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AI: Machine Learning with Python
Section 1 Welcome
Lecture 1 Welcome (1:29)
Section 2 Introduction to Machine Learning
Lecture 2 Introduction (0:56)
Lecture 3 What Is Machine Learning (2:44)
Lecture 4 Types of Machine Learning (3:20)
Lecture 5 Overview (3:23)
Lecture 6 Why This Course (1:57)
Lecture 7 Installing Python and Jupyter Notebook (4:26)
Lecture 8 Python and Jupyter Notebook Demo (8:52)
Section 3 Understanding the Machine Learning Workflow
Lecture 9 Machine Learning Workflow Overview (4:24)
Section 4 How to ask questions
Lecture 10 From Question to Solution Statement (5:49)
Section 5 Preparing Your Data
Lecture 11 Introduction to Data Preparation (2:18)
Lecture 12 Getting Data (3:33)
Lecture 13 GitHub Repository (0:17)
Lecture 14 Loading, Cleaning, and Inspecting Data (6:52)
Lecture 15 Molding Data (4:48)
Section 6 Algorithm
Lecture 16 The Role of the Algorithm (2:41)
Lecture 17 Narrowing the Selection (4:10)
Lecture 18 Selecting Our Initial Algorithm (4:34)
Section 7 Training the Model
Lecture 19 Introduction to Training (1:37)
Lecture 20 The Training Process (3:55)
Lecture 21 Python Training Tools (1:39)
Lecture 22 Splitting Data and Training the Algorithm (8:45)
Section 8 Testing Your Model's Accuracy
Lecture 23 Introduction (1:20)
Lecture 24 Evaluating the Naive Bayes Model (3:41)
Lecture 25 Performance Improvement, Take 1 (2:21)
Lecture 26 Why Overfitting Is Bad (3:40)
Lecture 27 Performance Improvement, Take 2 (2:40)
Lecture 28 Understanding and Fixing Unbalanced Classes (2:11)
Lecture 29 What Is Cross Validation (4:30)
Lecture 30 Implementing and Evaluating Cross Validation (2:41)
Lecture 31 Summarizing the Evaluation (1:24)
Section 9 Outro
Lecture 32 Conclusion (3:17)
Lecture 33 Outro (0:58)
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Lecture 22 Splitting Data and Training the Algorithm
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