https://github.com/gniziemazity/javascript_chart TOOLS 🔧 Visual Studio Code: https://code.visualstudio.com/download 🔧 Google Chrome: https://www.google.com/chrome 🔧 Node JS: https://nodejs.org/en/download (make sure you add 'node' and 'npm' to the PATH environment variables when asked!) TIMESTAMPS ⌨️(0:00:00) Introduction ⌨️(0:05:04) Drawing App ⌨️(0:46:46) Homework 1 ⌨️(0:47:05) Working with Data ⌨️(1:08:54) Data Visualizer ⌨️(1:29:52) Homework 2 ⌨️(1:30:05) Feature Extraction ⌨️(1:38:07) Scatter Plot ⌨️(1:46:12) Custom Chart ⌨️(2:01:03) Homework 3 ⌨️(2:01:35) Nearest Neighbor Classifier ⌨️(2:43:21) Homework 4 (better box) ⌨️(2:43:53) Data Scaling ⌨️(2:54:45) Homework 5 ⌨️(2:55:23) K Nearest Neighbors Classifier ⌨️(3:04:18) Homework 6 ⌨️(3:04:49) Model Evaluation ⌨️(3:21:29) Homework 7 ⌨️(3:22:01) Decision Boundaries ⌨️(3:39:26) Homework 8 ⌨️(3:39:59) Python & SkLearn ⌨️(3:50:35) Homework 9

Even if you are completely new to neural networks, this course will get you comfortable with the concepts and math behind them. Neural networks are at the core of what we are calling Artificial Intelligence today. They can seem impenetrable, even mystical, if you are trying to understand them for the first time, but they don't have to. ⭐️ Contents ⭐️ ⌨️ (0:00:00) How neural networks work ⌨️ (0:24:13) What neural networks can learn and how they learn it ⌨️ (0:51:37) How convolutional neural networks (CNNs) work ⌨️ (1:16:55) How recurrent neural networks (RNNs) and long-short-term memory (LSTM) work ⌨️ (1:42:49) Deep learning demystified ⌨️ (2:03:33) Getting closer to human intelligence through robotics ⌨️ (2:49:18) How CNNs work, in depth 🎥 Lectures by Brandon Rohrer. Check out his YouTube channel: https://www.youtube.com/user/BrandonRohrer 🔗 Find more courses from Brandon at https://end-to-end-machine-learning.teachable.com/ -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://medium.freecodecamp.org And subscribe for new videos on technology: https://youtube.com/subscription_center?add_user=freecodecamp #machine learning #deep learning #neural networks #artificial intelligence #logistic regression #probability #math #computer science #statistics #deep learning course #deep learning tutorial #neural networks for beginners #deep neural network #convolutional neural networks #recurrent neural networks #long-short-term memory #ai

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🔴 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

Prepare for a job interview about deep learning. This course covers 50 common interview questions related to deep learning and gives detailed explanations. ✏️ Course created by Tatev Karen Aslanyan. ✏️ Expanded course with 100 questions: https://courses.lunartech.ai/courses/deep-learning-interview-preparation-course-100-q-a-s ⭐️ Contents ⭐️ ⌨️ 0:00:00 Introduction ⌨️ 0:08:20 Question 1: What is Deep Learning? ⌨️ 0:11:45 Question 2: How does Deep Learning differ from traditional Machine Learning? ⌨️ 0:15:25 Question 3: What is a Neural Network? ⌨️ 0:21:40 Question 4: Explain the concept of a neuron in Deep Learning ⌨️ 0:24:35 Question 5: Explain architecture of Neural Networks in simple way ⌨️ 0:31:45 Question 6: What is an activation function in a Neural Network? ⌨️ 0:35:00 Question 7: Name few popular activation functions and describe them ⌨️ 0:47:40 Question 8: What happens if you do not use any activation functions in a neural network? ⌨️ 0:48:20 Question 9: Describe how training of basic Neural Networks works ⌨️ 0:53:45 Question 10: What is Gradient Descent? ⌨️ 1:03:50 Question 11: What is the function of an optimizer in Deep Learning? ⌨️ 1:09:25 Question 12: What is backpropagation, and why is it important in Deep Learning? ⌨️ 1:17:25 Question 13: How is backpropagation different from gradient descent? ⌨️ 1:19:55 Question 14: Describe what Vanishing Gradient Problem is and it’s impact on NN ⌨️ 1:25:55 Question 15: Describe what Exploding Gradients Problem is and it’s impact on NN ⌨️ 1:33:55 Question 16: There is a neuron in the hidden layer that always results in an error. What could be the reason? ⌨️ 1:37:50 Question 17: What do you understand by a computational graph? ⌨️ 1:43:28 Question 18: What is Loss Function and what are various Loss functions used in Deep Learning? ⌨️ 1:47:15 Question 19: What is Cross Entropy loss function and how is it called in industry? ⌨️ 1:50:18 Question 20: Why is Cross-entropy preferred as the cost function for multi-class classification problems? ⌨️ 1:53:10 Question 21: What is SGD and why it’s used in training Neural Networks? ⌨️ 1:58:24 Question 22: Why does stochastic gradient descent oscillate towards local minima? ⌨️ 2:03:38 Question 23: How is GD different from SGD? ⌨️ 2:08:19 Question 24: How can optimization methods like gradient descent be improved? What is the role of the momentum term? ⌨️ 2:14:22 Question 25: Compare batch gradient descent, minibatch gradient descent, and stochastic gradient descent. ⌨️ 2:19:12 Question 26: How to decide batch size in deep learning (considering both too small and too large sizes)? ⌨️ 2:26:01 Question 27: Batch Size vs Model Performance: How does the batch size impact the performance of a deep learning model? ⌨️ 2:29:33 Question 28: What is Hessian, and how can it be used for faster training? What are its disadvantages? ⌨️ 2:34:12 Question 29: What is RMSProp and how does it work? ⌨️ 2:38:43 Question 30: Discuss the concept of an adaptive learning rate. Describe adaptive learning methods ⌨️ 2:43:34 Question 31: What is Adam and why is it used most of the time in NNs? ⌨️ 2:49:59 Question 32: What is AdamW and why it’s preferred over Adam? ⌨️ 2:54:50 Question 33: What is Batch Normalization and why it’s used in NN? ⌨️ 3:03:19 Question 34: What is Layer Normalization, and why it’s used in NN? ⌨️ 3:06:20 Question 35: What are Residual Connections and their function in NN? ⌨️ 3:15:05 Question 36: What is Gradient clipping and their impact on NN? ⌨️ 3:18:09 Question 37: What is Xavier Initialization and why it’s used in NN? ⌨️ 3:22:13 Question 38: What are different ways to solve Vanishing gradients? ⌨️ 3:25:25 Question 39: What are ways to solve Exploding Gradients? ⌨️ 3:26:42 Question 40: What happens if the Neural Network is suffering from Overfitting relate to large weights? ⌨️ 3:29:18 Question 41: What is Dropout and how does it work? ⌨️ 3:33:59 Question 42: How does Dropout prevent overfitting in NN? ⌨️ 3:35:06 Question 43: Is Dropout like Random Forest? ⌨️ 3:39:21 Question 44: What is the impact of Drop Out on the training vs testing? ⌨️ 3:41:20 Question 45: What are L2/L1 Regularizations and how do they prevent overfitting in NN? ⌨️ 3:44:39 Question 46: What is the difference between L1 and L2 regularisations in NN? ⌨️ 3:48:43 Question 47: How do L1 vs L2 Regularization impact the Weights in a NN? ⌨️ 3:51:56 Question 48: What is the curse of dimensionality in ML or AI? ⌨️ 3:53:04 Question 49: How deep learning models tackle the curse of dimensionality? ⌨️ 3:56:47 Question 50: What are Generative Models, give examples?

Understanding AI from Scratch – Neuaral Networks Without Libraries Course Learn the fundamentals of Neural Networks by building one from scratch without libraries. You'll manually tweak network parameters to teach a car how to drive within a specially crafted playground. Dr. Radu teaches computer science at a university in Finland, and has over a decade of experience with machine learning and AI, and he emphasizes the importance of revisiting basics in an era of easy-to-use complex models. You will have homework assignments which you can discuss with Dr. Radu on his Discord: discord.gg/gJFcF5XVn9 ✏️ Course developed by @Radu 🏆 Win prizes by following along: https://youtu.be/EhJ7qD5HwpY 🚗 THE PLAYGROUND: https://radufromfinland.com/projects/aiCarPlayground/?s=default 🚙 HELP COLLECT DATA BY RACING: https://radufromfinland.com/projects/aiRacer ⭐ OTHER LINKS ⭐ Phase 1 – Self-Driving Car with JavaScript: https://youtu.be/Rs_rAxEsAvI Phase 2 – Virtual World Filled with Self-Driving Cars: https://youtu.be/5iHejdqYIa8 Vanilla JavaScript Chart Tutorial: https://youtu.be/n8uCt1TSGKE Machine Learning JavaScript Course: https://youtu.be/vDDjtwQDw2k 3b1b's Neural Network Video: https://youtu.be/aircAruvnKk?si=JB6fvUT3Ko1mkU3x Another Playground: https://playground.tensorflow.org Desmos 3D: https://www.desmos.com/3d Another Playground: https://playground.tensorflow.org Top-view Cars Clip: https://youtu.be/jZrjE_ixu18 💻 CODE 💻 1. Phase 1 https://github.com/gniziemazity/self-driving-car 2. Phase 2 https://github.com/gniziemazity/virtual-world 3. Phase 3 https://github.com/gniziemazity/understanding_ai 💻 Use "11. MiniMap" from Phase 2 at 02:36:49 💻 Use "1. Starting Codebase" from Phase 3 in Lesson 6 (if you didn't code anything yet) ☕ Buy Radu a Coffee: https://www.buymeacoffee.com/radum ⚡️Join Radu's Channel: https://youtube.com/@radu/join ⭐ Contents ⭐ ⌨️ (0:00:00) Introduction ⌨️ (0:07:12) The Playground ⌨️ (0:14:34) One Neuron ⌨️ (0:29:07) Clarrifications ⌨️ (0:31:58) Lesson 2 ⌨️ (0:32:16) Genetic Algorithm ⌨️ (0:43:04) 2 Inputs ⌨️ (1:02:42) Hidden Layers ⌨️ (1:13:03) Misconceptions ⌨️ (1:14:18) Lesson 3 (More Outputs) ⌨️ (1:55:59) Lesson 4 (Traffic Rules) ⌨️ (2:24:10) Lesson 5 (Compass Sensor) ⌨️ (2:35:20) The need for Shortest Path ⌨️ (2:37:10) Updating the Self-driving Car codebase ⌨️ (2:57:39) Lesson 6 (Dijkstra's Algorithm) ⌨️ (3:24:03) Lesson 7 (Dijkstra with AI Agents) ⌨️ (3:43:13) Final Challenge

Learn prompt engineering techniques to get better results from ChatGPT and other LLMs. ✏️ Course developed by @AniaKubow ⭐️ Contents ⭐️ ⌨️ (00:00) Introduction ⌨️ (01:31) What is Prompt Engineering? ⌨️ (02:17) Introduction to AI ⌨️ (03:52) Why is Machine learning useful? ⌨️ (06:36) Linguistics ⌨️ (08:04) Language Models ⌨️ (14:35) Prompt Engineering Mindset ⌨️ (15:38) Using GPT-4 ⌨️ (20:41) Best practices ⌨️ (31:20) Zero shot and few shot prompts ⌨️ (35:06) AI hallucinations ⌨️ (36:43) Vectors/text embeddings ⌨️ (40:28) 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 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. Hindi इस वीडियो को ज़्यादा लोगों तक पहुंचाने के लिए, इसे https://aloud.area120.google.com के ज़रिए एक आर्टिफ़िशियल वॉइस का इस्तेमाल करके डब किया गया है. सेटिंग्स मेनू में जाकर ऑडियो ट्रैक की भाषा बदली जा सकती है. (Hindi dubbed via Melt Labs - https://www.withmelt.com/) -- Learn to code for free and get a developer job: https://www.freecodecamp.org Read hundreds of articles on programming: https://freecodecamp.org/news

This video contains over 9 hours of python project tutorials designed to help you master python and learn through projects! There are over 21 unique python project walkthroughs in this content that range from beginner to advanced. See below for the timestamps for these projects: ➡️ Check out my software development course! https://techwithtim.net/dev 📖 General Resources 📖 Download VSCode: https://code.visualstudio.com/ Fix Pip (Windows): https://www.youtube.com/watch?v=AdUZArA-kZw&t=0s Fix Pip (Mac): https://www.youtube.com/watch?v=E-WhAS6qzsU&t=4s 🕥 Timestamps 🕥 00:00:00 | How To Navigate These Projects --- 00:01:46 | #1 - Quiz Game (Easy) Code: https://github.com/techwithtim/5-Python-Projects-For-Beginners/blob/main/quiz_game.py --- 00:22:00 | #2 - Number Guessing Game (Easy) Code: https://github.com/techwithtim/5-Python-Projects-For-Beginners/blob/main/number_guesser.py --- 00:39:49 | #3 - Rock, Paper, Scissors (Easy) Code: https://github.com/techwithtim/5-Python-Projects-For-Beginners/blob/main/rock_paper_scissors.py --- 00:54:40 | #4 - Choose Your Own Adventure Game (Easy) Code: https://github.com/techwithtim/5-Python-Projects-For-Beginners/blob/main/choose_your_own_adventure.py --- 01:06:47 | #5 - Password Manager (Medium) Code: https://github.com/techwithtim/5-Python-Projects-For-Beginners/ Fernet Cryptography Documentation: https://cryptography.io/en/latest/fernet/ --- 01:37:37 | #6 - PIG (Medium) Code: https://github.com/techwithtim/3-Mini-Python-Projects/blob/main/project1.py --- 01:59:07 | #7 - Madlibs Generator (Medium) Code: https://github.com/techwithtim/3-Mini-Python-Projects/blob/main/project2.py --- 02:15:04 | #8 - Timed Math Challenge (Medium) Code: https://github.com/techwithtim/3-Mini-Python-Projects/blob/main/project3.py --- 02:28:02 | #9 - Slot Machine (Medium) Code: https://github.com/techwithtim/Python-Slot-Machine --- 03:20:43 | #10 - Turtle Racing (Medium) Code: https://github.com/techwithtim/Turtle-Racing-V2 Turtle Docs: https://docs.python.org/3/library/turtle.html --- 04:13:09 | #11 - WPM Typing Test (Medium) Code: https://github.com/techwithtim/WPM_Typing_Test Curses Docs: https://docs.python.org/3/howto/curses.html 05:09:43 | #12 - Alarm Clock (Easy) Python Project Idea Blog: https://hackr.io/blog/python-projects Sound Effects: https://www.fesliyanstudios.com/royalty-free-sound-effects-download/alarm-203 --- 05:22:07 | #13 - Password Generator (Easy) Python Project Idea Blog: https://hackr.io/blog/python-projects --- 05:39:16 | #14 - Shortest Path Finder (Advanced) Code: https://github.com/techwithtim/3-Mini-Python-Projects-For-Intermediates/blob/main/path-finder.py --- 06:14:53 | #15 - NBA Stats & Current Scores (Medium) Code: https://github.com/techwithtim/3-Mini-Python-Projects-For-Intermediates/blob/main/nba-scores.py --- 06:38:22 | #16 - Currency Converter (Medium) Code: https://github.com/techwithtim/3-Mini-Python-Projects-For-Intermediates/blob/main/currency-converter.py API: https://free.currencyconverterapi.com/ --- 06:58:51 | #17 - YouTube Video Downloader (Medium) Code: https://github.com/techwithtim/Python-Beginner-Automation-Projects/blob/main/youtube.py --- 07:09:50 | #18 - Automated File Backup (Medium) Code: https://github.com/techwithtim/Python-Beginner-Automation-Projects/blob/main/backup.py --- 07:21:18 | #19 - Mastermind/4 Color Match (Advanced) --- 07:48:20 | #20 - Aim Trainer (Advanced) Code: https://github.com/techwithtim/Python-Aim-Trainer --- 08:39:20 | #21 - Advanced Python Scripting (Advanced) Code: https://github.com/techwithtim/Python-Scripting-Project #python projects #learn python #python project tutorial #python tutorial #python tech with tim #tech with tim python #python mini projects #projects python #how to learn python #python project based learning #21 python projects #python masterclass #python practice #learning python #how to learn python fast #python tutorials #python coding #python programming #python #software development #coding projects #programming projects

In this comprehensive course on algorithmic trading, you will learn about three cutting-edge trading strategies to enhance your financial toolkit. In the first module, you'll explore the Unsupervised Learning Trading Strategy, utilizing S&P 500 stocks data to master features, indicators, and portfolio optimization. Next, you'll leverage the power of social media with the Twitter Sentiment Investing Strategy, ranking NASDAQ stocks based on engagement and evaluating performance against the QQQ return. Lastly, the Intraday Strategy will introduce you to the GARCH model, combining it with technical indicators to capture both daily and intraday signals for potential lucrative positions. ✏️ Course developed by @lachone_ 💻 Code and course resources: https://github.com/Luchkata/Algorithmic_Trading_Machine_Learning/tree/main 🔗 You can sign up for the data API used here: https://intelligence.financialmodelingprep.com/pricing-plans?couponCode=lachezaryt&utm_source=youtube&utm_medium=youtube&utm_campaign=lachezarfcc 🔗 Learn more about Lachezar and Quantitative Trading with Python here: https://www.quantfactory.ai/p/become-a-quant-trader1 ⭐️ Contents ⭐️ 0:00:00 - Algorithmic Trading & Machine Learning Fundamentals 0:15:25 - Building An Unsupervised Learning Trading Strategy 2:05:08 - Building A Twitter Sentiment Investing Strategy 2:28:08 - Building An Intraday Strategy Using GARCH Model 🎉 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

This course from Harvard University explores the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like large language models, game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. This course has been updated for 2023 to include an in-depth section on large language models. ✏️ Course developed by Brian Yu for Harvard University. Learn more about Brian: https://brianyu.me/ 🔗 Course resources: https://cs50.harvard.edu/ai/2020/ ⭐️ Course Contents ⭐️ ⌨️ (00:00:00) Introuction ⌨️ (00:02:26) Search ⌨️ (01:51:55) Knowledge ⌨️ (03:39:39) Uncertainty ⌨️ (05:34:08) Optimization ⌨️ (07:18:52) Learning ⌨️ (09:04:41) Neural Networks ⌨️ (10:46:00) Language 🎉 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

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Welcome to this No Black Box Machine Learning Course in JavaScript. It’s a course where we code without using libraries because it’s the best way to learn all inner workings of a machine learning system and you’ll greatly improve your software development skills as well. The goal in this course is to build a web app that learns to recognize drawings. This is phase 2, where we increase the accuracy of the method we developed in Phase 1. We do this by implementing more sophisticated features and using other classification methods (like the Neural Network). In Phase 2 we also learn about Data Cleaning, Confusion Matrices, Geometry and the difference between Vector and Raster data (pixels). 🎥 No Black Box Phase 1 Course: https://youtu.be/vDDjtwQDw2k ✏️ Course created by @Radu (PhD in Computer Science) 📁 Data: https://github.com/gniziemazity/drawing-data 💻 Code: https://github.com/gniziemazity/ml-course-phase-2 💻 Ilya's code: https://gist.github.com/id-ilych/8630fb273e5c5a0b64ca1dc080d68b63 💻 Neural Network Code: https://github.com/gniziemazity/neural-network Phase 3 Poll: https://forms.office.com/e/QTMCLLaV24 ⭐️ Other Resources ⭐️ Recognizer we build in this course: https://radufromfinland.com/projects/ml/recognizer Euclidean Distance Video: https://youtu.be/3rPwfmrCwVw Interpolation Video: https://youtu.be/J_puRs40GhM Draw the Portal Game Tutorial (Inspired from Dr. Strange): https://youtu.be/0SxiyLk2IMM Why the Circle has the Largest Area: https://youtu.be/CFBa2ezTQJQ Recognizing drawings via webcam: https://youtu.be/QXB1ytG95gs Self-driving Car Course: https://youtu.be/Rs_rAxEsAvI Discord Server: https://discord.com/invite/gJFcF5XVn9 Scikit-learn documentation: http://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html ⭐️ Contents ⭐️ 0:00:00 Introduction 0:04:07 Phase 1 Code Review 0:23:11 Data Cleaning 0:41:30 Confusion Matrix 1:16:00 Euclidean Distance Marker 1:16:06 Measuring the Elongation 1:39:23 Measuring the Roundness 1:59:20 Vector vs Raster (Pixels) 2:22:40 Neural Networks 3:04:49 Optimizing Neural Networks 3:25:15 Deep Neural Networks 🎉 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

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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

In this video I'm going to teach you Python by working through a project from scratch! A completely new project to me! Hopefully this will make you a lot more confident and help you learn Python. Follow along and comment down below how it worked for you! Get started with OctoML and Download the CLI for free at: https://try.octoml.ai/Tim 💻 ProgrammingExpert is the best platform to learn how to code and become a software engineer as fast as possible! Check it out here: https://programmingexpert.io/tim and use code "tim" for a discount! Completed Code: https://github.com/techwithtim/Python-Slot-Machine ⭐️ Timestamps ⭐️ 00:00 | Learn Python With One Project 00:54 | OctoML 02:10 | Building The Project 53:45 | Outro ◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️ 👕 Merchandise: https://teespring.com/stores/tech-with-tim-merch-shop 📸 Instagram: https://www.instagram.com/tech_with_tim 📱 Twitter: https://twitter.com/TechWithTimm ⭐ Discord: https://discord.gg/twt 📝 LinkedIn: https://www.linkedin.com/in/tim-ruscica-82631b179/ 🌎 Website: https://techwithtim.net 📂 GitHub: https://github.com/techwithtim 🔊 Podcast: https://anchor.fm/tech-with-tim 🎬 My YouTube Gear: https://www.techwithtim.net/gear/ 💵 One-Time Donations: https://www.paypal.com/donate?hosted_button_id=CU9FV329ADNT8 💰 Patreon: https://www.patreon.com/techwithtim ◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️◼️ ⭐️ Hashtags ⭐️ #techwithtim #python #learnpython #octoml #tech with tim #python #learn python #learn python with 1 project #python how to #how to do python project #pulumi #pulumi promo code #octoml #free coupon code #octoml coupon code #python porject #python project tech with tim #tech with tim python #python project for beginners #beginner python project #learn python through projects #python tutorial 2022 #python tutorial project

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Below are the topics covered in this Cyber Security Training For Beginners: 00:00:00 Introduction 00:05:35 Requirement of Cyber Security 00:23:44 History of Cybersecurity 00:34:57 Types of hackers 00:42:57 Skills Necessary 00:51:45 What is Penetration testing? 01:01:22 What is Footprinting? 01:13:40 Handson 01:33:23 History of the internet 01:42:11 OSI and TCP/IP Model 01:50:59 What is Wireshark? 01:58:59 What is DHCP? 02:08:36 Cryptography 02:11:00 History of Cryptography 02:19:00 Digital Encryption Standard 02:54:13 Bitlocker 03:07:50 What is IDS 03:15:50 What is pishing 03:20:38 Password Attacks 03:23:50 Packet Flooding 03:27:02 What is a Drive-by-download 03:33:26 BluVector 03:46:15 Cybersecurity Frameworks 04:03:51 Cybersecurity is an evergreen industry 04:19:52 Why Become an cyber security engineer 04:23:52 Who is a cybersecurity engineer ? 04:26:17 Roles and responsibilities of cyber security 04:59:05 How to choose the right cybersecurity certification? 05:05:29 Keylogger 05:07:53 SQL Vulnerability assessment 05:35:54 Top Cyber attacks in history 05:40:42 Cybersecurity challenges 05:46:18 Types of hacking 06:08:43 What is kali Linux? 06:14:19 Hands-on in Kali Linux 06:39:56 What is a proxy chain? 06:55:08 What is a Mac Address? 07:26:21 Cryptography 08:23:59 What is cross-site scripting? 08:26:23 types of cross-site scripting 08:36:00 How to use cross-site scripting 08:52:48 How to prevent cross-site scripting? 09:13:37 What is SQL Injection? 09:32:49 What is steganography? 10:00:50 Steganography tools 10:13:39 Ethical hacking and roles in ethical hacking 10:18:27 Ethical hacking tools 10:21:39 Cybersecurity interview questions 🔴 𝐋𝐞𝐚𝐫𝐧 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐅𝐨𝐫 𝐅𝐫𝐞𝐞! 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Starting from the Ground level Security Essentials, this course will lead you through Cryptography. Why Learn Cyber Security? Cybersecurity is the gathering of advances that procedures and practices expected to ensure systems, PCs, projects and information from assault, harm or unapproved get to. In a processing setting, security incorporates both cybersecurity and physical security. For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free). #yt:cc=on #cybersecurity full course #cyber security training for beginners #cybersecurity #cyber security #cyber security full course #cybersecurity and ethical hacking course #cybersecurity and ethical hacking #cybersecurity crash course #cybersecurity for beginners #cyber security full course for beginners #cyber security course #what is cyber security #cyber security tutorial #learn cyber security #ethical hacking #cybersecurity roadmap #cybersecurity edureka #Edureka

Learn about Microsoft Azure and pass the Azure Fundamentals exam! The Azure Fundamentals exam (AZ-900) is an opportunity to prove knowledge of cloud concepts, core Azure services, Azure pricing, SLA, and lifecycle, and the fundamentals of cloud security, privacy, compliance, and trust. You will learn about all these things in this course. ✏️ Course developed by Andrew Brown of ExamPro. Check out the ExamPro YouTube channel: https://www.youtube.com/channel/UC2EsmbKnDNE7y1N3nZYCuGw 🔗 ExamPro Certification Training: https://www.exampro.co 🐦 Twitter: https://twitter.com/andrewbrown 📷 Instagram: https://www.instagram.com/exampro.co/ ⭐️ Course Contents ⭐️ ☁️ (00:01:05) Introduction Why should I get the AZ-900? Exam Guide Walkthrough ☁️ (00:24:24) Cloud Concepts What is Cloud Computing Common Cloud Services What is Microsoft and Azure Benefits of Cloud Computing Types of Cloud Computing Types of Cloud Computing Responsibilities Cloud Deployment Models Total Cost of Ownership CAPEX vs OPEX Cloud Architecture Terminologies High Availability High Scalability High Elasticity Fault Tolerance High Durability Business Continuity Plan Disaster Recovery Options ☁️ (00:57:44) Evolution of Compute Dedicated Servers Virtual Machines Containers Functions ☁️ (01:03:37) Global Infrastructure Regions and Geographies Paired Regions Region Types and Service Availability Special Regions Availability Zones AZ Supported Regions Availability Sets Fault and Update Domains ☁️ (01:19:26) Getting Started Follow Along Creating an Azure Account Registered Providers Create a User Account Enabling MFA ☁️ (01:44:28) Compute Services Computing Services Azure Virtual machines Operation Systems Azure Scale Sets Load Balancer Scaling Policy Health Montoring Azure Virtual Desktop Launching a Server ☁️ (02:06:57) Azure App Service Introduction to Azure App Service Runtimes Custom Containers Deployment Slots App Service Enviroment Azure App Service Plan Configure and Deploy App Service ☁️ (02:27:13) Azure Container Instances Introduction to ACI Container Restart Policies Container Environment Variables Container Persistent Storage Create an Azure Container Instances ☁️ (02:34:49) Virtual Networking Introduction to VNets VNet Peering Network Interfaces Subnets Introduction to Azure DNS Introduction to Virtual Network Gateways Azure Express Routes Private Links Create a VNet ☁️ (02:49:27) Storage Accounts Storage Services Introduction to Storage Accounts Storage Comparsion Core Storage Services Performance Tiers Access Tiers Replication Data Redundancy LRS ZRS GRS GZRS RAGRS_RA GZRS Introduction to Azure Blob Azure Blob Types AZCopy Azure Storage Explorer Introduction to Azure Files Azure Files Use Cases Azure File Sync Storing files in Blob Storage ☁️ (03:24:14) Migration Options Azure Cloud Adoption Framework Azure Migrate Azure Migrate Integrated tools Azure Databox ☁️ (03:33:10) Microsoft Entra ID Microsoft Entra ID Name Change Introduction to Entra ID Use Cases AD vs AAD Terminology AD DS Microsoft Entra Domain Services Single Sign On SSO External Identities Conditional Access ☁️ (03:49:46) Security Zero Trust Model Zero Trust Principles Defense In Depth Azure Defender MFA Azure Security Center Key Vault Azure DDoS Protection Azure Firewall Application Gateway Routing Rules Role Based Access Control RBAC Management Groups Service Health Azure Advisor ☁️ (04:16:09) Technology Overview Database Services Application Integration Developer and Mobile Tools Azure DevOps Services Cloud Native Networking Services Enterprise Hybrid Networking Services Azure Traffic Manager IoT Services Big Data and Analytics Services AI ML Services Introduction AI ML Services Serverless Services ☁️ (04:39:34) Follow Alongs Create a Resource Group Creating an Azure Function ☁️ (04:45:54) Billing And Pricing Service Level Agreements Service Credits Composite SLAs TCO Calculator Azure Marketplace Azure Hybrid Benefit Azure Subscriptions First Look at Subscriptions Pricing Calculator Azure Cost Management Resource Tags ☁️ (05:07:47) Governance and Compliance Microsoft Purview Information Protection Introduction Resource Locks Azure Blueprints ☁️ (05:15:31) Azure Management Tools Azure Portal Portal Preview and Features Cloud Shell Exploring Azure Cloud Shell PowerShell PowerShell Follow Along Azure CLI Azure CLI Follow Along Visual Studio Code Azure SDK Follow Along Introduction to ARM Use Cases ARM Scoping Introduction ARM Templates Follow Along Azure Bicep Follow Along Azure Terraform Follow Along Free Tier Spend and Usage ☁️ (08:12:11) Monitoring Tools Introduction to Azure Monitor The Pillars of Obervability Anatomy of Azure Monitor Log Analytics Log Analytics Workspaces Azure Alerts Application Insights

Learn the basics of the Linux Operating System in this crash course for beginners. Linux is a clone of the UNIX operating system, so understanding one is understanding the other. The goal is for you to get a full understanding of how the Linux OS works. If you're still using Windows or Mac OS, you can still learn Linux using a virtual machine. ✏️ Course from KeepItTechie. Check out his YouTube channel: https://www.youtube.com/channel/UCP2bshADPrVMoNrdJvZEQzw ⭐️ Course Contents ⭐ ⌨️ (0:00:00) Intro ⌨️ (0:08:09) Install Linux ⌨️ (0:29:43) Desktop Environment ⌨️ (0:41:18) Terminal ⌨️ (0:49:25) Working with Directories ⌨️ (1:15:49) Working with Files ⌨️ (1:34:49) Working with File Content ⌨️ (1:51:14) Linux File Structure ⌨️ (2:03:68) System Information ⌨️ (2:15:42) Networking ⌨️ (2:20:10) Linux Package Manager ⌨️ (2:34:10) Text Editor ⌨️ (2:46:56) Outro 🎉 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

🔥 Data Science Course (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎"): https://www.edureka.co/masters-program/data-scientist-certification This Edureka Data Science Full Course video will help you understand and learn Data Science Algorithms in detail. This Data Science Tutorial is ideal for both beginners as well as professionals who want to master Data Science Algorithms. Below are the topics covered in this Data Science for Beginners course: 00:00 Data Science Full Course Agenda 2:44 Introduction to Data Science 9:55 Data Analysis at Walmart 13:20 What is Data Science? 14:39 Who is a Data Scientist? 16:50 Data Science Skill Set 21:51 Data Science Job Roles 26:58 Data Life Cycle 30:25 Statistics & Probability 34:31 Categories of Data 34:50 Qualitative Data 36:09 Quantitative Data 39:11 What is Statistics? 41:32 Basic Terminologies in Statistics 42:50 Sampling Techniques 45:31 Random Sampling 46:20 Systematic Sampling 46:50 Stratified Sampling 47:54 Types of Statistics 50:38 Descriptive Statistics 55:52 Measures of Spread 55:56 Range 56:44 Inter Quartile Range 58:58 Variance 59:36 Standard Deviation 1:14:25 Confusion Matrix 1:19:16 Probability 1:24:14 What is Probability? 1:27:13 Types of Events 1:27:58 Probability Distribution 1:28:15 Probability Density Function 1:30:02 Normal Distribution 1:30:51 Standard Deviation & Curve 1:31:19 Central Limit Theorem 1:33:12 Types of Probability 1:33:34 Marginal Probability 1:34:06 Joint Probability 1:34:58 Conditional Probability 1:35:56 Use-Case 1:39:46 Bayes Theorem 1:45:44 Inferential Statistics 1:56:40 Hypothesis Testing 2:00:34 Basics of Machine Learning 2:01:41 Need for Machine Learning 2:07:03 What is Machine Learning? 2:09:21 Machine Learning Definitions 2:11:48 Machine Learning Process 2:18:31 Supervised Learning Algorithm 2:19:54 What is Regression? 2:21:23 Linear vs Logistic Regression 2:33:51 Linear Regression 2:25:27 Where is Linear Regression used? 2:27:11 Understanding Linear Regression 2:37:00 What is R-Square? 2:46:35 Logistic Regression 2:51:22 Logistic Regression Curve 2:53:02 Logistic Regression Equation 2:56:21 Logistic Regression Use-Cases 2:58:23 Demo 3:00:57 Implement Logistic Regression 3:02:33 Import Libraries 3:05:28 Analyzing Data 3:11:52 Data Wrangling 3:23:54 Train & Test Data 3:20:44 Implement Logistic Regression 3:31:04 SUV Data Analysis 3:38:44 Decision Trees 3:39:50 What is Classification? 3:42:27 Types of Classification 3:42:27 Decision Tree 3:43:51 Random Forest 3:45:06 Naive Bayes 3:47:12 KNN 3:49:02 What is Decision Tree? 3:55:15 Decision Tree Terminologies 3:56:51 CART Algorithm 3:58:50 Entropy 4:00:15 What is Entropy? 4:23:52 Random Forest 4:27:29 Types of Classifier 4:31:17 Why Random Forest? 4:39:14 What is Random Forest? 4:51:26 How Random Forest Works? 4:51:36 Random Forest Algorithm 5:04:23 K Nearest Neighbour 5:05:33 What is KNN Algorithm? 5:08:50 KNN Algorithm Working 5:24:30 What is Naive Bayes? 5:25:13 Bayes Theorem 5:27:48 Bayes Theorem Proof 5:29:43 Naive Bayes Working 5:39:06 Types of Naive Bayes 5:53:37 Support Vector Machine 5:57:40 What is SVM? 5:59:46 How does SVM work? 6:03:00 Introduction to Non-Linear SVM 6:04:48 SVM Example 6:06:12 Unsupervised Learning Algorithms - KMeans 6:06:18 What is Unsupervised Learning? 6:06:45 Unsupervised Learning: Process Flow 6:07:17 What is Clustering? 6:09:15 Types of Clustering 6:10:15 K-Means Clustering 6:10:40 K-Means Algorithm Working 6:16:17 K-Means Algorithm 6:19:16 Fuzzy C-Means Clustering 6:21:22 Hierarchical Clustering 6:22:53 Association Clustering 6:24:57 Association Rule Mining 6:30:35 Apriori Algorithm 6:37:45 Apriori Demo 6:40:49 What is Reinforcement Learning? 6:42:48 Reinforcement Learning Process 6:51:10 Markov Decision Process 6:54:53 Understanding Q - Learning 7:13:12 Q-Learning Demo 7:25:34 The Bellman Equation 7:48:39 What is Deep Learning? 7:52:53 Why we need Artificial Neuron? 7:54:33 Perceptron Learning Algorithm 7:57:57 Activation Function 8:03:14 Single Layer Perceptron 8:04:04 What is Tensorflow? 8:07:25 Demo 8:21:03 What is a Computational Graph? 8:49:18 Limitations of Single Layer Perceptron 8:50:08 Multi-Layer Perceptron 8:51:24 What is Backpropagation? 8:52:26 Backpropagation Learning Algorithm 8:59:31 Multi-layer Perceptron Demo 9:01:23 Data Science Interview Questions 🔴 Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV Edureka Data Science Training & Certifications 🔵 Data Science Training using Python: http://bit.ly/2P2Qbl8 🔵 Python Programming Training: http://bit.ly/2OYsVoE 🔵 Python Masters Program: https://bit.ly/3e640cY 🔵 Machine Learning Course using Python: http://bit.ly/2SApG99 🔵 Data Scientist Masters Program: http://bit.ly/39HLiWJ 🔵 Machine Learning Engineer Masters Program: http://bit.ly/38Ch2MC For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: +18338555775 (toll free). #yt:cc=on #data science #Data Science full course #data science course #data science for beginners #data science course for beginners #data science tutorial #data science training #introduction to data science #data science tutorial for beginners #data scientist #what is data science #Learn Data Science #who is a data scientist #data science with python #statistics for data science #edureka #data science edureka #python edureka #machine learning edureka #edureka data science

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/2Ze53pq Listen to the first lecture in Andrew Ng's machine learning course. This course provides a broad introduction to machine learning and statistical pattern recognition. Learn about both supervised and unsupervised learning as well as learning theory, reinforcement learning and control. Explore recent applications of machine learning and design and develop algorithms for machines. Andrew Ng is an Adjunct Professor of Computer Science at Stanford University. View more about Andrew on his website: https://www.andrewng.org/ To follow along with the course schedule and syllabus, visit: http://cs229.stanford.edu/syllabus-autumn2018.html 0:00 Introduction 05:21 Teaching team introductions 06:42 Goals for the course and the state of machine learning across research and industry 10:09 Prerequisites for the course 11:53 Homework, and a note about the Stanford honor code 16:57 Overview of the class project 25:57 Questions #AndrewNg #machinelearning #Andrew Ng #Computer Science #Stanford #Machine Learning #Graduate Course #Artificial Intelligence #AI #Stanford Online #ML

Learn the R programming language in this tutorial course. This is a hands-on overview of the statistical programming language R, one of the most important tools in data science. 💻Course Files: https://drive.google.com/drive/folders/15U8WjVKbYXaq6N6Wb_6bCr9QZ1DwCkAO 💻 Course created by Barton Poulson from datalab.cc. 🔗 Check out the datalab.cc YouTube channel: https://www.youtube.com/user/datalabcc 🔗 Watch more free data science courses at http://datalab.cc/ ⭐️ Course Contents ⭐️ ⌨️ (0:00:00) Welcome ⌨️ (0:02:20) Installing R ⌨️ (0:07:17) RStudio ⌨️ (0:11:52) Packages ⌨️ (0:19:16) plot() ⌨️ (0:27:49) Bar Charts ⌨️ (0:32:10) Histograms ⌨️ (0:39:44) Scatterplots ⌨️ (0:44:39) Overlaying Plots ⌨️ (0:52:30) summary() ⌨️ (0:55:49) describe() ⌨️ (1:00:17) Selecting Cases ⌨️ (1:06:14) Data Formats ⌨️ (1:21:39) Factors ⌨️ (1:28:34) Entering Data ⌨️ (1:34:18) Importing Data ⌨️ (1:42:29) Hierarchical Clustering ⌨️ (1:49:35) Principal Components ⌨️ (1:59:16) Regression ⌨️ (2:08:36) Next Steps -- 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 #R (Programming Language) #Data Science #Feature Engineering #Visualization #Data Exploration #R Programming #R Programming Tutorial #R Programming Training #Data Science with R #Data Scientist #Machine Learning with R #Programming #Tutorial #Training #Data Science Training #Data Science Tutorial #Machine Learning #Data Analysis #Data Visualization #Data Science with R Programming #language #tutorial #programming #r tutorial #r course #r programming course

🔥𝐄𝐝𝐮𝐫𝐞𝐤𝐚'𝐬 𝐂𝐞𝐫𝐭𝐢𝐟𝐢𝐞𝐝 𝐄𝐭𝐡𝐢𝐜𝐚𝐥 𝐇𝐚𝐜𝐤𝐞𝐫 𝐂𝐄𝐇 𝐕𝟏2 𝐂𝐨𝐮𝐫𝐬𝐞: https://www.edureka.co/ceh-ethical-hacking-certification-course (Use code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎") This Edureka Session on "CEH v12(Certified Ethical Hacking)", covers all the basic aspects of Certified Ethical Hacking Certification. It establishes the concepts like CEH Exam basics, skills required, salaries and even trends to get you up to speed with certified ethical hacking. 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