##### よく話題になっている単語

動画数：108件

### Machine Learning for Everybody – Full Course

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
--
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2022年09月27日

00:00:00 - 03:53:53

### Data Science Full Course - Learn Data Science in 10 Hours | Data Science For Beginners | Edureka

🔥 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
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Edureka Data Science Training & Certifications
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For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: +18338555775 (toll free).
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2019年08月18日

00:00:00 - 10:23:57

### Artificial Intelligence Full Course | Artificial Intelligence Tutorial for Beginners | Edureka

🔥 Machine Learning Engineer Masters Program (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎"): https://www.edureka.co/masters-program/machine-learning-engineer-training
This Edureka video on "Artificial Intelligence Full Course" will provide you with a comprehensive and detailed knowledge of Artificial Intelligence concepts with hands-on examples. The following topics are covered in this Artificial Intelligence Full Course:
00:00 Introduction to Artificial Intelligence Course
02:27 History Of AI
06:45 Demand For AI
08:46 What Is Artificial Intelligence?
09:50 AI Applications
16:49 Types Of AI
20:24 Programming Languages For AI
27:12 Introduction To Machine Learning
28:08 Need For Machine Learning
31:48 What Is Machine Learning?
34:13 Machine Learning Definitions
37:26 Machine Learning Process
49:13 Types Of Machine Learning
49:21 Supervised Learning
52:00 Unsupervised Learning
53:44 Reinforcement Learning
55:29 Supervised vs Unsupervised vs Reinforcement Learning
58:23 Types Of Problems Solved Using Machine Learning
1:04:49 Supervised Learning Algorithms
1:05:17 Linear Regression
1:11:20 Linear Regression Demo
1:26:36 Logistic Regression
1:35:36 Decision Tree
1:55:18 Random Forest
2:07:31 Naive Bayes
2:14:37 K Nearest Neighbour (KNN)
2:20:31 Support Vector Machine (SVM)
2:26:40 Demo (Classification Algorithms)
2:42:36 Unsupervised Learning Algorithms
2:42:45 K-means Clustering
2:50:49 Demo (Unsupervised Learning)
2:56:40 Reinforcement Learning
3:24:36 Demo (Reinforcement Learning)
3:31:41 AI vs Machine Learning vs Deep Learning
3:33:08 Limitations Of Machine Learning
3:36:32 Introduction To Deep Learning
3:38:36 How Deep Learning Works?
3:40:48 What Is Deep Learning?
3:41:50 Deep Learning Use Case
3:43:14 Single Layer Perceptron
3:50:56 Multi Layer Perceptron (ANN)
3:52:55 Backpropagation
3:54:39 Training A Neural Network
4:01:02 Limitations Of Feed Forward Network
4:03:18 Recurrent Neural Networks
4:05:36 Convolutional Neural Networks
4:09:00 Demo (Deep Learning)
4:29:02 Natural Language Processing
4:30:53 What Is Text Mining?
4:32:43 What Is NLP?
4:33:26 Applications Of NLP
4:35:53 Terminologies In NLP
4:41:19 NLP Demo
4:47:21 Machine Learning Masters Program
🔴 𝐋𝐞𝐚𝐫𝐧 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐅𝐨𝐫 𝐅𝐫𝐞𝐞! 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐂𝐡𝐚𝐧𝐧𝐞𝐥: https://edrk.in/DKQQ4Py
Python Full Course: https://www.youtube.com/watch?v=WGJJIrtnfpk
Statistics and Probability Tutorial: https://www.youtube.com/watch?v=XcLO4f1i4Yo
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🔵 Data Scientist Masters Program: http://bit.ly/2t1snGM
🔵 Machine Learning Engineer Masters Program: https://bit.ly/3Hi1sXN
🔴 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐔𝐧𝐢𝐯𝐞𝐫𝐬𝐢𝐭𝐲 𝐏𝐫𝐨𝐠𝐫𝐚𝐦
🔵 Advanced Certificate Program in Data Science with E&ICT Academy, IIT Guwahati: http://bit.ly/3V7ffrh
🔵 University of Cambridge Online Certifications: https://bit.ly/3RSNTXi
📢📢 𝐓𝐨𝐩 𝟏𝟎 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐭𝐨 𝐋𝐞𝐚𝐫𝐧 𝐢𝐧 𝟐𝟎𝟐𝟒 𝐒𝐞𝐫𝐢𝐞𝐬 📢📢
⏩ NEW Top 10 Technologies To Learn In 2024 - https://www.youtube.com/watch?v=vaLXPv0ewHU
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📌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/
-------------------------------------
About the Masters Program
Edureka’s Machine Learning Engineer Masters Program makes you proficient in techniques like Supervised Learning, Unsupervised Learning and Natural Language Processing. It includes training on the latest advancements and technical approaches in Artificial Intelligence & Machine Learning such as Deep Learning, Graphical Models and Reinforcement Learning.
The Master's Program Covers Topics LIke:
Python Programming
PySpark
HDFS
Spark SQL
Machine Learning Techniques and Artificial Intelligence Types
Tokenization
Named Entity Recognition
Lemmatization
Supervised Algorithms
Unsupervised Algorithms
Tensor Flow
Deep learning
Keras
Neural Networks
Bayesian and Markov’s Models
Inference
Decision Making
Bandit Algorithms
Bellman Equation
Policy Gradient Methods.
-------------
Please write back to us at [email protected] or call us at IND: 9606058406 / US: +18338555775 (toll-free) for more information
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2019年06月02日

00:00:00 - 04:52:51

### Machine Learning Full Course - Learn Machine Learning 10 Hours | Machine Learning Tutorial | Edureka

🔥 Machine Learning Engineer Masters Program (Use Code "𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎"): https://www.edureka.co/masters-program/machine-learning-engineer-training
This Edureka Machine Learning Full Course video will help you understand and learn Machine Learning Algorithms in detail. This Machine Learning Tutorial is ideal for both beginners as well as professionals who want to master Machine Learning Algorithms. Below are the topics covered in this Machine Learning Tutorial for Beginners video:
00:00 Introduction to Machine Learning Full Course
2:47 What is Machine Learning?
4:08 AI vs ML vs Deep Learning
5:43 How does Machine Learning works?
6:18 Types of Machine Learning
6:43 Supervised Learning
8:38 Supervised Learning Examples
11:49 Unsupervised Learning
13:54 Unsupervised Learning Examples
16:09 Reinforcement Learning
18:39 Reinforcement Learning Examples
19:34 AI vs Machine Learning vs Deep Learning
22:09 Examples of AI
23:39 Examples of Machine Learning
25:04 What is Deep Learning?
25:54 Example of Deep Learning
27:29 Machine Learning vs Deep Learning
33:49 Jupyter Notebook Tutorial
34:49 Installation
50:24 Machine Learning Tutorial
51:04 Classification Algorithm
51:39 Anomaly Detection Algorithm
52:14 Clustering Algorithm
53:34 Regression Algorithm
54:14 Demo: Iris Dataset
1:12:11 Stats & Probability for Machine Learning
1:16:16 Categories of Data
1:16:36 Qualitative Data
1:17:51 Quantitative Data
1:20:55 What is Statistics?
1:23:25 Statistics Terminologies
1:24:30 Sampling Techniques
1:27:15 Random Sampling
1:28:05 Systematic Sampling
1:28:35 Stratified Sampling
1:29:35 Types of Statistics
1:32:21 Descriptive Statistics
1:37:36 Measures of Spread
1:44:01 Information Gain & Entropy
1:56:08 Confusion Matrix
2:00:53 Probability
2:03:19 Probability Terminologies
2:04:55 Types of Events
2:05:35 Probability of Distribution
2:10:45 Types of Probability
2:11:10 Marginal Probability
2:11:40 Joint Probability
2:12:35 Conditional Probability
2:13:30 Use-Case
2:17:25 Bayes Theorem
2:23:40 Inferential Statistics
2:24:00 Point Estimation
2:26:50 Interval Estimate
2:30:10 Margin of Error
2:34:20 Hypothesis Testing
2:41:25 Supervised Learning Algorithms
2:42:40 Regression
2:44:05 Linear vs Logistic Regression
2:49:55 Understanding Linear Regression Algorithm
3:11:10 Logistic Regression Curve
3:18:34 Titanic Data Analysis
3:58:39 Decision Tree
3:58:59 what is Classification?
4:01:24 Types of Classification
4:08:35 Decision Tree
4:14:20 Decision Tree Terminologies
4:18:05 Entropy
4:44:05 Credit Risk Detection Use-case
4:51:45 Random Forest
5:00:40 Random Forest Use-Cases
5:04:29 Random Forest Algorithm
5:16:44 KNN Algorithm
5:20:09 KNN Algorithm Working
5:27:24 KNN Demo
5:35:05 Naive Bayes
5:40:55 Naive Bayes Working
5:44:25Industrial Use of Naive Bayes
5:50:25 Types of Naive Bayes
5:51:25 Steps involved in Naive Bayes
5:52:05 PIMA Diabetic Test Use Case
6:04:55 Support Vector Machine
6:10:20 Non-Linear SVM
6:12:05 SVM Use-case
6:13:30 k Means Clustering & Association Rule Mining
6:16:33 Types of Clustering
6:17:34 K-Means Clustering
6:17:59 K-Means Working
6:21:54 Pros & Cons of K-Means Clustering
6:23:44 K-Means Demo
6:28:44 Hierarchical Clustering
6:31:14 Association Rule Mining
6:34:04 Apriori Algorithm
6:39:19 Apriori Algorithm Demo
6:43:29 Reinforcement Learning
6:46:39 Reinforcement Learning: Counter-Strike Example
6:53:59 Markov's Decision Process
6:58:04 Q-Learning
7:02:39 The Bellman Equation
7:12:14 Transitioning to Q-Learning
7:17:29 Implementing Q-Learning
7:23:33 Machine Learning Projects
7:38:53 Who is a ML Engineer?
7:39:28 ML Engineer Job Trends
7:40:43 ML Engineer Salary Trends
7:42:33 ML Engineer Skills
7:44:08 ML Engineer Job Description
7:45:53 ML Engineer Resume
7:54:48 Machine Learning Interview Questions
🔴 𝐋𝐞𝐚𝐫𝐧 𝐓𝐫𝐞𝐧𝐝𝐢𝐧𝐠 𝐓𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐢𝐞𝐬 𝐅𝐨𝐫 𝐅𝐫𝐞𝐞! 𝐒𝐮𝐛𝐬𝐜𝐫𝐢𝐛𝐞 𝐭𝐨 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐘𝐨𝐮𝐓𝐮𝐛𝐞 𝐂𝐡𝐚𝐧𝐧𝐞𝐥: https://edrk.in/DKQQ4Py
Edureka Machine Learning Training
🔵 Machine Learning Course using Python: http://bit.ly/38BaJco
🔵 Machine Learning Engineer Masters Program: http://bit.ly/2UYS46r
🔵Python Masters Program: https://bit.ly/3cVibjY
🔵 Python Programming Training: http://bit.ly/38ykZCg
🔵 Data Scientist Masters Program: http://bit.ly/31ZsWOn
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📌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/
For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: +18338555775 (toll-free).
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2019年09月22日

00:00:00 - 09:38:32

### Practical Machine Learning Tutorial with Python Intro p.1

The objective of this course is to give you a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms.
In this series, we'll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks.
For each major algorithm that we cover, we will discuss the high level intuitions of the algorithms and how they are logically meant to work. Next, we'll apply the algorithms in code using real world data sets along with a module, such as with Scikit-Learn. Finally, we'll be diving into the inner workings of each of the algorithms by recreating them in code, from scratch, ourselves, including all of the math involved. This should give you a complete understanding of exactly how the algorithms work, how they can be tweaked, what advantages are, and what their disadvantages are.
In order to follow along with the series, I suggest you have at the very least a basic understanding of Python. If you do not, I suggest you at least follow the Python 3 Basics tutorial until the module installation with pip tutorial. If you have a basic understanding of Python, and the willingness to learn/ask questions, you will be able to follow along here with no issues. Most of the machine learning algorithms are actually quite simple, since they need to be in order to scale to large datasets. Math involved is typically linear algebra, but I will do my best to still explain all of the math. If you are confused/lost/curious about anything, ask in the comments section on YouTube, the community here, or by emailing me. You will also need Scikit-Learn and Pandas installed, along with others that we'll grab along the way.
Machine learning was defined in 1959 by Arthur Samuel as the "field of study that gives computers the ability to learn without being explicitly programmed." This means imbuing knowledge to machines without hard-coding it.
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2016年04月11日

00:00:00 - 00:05:55

### Machine Learning Full Course - 12 Hours | Machine Learning Roadmap [2024] | Edureka

🔥 𝐄𝐝𝐮𝐫𝐞𝐤𝐚 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐂𝐨𝐮𝐫𝐬𝐞 𝐌𝐚𝐬𝐭𝐞𝐫 𝐏𝐫𝐨𝐠𝐫𝐚𝐦 : https://www.edureka.co/masters-program/machine-learning-engineer-training (𝐔𝐬𝐞 𝐂𝐨𝐝𝐞: 𝐘𝐎𝐔𝐓𝐔𝐁𝐄𝟐𝟎)
This Edureka Machine Learning Full Course video will help you understand and learn Machine Learning Algorithms in detail. This Machine Learning Tutorial is ideal for both beginners and professionals who want to master Machine Learning Algorithms. Below are the topics covered in this Machine Learning Roadmap course:
00:00:00 Introduction to Machine Learning Full Course
00:01:08 Agenda of Machine Learning Full Course
00:02:45 What is Machine learning?
00:06:28 Supervised Machine Learning
00:11:49 Un-Supervised Machine Learning
00:16:03 Reinforcement Machine Learning
00:32:21 How to Become a Machine Learning Engineer?
00:41:53 Machine Learning Algorithm
01:03:46 Linear Regression Algorithm
01:06:40 What is Linear Regression
01:11:13 Linear Regression Use Cases
01:12:24 Use Case- How to Implement Linear Regression using Python
01:30:22 Logistic Regression Algorithm
01:35:44 Logistic Regression Use cases
02:17:36 Linear Regression Vs Logistic Regression
02:21:05 Decision Tree Algorithm
02:25:53 Types of Classification
02:34:57 What is Decision Tree?
02:58:25 What is Pruning?
02:58:36 Hands-on
03:06:42 Random Forest
03:10:46 Working of Random Forest
03:17:45 Splitting Methods
03:20:32 Advantages & Disadvantages of Random Forest
03:23:52 Hands-on Random Forest
03:35:18 KNN Algorithm
03:37:39 Features of the KNN Algorithm
03:45:54 How KNN works
03:51:21 Hands-on KNN Algorithm
04:07:45 Naive Bayes Classifier
04:29:25 Support Vector Machine
04:31:13 How do SVM work
04:55:00 K- Means Clustering Algorithm
04:58:26 K Means Clustering
05:07:16 Agglomerative Clustering
05:09:16 Division Clustering
05:09:41 Mean shift Clustering
05:18:21 Hierarchical Clustering
05:25:10 How Agglomerative Clustering Works
05:32:59 Applications of Hierarchical Clustering
05:38:34 Apriori Algorithm Explained
05:52:58 Demo
06:30:26 Linear Algebra Application
06:54:00 Probability
07:07:01 Statistics
07:12:47 Types of Statistics
07:38:40 How to select the correct predictive modeling techniques
07:50:54 ML Model Deployment with Flask on Heroku
08:28:32 Azure Machine Learning
08:54:49 AWS Machine Learning
09:35:24 Machine learning Engineer Skills
09:43:30 Machine Learning Engineer Job Trend, Salary & Resume
09:59:20 Top Machine Learning Tools & Frameworks
10:09:12 Machine Learning Roadmap
10:22:20 Machine Learning Interview Question & Answers
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2023年01月13日

00:00:00 - 11:55:00

### Scikit-Learn Course - Machine Learning in Python Tutorial

Scikit-learn is a free software machine learning library for the Python programming language. Learn about machine learning using scikit-learn in this full course.
💻 Code: https://github.com/DL-Academy/MachineLearningSKLearn
🔗 Scikit-learn website: https://scikit-learn.org
✏️ Course from DL Academy. Check out their YouTube channel: https://www.youtube.com/channel/UCTgBlZ1fmNa87NUY1xvoxpg
🔗 View more courses here: https://thedlacademy.com/
⭐️ Course Contents ⭐️
Chapter 1 - Getting Started with Machine Learning
⌨️ (0:00) Introduction
⌨️ (0:22) Installing SKlearn
⌨️ (3:37) Plot a Graph
⌨️ (7:33) Features and Labels_1
⌨️ (11:45) Save and Open a Model
Chapter 2 - Taking a look at some machine learning algorithms
⌨️ (13:47) Classification
⌨️ (17:28) Train Test Split
⌨️ (25:31) What is KNN
⌨️ (33:48) KNN Example
⌨️ (43:54) SVM Explained
⌨️ (51:11) SVM Example
⌨️ (57:46) Linear regression
⌨️ (1:07:49) Logistic vs linear regression
⌨️ (1:23:12) Kmeans and the math beind it
⌨️ (1:31:08) KMeans Example
Chapter 3 - Artificial Intelligence and the science behind It
⌨️ (1:42:02) Neural Network
⌨️ (1:56:03) Overfitting and Underfitting
⌨️ (2:03:05) Backpropagation
⌨️ (2:18:16) Cost Function and Gradient Descent
⌨️ (2:26:24) CNN
⌨️ (2:31:46) Handwritten Digits Recognizer
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2020年06月24日

00:00:00 - 02:54:25

### Lecture 7 - Kernels | Stanford CS229: Machine Learning Andrew Ng (Autumn 2018)

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3GftN16
Andrew Ng
Adjunct Professor of Computer Science
https://www.andrewng.org/
To follow along with the course schedule and syllabus, visit:
http://cs229.stanford.edu/syllabus-autumn2018.html
0:00 Introduction
0:10 Support vector machine algorithm
2:47 Derivation of this classification problem
7:47 Decision boundary
11:58 The represented theorem
13:20 Logistic Regression
26:31 The dual optimization problem
28:48 Apply kernels
28:56 Kernel trick
31:45 A kernel function
33:56 No free lunch theorem
34:40 Example of kernels
54:13 Kernel matrix
59:16 Gaussian kernel
59:39 The gaussian kernel
1:11:57 Dual form
1:13:35 Examples of SVM kernels
1:14:13 Handwritten digit classification
1:15:39 Protein sequence classifier
1:17:03 Design a feature vector
#Stanford #Stanford Online #Andrew Ng #CS229 #AI

2020年04月18日

00:00:00 - 01:20:25

### Complete Natural Language Processing (NLP) Tutorial in Python! (with examples)

In this video we go through the major concepts in natural language processing using Python libraries! We use examples to help drill down the concepts. There is content in this video for all skill levels (beginners to experts). I originally recorded this video for the PyCon Conference.
GitHub repo: https://github.com/KeithGalli/pycon2020
Patreon: https://www.patreon.com/keithgalli
YT Membership: https://www.youtube.com/c/KGMIT/membership
Some of the topics we cover:
- Bag-of-words
- Word vectors
- Stemming/Lemmatization
- Spell correction
- Transformer Architecture (Attention is all you need)
- State of the art models (OpenAI GPT, BERT)
Some of the libraries used:
- sklearn
- spaCy
- NLTK
- TextBlob
Hope you enjoy & let me know if you have any questions! Make sure to subscribe if you haven't already :).
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If you are curious to learn how I make my tutorials, check out this video: https://youtu.be/LEO4igyXbLs
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-------------------------
Video Timeline!
~~ NLP Fundamentals ~~
0:00 - Announcements!
1:12 - Video overview & timeline
3:06 - Bag of words (BOW) overview
4:42 - Bag of words example code! (sklearn | CountVectorizer, fit_transform)
11:20 - Building a text classification model using bag-of-words (SVM)
14:07 - Predicting new utterances classes using our model (transform)
16:02 - Unigram, bigram, ngrams (using consecutive words in your model)
19:28 - Word vectors overview
23:27 - Word vectors example code! (Using spaCy library)
28:10 - Building a text classification model using word vectors
34:04 - Predicting new utterances using our model
~~ Miscellaneous NLP Techniques ~~
40:42 - Regexes (pattern matching) in Python.
52:30 - Stemming/Lemmatization in Python (text normalization w/ NLTK library)
1:01:17 - Stopwords Removal (removing most common words from sentences)
1:05:56 - Various other techniques (spell correction, sentiment analysis, part-of-speech tagging).
~~ State-of-the-art Models ~~
1:12:45 - Recurrent Neural Networks (RNNs) for text classification
1:17:00 - Transformer architectures (attention is all you need)
1:21:00 - Writing Python code to leverage transformers (BERT | spacy-transformers)
1:25:00 - Writing a classification model using transformers/BERT
1:29:37 - Fine-tuning transformer models
1:31:16 - Bring it all together and build a high performance model to classify the categories of Amazon reviews!
#Keith Galli #python #programming #python 3 #data science #data analysis #python programming #NLP #machine learning #ML #AI #artificial intelligence #natural language processing #hugging face #huggingface #pytorch #spell correction #stemming #lemmatization #openai gpt #gpt-2 #BERT #transformer architecture #attention is all you need #sklearn #scikit-learn #python3 #NLP in python #text analysis #text generation #state of the art #sota #data engineering #software development #data #datasets

2022年03月17日

00:00:00 - 01:37:46

### Machine Learning Projects for Beginners (Datasets Included)

This video covers some machine learning projects for beginners. Each python machine learning project I discuss has a corresponding dataset that can be found below. As a beginner data scientist I always recommend learning the basics first and then moving into more advanced models and techniques. That is why I recommend starting with linear regression moving to K-Nearest-Neighbors, Using SVM's and finally trying Neural Networks.
Thanks to Simplilearn for Sponsoring this Video!
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My Machine Learning Tutorials: https://techwithtim.net/tutorials/machine-learning-python/
Projects & Datasets:
* Studnet Perforamce: https://archive.ics.uci.edu/ml/datasets/Student+Performance
* Car Safety: https://archive.ics.uci.edu/ml/datasets/Car+Evaluation
* Breast Cancer Dataset: https://techwithtim.net/tutorials/machine-learning-python/svm-1/
* MNIST Digit Dataset: https://techwithtim.net/tutorials/machine-learning-python/k-means-2/
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2019年09月25日

00:00:00 - 00:06:58

### Regression Features and Labels - Practical Machine Learning Tutorial with Python p.3

We'll be using the numpy module to convert data to numpy arrays, which is what Scikit-learn wants. We will talk more on preprocessing and cross_validation when we get to them in the code, but preprocessing is the module used to do some cleaning/scaling of data prior to machine learning, and cross_ alidation is used in the testing stages. Finally, we're also importing the LinearRegression algorithm as well as svm from Scikit-learn, which we'll be using as our machine learning algorithms to demonstrate results.
At this point, we've got data that we think is useful. How does the actual machine learning thing work? With supervised learning, you have features and labels. The features are the descriptive attributes, and the label is what you're attempting to predict or forecast. Another common example with regression might be to try to predict the dollar value of an insurance policy premium for someone. The company may collect your age, past driving infractions, public criminal record, and your credit score for example. The company will use past customers, taking this data, and feeding in the amount of the "ideal premium" that they think should have been given to that customer, or they will use the one they actually used if they thought it was a profitable amount.
Thus, for training the machine learning classifier, the features are customer attributes, the label is the premium associated with those attributes.
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2016年04月12日

00:00:00 - 00:10:17

### Data Science Hands-On Crash Course

Learn the basics of Data Science in the crash course. You will learn about the theory and code behind the most common algorithms used in data science.
✏️ Course created by Marco Peixeiro. Check out his channel: https://www.youtube.com/channel/UC-0lpiwlftqwC7znCcF83qg
💻 Code: https://github.com/marcopeix/datasciencewithmarco
💻 Datasets: https://github.com/marcopeix/datasciencewithmarco/tree/master/data
⭐️ Course Contents ⭐️
⌨️ (00:00) Introduction
⌨️ (03:06) Setup
⌨️ (04:29) Linear regression (theory)
⌨️ (09:29) Linear regression (Python)
⌨️ (20:59) Classification (theory)
⌨️ (30:16) Classifiaction (Python)
⌨️ (49:30) Resampling & regularization (theory)
⌨️ (56:09) Resampling and regularization (Python)
⌨️ (1:05:17) Decision trees (theory)
⌨️ (1:13:12) Decision trees (Python)
⌨️ (1:24:50) SVM (theory)
⌨️ (1:28:17) SVM (Python)
⌨️ (1:58:24) Unsupervised learning (theory)
⌨️ (2:06:54) Unsupervised learning (Python)
⌨️ (2:20:55) Conclusion
⭐️ Special thanks to our Champion supporters! ⭐️
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2020年10月08日

00:00:00 - 02:21:12

### Real-World Python Machine Learning Tutorial w/ Scikit Learn (sklearn basics, NLP, classifiers, etc)

Practice your Python Pandas data science skills with problems on StrataScratch!
https://stratascratch.com/?via=keith
In this video we walk through a real world python machine learning project using the sci-kit learn library. In it we work our way to building a model that automatically classifies text as either having a positive or negative sentiment. We do this by using amazon reviews as our training data. Full video timeline in the comments!
Link to Code & Data:
https://github.com/keithgalli/sklearn
Raw Data download:
http://jmcauley.ucsd.edu/data/amazon/
Sci-kit learn documentation:
https://scikit-learn.org/stable/documentation.html
Make sure you have sci-kit learn downloaded! To do this either run "pip install sklearn" or use python through Anaconda.
Join the Python Army to get access to perks!
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Video outline!
0:00 - What we will be doing!
3:40 - Sci-Kit Learn Overview
6:38 - How do we find training data?
9:33 - Download data
11:45 - Load our data into Jupyter Notebook
16:38 - Cleaning our code a bit (building data class)
20:13 - Using Enums
22:50 - Converting text to numerical vectors, bag of words (BOW) explanation
25:45 - Training/Test Split (make sure to "pip install sklearn" !)
33:45 - Bag of words in sklearn (CountVectorizer)
40:05 - fit_transform, fit, transform methods
42:05 - Model Selection (SVM, Decision Tree, Naive Bayes, Logistic Regression) & Classification
47:50 - predict method
53:35 - Analysis & Evaluation (using clf.score() method)
56:58 - F1 score
1:01:01 - Improving our model (evenly distributing positive & negative examples and loading in more data)
1:20:36 - Let's see our model in action! (qualitative testing)
1:22:24 - Tfidf Vectorizer
1:25:40 - GridSearchCv to automatically find the best parameters
1:31:30 - Further NLP improvement opportunities
1:32:50 - Saving our model (Pickle) and reloading it later
1:36:37 - Category Classifier
1:39:14 - Confusion Matrix
---------------------
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.
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2019年10月01日

00:00:00 - 01:40:49

### Python Machine Learning Tutorial #8 - Using Sklearn Datasets

In this machine learning python tutorial I will be introducing Support Vector Machines. This is mainly used for classification and is capable of performing classification for large dimensional data. I will also be showing you how to load datasets straight from the sklearn module.
⭐ Kite is a free AI-powered coding assistant for Python that will help you code smarter and faster. Integrates with Atom, PyCharm, VS Code, Sublime, Vim, and Spyder. I've been using Kite for 6 months and I love it! https://kite.com/download/?utm_medium=referral&utm_source=youtube&utm_campaign=techwithtim&utm_content=description-only
Text-Based Tutorial & Code: https://techwithtim.net/tutorials/machine-learning-python/svm-1/
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2019年01月24日

00:00:00 - 00:07:14

### Python Machine learning with SKLearn Tutorial for Investing - Intro

Intro to a practical example of Machine Learning with the Python programming language and the Scikit-learn, or sklearn, module. We're covering an example with investing, where we use machine learning to discern fundamental characteristics of companies that perform well over a long term period.
Windows (and 64 bit):
Scikit-learn Installers: https://pypi.python.org/pypi/scikit-learn/0.15.2#downloads
NumPy Installer: http://www.lfd.uci.edu/~gohlke/pythonlibs/#numpy
SciPy Installer: http://www.lfd.uci.edu/~gohlke/pythonlibs/#scipy
Others:
Scikit-learn .whl files for pip install: https://pypi.python.org/pypi/scikit-learn/0.15.2#downloads
NumPy - git clone http://github.com/numpy/numpy.git numpy
SciPy - git clone http://github.com/numpy/numpy.git numpy
Playlist: https://www.youtube.com/watch?v=URTZ2jKCgBc&list=PLQVvvaa0QuDd0flgGphKCej-9jp-QdzZ3&index=1
sample code: http://pythonprogramming.net
http://seaofbtc.com
http://sentdex.com
http://hkinsley.com
https://twitter.com/sentdex
Bitcoin donations: 1GV7srgR4NJx4vrk7avCmmVQQrqmv87ty6
#Scikit-learn #Machine Learning (Software Genre) #Python (Programming Language) #Tutorial (Media Genre) #svm #support vector machine #investing #Market #Stock #Trading

2014年12月23日

00:00:00 - 00:12:41

### Python Machine Learning Tutorial #10 - SVM P.3 - Implementing a SVM

This python machine learning tutorial covers implementing a support vector machine to classify data.
⭐ Kite is a free AI-powered coding assistant for Python that will help you code smarter and faster. Integrates with Atom, PyCharm, VS Code, Sublime, Vim, and Spyder. I've been using Kite for 6 months and I love it! https://kite.com/download/?utm_medium=referral&utm_source=youtube&utm_campaign=techwithtim&utm_content=description-only
Text-Based Tutorial & Code Here: https://techwithtim.net/tutorials/machine-learning-python/svm-p-3-implementation/
SVC Docs: https://scikit-learn.org/stable/modules/generated/sklearn.svm.SVC.html
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2019年01月26日

00:00:00 - 00:09:58

### Python Machine Learning Tutorial #9 - SVM P.2 - How Support Vector Machines Work

In this machine learning python tutorial explain how a support vector machine works. SVM works by creating a hyperplane that divides the test data into its classes. I then look at which side of the hyperplane a test data point is on and classifies it. ⭐ Kite is a free AI-powered coding assistant for Python that will help you code smarter and faster. Integrates with Atom, PyCharm, VS Code, Sublime, Vim, and Spyder. I've been using Kite for 6 months and I love it! https://kite.com/download/?utm_medium=referral&utm_source=youtube&utm_campaign=techwithtim&utm_content=description-only
Text-Based Tutorial & Code: https://techwithtim.net/tutorials/machine-learning-python/svm-2/
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2019年01月25日

00:00:00 - 00:14:21

### Statistical Learning: 9.4 Example and Comparison with Logistic Regression

Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing
Trevor Hastie, Professor of Statistics and Biomedical Data Sciences at Stanford University - https://statistics.stanford.edu/people/trevor-j-hastie
Robert Tibshirani, Professor of Statistics and Biomedical Data Sciences at Stanford University - https://statistics.stanford.edu/people/robert-tibshirani
Jonathan Taylor, Professor Statistics at Stanford University - https://statistics.stanford.edu/people/jonathan-taylor
You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion. You can choose to take the course in R (https://www.edx.org/course/statistica) or in Python (https://www.edx.org/learn/data-analysis-statistics/stanford-university-statistical-learning-with-python)
For more information about courses on Statistics, you can browse our Stanford Online Catalog: https://stanford.io/3QHRi72
0:00 Example: Heart Data
5:17 Example continued: Heart Test Data
6:04 SVMs: more than 2 classes?
8:14 Support Vector versus Logistic Regression?
12:00 Which to use: SVM or Logistic Regression
#Stanford #Stanford Online

2022年10月08日

00:00:00 - 00:14:48

### Statistical Learning: 9.3 Feature Expansion and the SVM

Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing
Trevor Hastie, Professor of Statistics and Biomedical Data Sciences at Stanford University - https://statistics.stanford.edu/people/trevor-j-hastie
Robert Tibshirani, Professor of Statistics and Biomedical Data Sciences at Stanford University - https://statistics.stanford.edu/people/robert-tibshirani
Jonathan Taylor, Professor Statistics at Stanford University - https://statistics.stanford.edu/people/jonathan-taylor
You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion. You can choose to take the course in R (https://www.edx.org/course/statistica) or in Python (https://www.edx.org/learn/data-analysis-statistics/stanford-university-statistical-learning-with-python)
For more information about courses on Statistics, you can browse our Stanford Online Catalog: https://stanford.io/3QHRi72
0:00 Introduction
0:11 Feature Expansion
2:05 Cubic Polynomials
3:28 Nonlinearities and Kernels
4:16 Inner products and support vectors
6:43 Support Vector Classifier
9:10 Kernels and Support Vector Machines
11:59 Radial Kernel
#Stanford #Stanford Online

2022年10月08日

00:00:00 - 00:15:05

### Creating an SVM from scratch - Practical Machine Learning Tutorial with Python p.25

Welcome to the 25th part of our machine learning tutorial series and the next part in our Support Vector Machine section. In this tutorial, we're going to begin setting up or own SVM from scratch.
Before we dive in, however, I will draw your attention to a few other options for solving this constraint optimization problem:
First, the topic of constraint optimization is massive, and there is quite a bit of material on the subject. Even just our subsection: Convex Optimization, is massive. A starting place might be: https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf. For a starting place for constraint optimization in general, you could also check out http://www.mit.edu/~dimitrib/Constrained-Opt.pdf
Within the realm of Python specifically, the CVXOPT package has various convex optimization methods available, one of which is the quadratic programming problem we have (found @ cvxopt.solvers.qp).
Also, even more specifically there is libsvm's Python interface, or the libsvm package in general. We are opting to not make use of any of these, as the optimization problem for the Support Vector Machine IS basically the entire SVM problem.
Now, to begin our SVM in Python.
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#support vector machine #svm #machine learning #python #classification #artificial intelligence #tutorial

2016年05月24日

00:00:00 - 00:10:16

### Soft Margin SVM and Kernels with CVXOPT - Practical Machine Learning Tutorial with Python p.32

In this tutorial, we cover the Soft Margin SVM, along with Kernels and quadratic programming with CVXOPT all in one quick tutorial using some example code from:
http://www.mblondel.org/journal/2010/09/19/support-vector-machines-in-python/
Visualizing the conversion of many dimensions back to 2D: https://www.youtube.com/watch?v=3liCbRZPrZA
Quadratic programming with CVXOPT: http://cvxopt.org/userguide/coneprog.html#quadratic-programming
Docs qp example: http://cvxopt.org/examples/tutorial/qp.html
Another CVXOPT tutorial: https://courses.csail.mit.edu/6.867/wiki/images/a/a7/Qp-cvxopt.pdf
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#cvxopt #python #machine learning #svm #support vector machine #kernel #soft margin svm #classification #quadratic programming #artificial intelligence #scikit-learn #sklearn #tensorflow

2016年06月01日

00:00:00 - 00:11:11

### Stanford CS229: Machine Learning | Summer 2019 | Lecture 8 - Kernel Methods & Support Vector Machine

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/3DYVYzo
Anand Avati
Computer Science, PhD
To follow along with the course schedule and syllabus, visit:
http://cs229.stanford.edu/syllabus-summer2019.html
#Anand Avati #Machine Learning #CS229 #Stanford #Kernel Methods #Support Vector Machine #SVM

2021年04月14日

00:00:00 - 01:55:09

### Support Vector Machine Tutorial Using R | SVM Algorithm Explained | Data Science Training | Edureka

** Data Science Certification using R: https://www.edureka.co/data-science-r-programming-certification-course **
This session is dedicated to how SVM works, the various features of SVM and how it used in the real world. The following topics will be covered today:
(01:15) Introduction to machine learning
((04:15) What is Support Vector Machine (SVM)?
(06:19) How does SVM work?
(09:35) Non-linear SVM
(11:20) SVM Use case
(12:43) Hands-On
Blog Series: http://bit.ly/data-science-blogs
Data Science Training Playlist: http://bit.ly/data-science-playlist
- - - - - - - - - - - - - - - - -
Subscribe to our channel to get video updates. Hit the subscribe button above: https://goo.gl/6ohpTV
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#svmalgorithm #svmwithr #svmclassifier #datascience #datasciencetutorial #datasciencewithr #datasciencecourse #datascienceforbeginners #datasciencetraining #datasciencetutorial
- - - - - - - - - - - - - - - - -
About the Course
Edureka's Data Science course will cover the whole data lifecycle ranging from Data Acquisition and Data Storage using R-Hadoop concepts, Applying modeling through R programming using Machine learning algorithms and illustrate impeccable Data Visualization by leveraging on 'R' capabilities.
- - - - - - - - - - - - - -
Why Learn Data Science?
Data Science training certifies you with ‘in demand’ Big Data Technologies to help you grab the top paying Data Science job title with Big Data skills and expertise in R programming, Machine Learning and Hadoop framework.
After the completion of the Data Science course, you should be able to:
1. Gain insight into the 'Roles' played by a Data Scientist
2. Analyze Big Data using R, Hadoop and Machine Learning
3. Understand the Data Analysis Life Cycle
4. Work with different data formats like XML, CSV and SAS, SPSS, etc.
5. Learn tools and techniques for data transformation
6. Understand Data Mining techniques and their implementation
7. Analyze data using machine learning algorithms in R
8. Work with Hadoop Mappers and Reducers to analyze data
9. Implement various Machine Learning Algorithms in Apache Mahout
10. Gain insight into data visualization and optimization techniques
11. Explore the parallel processing feature in R
- - - - - - - - - - - - - -
Who should go for this course?
The course is designed for all those who want to learn machine learning techniques with implementation in R language, and wish to apply these techniques on Big Data. The following professionals can go for this course:
1. Developers aspiring to be a 'Data Scientist'
2. Analytics Managers who are leading a team of analysts
3. SAS/SPSS Professionals looking to gain understanding in Big Data Analytics
4. Business Analysts who want to understand Machine Learning (ML) Techniques
5. Information Architects who want to gain expertise in Predictive Analytics
6. 'R' professionals who want to captivate and analyze Big Data
7. Hadoop Professionals who want to learn R and ML techniques
8. Analysts wanting to understand Data Science methodologies.
For online Data Science training, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll-free) for more information.
#yt:cc=on #support vector machine tutorial in r #svm tutorial using r #what is support vector machine #support vector machine object detection #working of support vector machine #svm using r #svm using r studio #support vector machine nonlinear classification #support vector machine non separable #support vector machine #support vector machine objective function #support vector machine in r example #machine learning tutorial #data science training #edureka #svm algorithm

2019年01月04日

00:00:00 - 00:30:15

### SVM Training - Practical Machine Learning Tutorial with Python p.26

In this support vector machine from scratch video, we talk about the training/optimization problem.
Additional Resources:
Convex Optimization Book: https://web.stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf
Sequential Minimal Optimization book: http://research.microsoft.com/pubs/68391/smo-book.pdf
More SMO: http://research.microsoft.com/pubs/69644/tr-98-14.pdf
CVXOPT (Convex Optimization Module for Python): http://cvxopt.org/
https://pythonprogramming.net
https://twitter.com/sentdex
https://www.facebook.com/pythonprogramming.net/
https://plus.google.com/+sentdex
#training #support vector machine #svm #machine learning #python #classification #artificial intelligence #tutorial

2016年05月24日

00:00:00 - 00:14:23

### Data Science Project- Predicting outcome with Support Vector Machine in Machine Learning | Edureka

🔥Python Certification Training: https://www.edureka.co/data-science-python-certification-course
This Edureka Live on Data Science Project-2 will help you understand how we can use the machine learning classifiers to predict outcomes based on data-driven insights.
🔹Python Tutorial Playlist: https://goo.gl/WsBpKe
🔹Blog Series: http://bit.ly/2sqmP4s
#PythonEdureka #Edureka #datascienceproject #pythonprojects #pythonprogramming #pythontutorial #PythonTraining
To subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV
Instagram: https://www.instagram.com/edureka_learning/
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LinkedIn: https://www.linkedin.com/company/edureka
Telegram: https://t.me/edurekaupdates
SlideShare: https://www.slideshare.net/EdurekaIN
Meetup: https://www.meetup.com/edureka/
--------------------------------------------------------------------------------------------------------------
How it Works?
1. This is a 5 Week Instructor-led Online Course,40 hours of assignment and 20 hours of project work
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training, you will be working on a real-time project for which we will provide you a Grade and a Verifiable Certificate!
---------------------------------
About the course
Python has been one of the premier, flexible, and powerful open-source language that is easy to learn, easy to use, and has powerful libraries for data manipulation and analysis. For over a decade, Python has been used in scientific computing and highly quantitative domains such as finance, oil and gas, physics, and signal processing.
Edureka's Python Certification Training not only focuses on the fundamentals of Python, Statistics and Machine Learning but also helps one gain expertise in applied Data Science at scale using Python. The training is a step by step guide to Python and Data Science with extensive hands-on. The course is packed with several activity problems and assignments and scenarios that help you gain practical experience in addressing predictive modeling problems that would either require Machine Learning using Python. Starting from basics of Statistics such as mean, median and mode to exploring features such as Data Analysis, Regression, Classification, Clustering, Naive Bayes, Cross-Validation, Label Encoding, Random Forests, Decision Trees and Support Vector Machines with a supporting example and exercise help you get into the weeds.
Furthermore, you will be taught of Reinforcement Learning which in turn is an important aspect of Artificial Intelligence. You will be able to train your machine based on real-life scenarios using Machine Learning Algorithms.
Edureka’s Python course will also cover both basic and advanced concepts of Python like writing Python scripts, sequence and file operations in Python. You will use libraries like pandas, numpy, matplotlib, scikit, and master concepts like Python machine learning, scripts, and sequence.
Why learn Python?
It's continued to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging programs is a breeze in Python with its built-in debugger.
It runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for commercial products, because of its OSI-approved open source license.
It has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that it is the " Next Big Thing " and a must for Professionals in the Data Analytics domain.
Who Should Go For This Course?
Programmers, Developers, Technical Leads, Architects
Developers aspiring to be a ‘Machine Learning Engineer'
Analytics Managers who are leading a team of analysts
Business Analysts who want to understand Machine Learning (ML) Techniques
Information Architects who want to gain expertise in Predictive Analytics
'Python' professionals who want to design automatic predictive models
For more information, please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775
#predictive analysis in python #machine learning #model building in data science #data science #data science with python #python for data science #data science projects #BMI Data #BMI data analysis #SVM in python #seaborn in python #data science life cycle #data science projects for beginners #learn data science #data science use case #seaborn use case #python programming #Python basics #python for beginners #python tutorial #python edureka #edureka

2020年04月03日

00:00:00 - 00:30:03

### Classification w/ K Nearest Neighbors Intro - Practical Machine Learning Tutorial with Python p.13

We begin a new section now: Classification. In covering classification, we're going to cover two major classificiation algorithms: K Nearest Neighbors and the Support Vector Machine (SVM). While these two algorithms are both classification algorithms, they acheive results in different ways.
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#k nearest neighbors #nearest neighbors #machine learning #artificial intelligence #tutorial #sklearn #scikit-learn #tensorflow #theano #python

2016年04月29日

00:00:00 - 00:11:11

### サポートベクターマシン (2)

滋賀大学MOOC「大学生のためのデータサイエンス（III）」より
#数理 #データサイエンス #AI

2021年07月13日

00:00:00 - 00:12:30

### Support Vector Machine In Python | Machine Learning in Python Tutorial | Python Training | Edureka

** Python Certification Training: https://www.edureka.co/machine-learning-certification-training **
This Edureka video on 'Support Vector Machine In Python' covers A brief introduction to Support Vector Machine in Python with a use case to implement SVM using Python. Following are the topics discussed:
Introduction To Machine learning
What is Support Vector Machine?
How Does SVM Work?
SVM Kernels
SVM Use Cases
How To Implement SVM?
Character Recognition Using SVM
Python Tutorial Playlist: https://goo.gl/WsBpKe
Blog Series: http://bit.ly/2sqmP4s
#Edureka #EdurekaSVM #Supportvectormachineinpython #pythonprojects #pythonprogramming #pythontutorial #PythonTraining #PythonEdureka
Do subscribe to our channel and hit the bell icon to never miss an update from us in the future: https://goo.gl/6ohpTV
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
-----------------------------------------------------------------------------------------------------------------------------------
How it Works?
1. This is a 5 Week Instructor-led Online Course,40 hours of assignment and 20 hours of project work
2. We have a 24x7 One-on-One LIVE Technical Support to help you with any problems you might face or any clarifications you may require during the course.
3. At the end of the training, you will be working on a real-time project for which we will provide you a Grade and a Verifiable Certificate!
- - - - - - - - - - - - - - - - -
About the Course
Edureka's Python Online Certification Training will make you an expert in Python programming. It will also help you learn Python the Big data way with integration of Machine learning, Pig, Hive and Web Scraping through beautiful soup. During our Python Certification training, our instructors will help you:
1. Master the Basic and Advanced Concepts of Python
2. Understand Python Scripts on UNIX/Windows, Python Editors and IDEs
3. Master the Concepts of Sequences and File operations
4. Learn how to use and create functions, sorting different elements, Lambda function, error handling techniques and Regular expressions ans using modules in Python
5. Gain expertise in machine learning using Python and build a Real Life Machine Learning application
6. Understand the supervised and unsupervised learning and concepts of Scikit-Learn
7. Master the concepts of MapReduce in Hadoop
8. Learn to write Complex MapReduce programs
9. Understand what is PIG and HIVE, Streaming feature in Hadoop, MapReduce job running with Python
10. Implementing a PIG UDF in Python, Writing a HIVE UDF in Python, Pydoop and/Or MRjob Basics
11. Master the concepts of Web scraping in Python
12. Work on a Real Life Project on Big Data Analytics using Python and gain Hands on Project Experience
- - - - - - - - - - - - - - - - - - -
Why learn Python?
Programmers love Python because of how fast and easy it is to use. Python cuts development time in half with its simple to read syntax and easy compilation feature. Debugging your programs is a breeze in Python with its built in debugger. Using Python makes Programmers more productive and their programs ultimately better. Python continues to be a favorite option for data scientists who use it for building and using Machine learning applications and other scientific computations.
Python runs on Windows, Linux/Unix, Mac OS and has been ported to Java and .NET virtual machines. Python is free to use, even for the commercial products, because of its OSI-approved open source license.
Python has evolved as the most preferred Language for Data Analytics and the increasing search trends on python also indicates that Python is the next "Big Thing" and a must for Professionals in the Data Analytics domain.
Who should go for python?
Edureka’s Data Science certification course in Python is a good fit for the below professionals:
· Programmers, Developers, Technical Leads, Architects
· Developers aspiring to be a ‘Machine Learning Engineer'
· Analytics Managers who are leading a team of analysts
· Business Analysts who want to understand Machine Learning (ML) Techniques
· Information Architects who want to gain expertise in Predictive Analytics
· 'Python' professionals who want to design automatic predictive models
For more information, Please write back to us at [email protected] or call us at IND: 9606058406 / US: 18338555775 (toll free)
#yt:cc=on #support vector machine #classification algorithms #SVM #SVM kernels #support vector machine use cases #Support vector machine implementation #Support Vector Machine using python #machine learning #classification in machine learning #cancer dataset #character recognition using SVM in python #character recognition using support vector machine #sci-kit learn #SVM using sci-kit learn #SVM algorithm #how does SVM work? #linear kernel #polynomial kernel #edureka

2019年12月06日

00:00:00 - 00:15:06

### Understanding Vectors - Practical Machine Learning Tutorial with Python p.21

In this tutorial, we cover some basics on vectors, as they are essential with the Support Vector Machine.
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#support vector machine #svm #machine learning #vectors #dot product #inner product #theano #tensorflow

2016年05月17日

00:00:00 - 00:06:07

### 【機械学習】サポートベクトルマシン（前編）| SVMの理論、ハードマージンとソフトマージン

← 第６回 ロジスティック回帰（後編）
https://youtu.be/KXE8fTlF44s
→第８回 サポートベクトルマシン（中編）
https://youtu.be/2IB7vkfGeAA
ご視聴ありがとうございます。
私は普段、AIエンジニア/データサイエンティストとして活動しています。このチャンネルでは、たくさんの人にAIの可能性を知っていただくことや、日々の学習の成果を視聴者の皆様とシェアしていくことを目標にしています。
この動画シリーズでは「機械学習をはじめよう」と題して、機械学習の基礎的な理論や実装の方法を解説していきます。
動画の内容を参考にして、ぜひ皆様も機械学習に挑戦してみてください！！
ーこの分野についてもっと詳しく学ぶならー
『パターン認識と機械学習 上』
https://amzn.to/2vSj7Ti
『パターン認識と機械学習 下』
https://amzn.to/2OI8cmm
『統計的学習の基礎 ―データマイニング・推論・予測―』
https://amzn.to/2MEXwHX
『Pythonではじめる機械学習 ―scikit-learnで学ぶ特徴量エンジニアリングと機械学習の基礎』
https://amzn.to/2nKQJ19
『Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning)』
https://amzn.to/2Mx9nYf
#機械学習 #AI #machine learning #人工知能 #Python #データ分析 #データサイエンス #データサイエンティスト #サポートベクトルマシン #サポートベクターマシン #SVM #ハードマージン #ソフトマージン

2018年08月23日

00:00:00 - 00:31:24