
What you would learn in Machine Learning with Python for Everyone Part 1: Learning Foundations, 2nd Edition course?
Machine Learning with Python for Everyone, Part 1: Learning Foundations, 2nd Edition
Course Content:
- Introduction
- Machine Learning with Python for Everyone: Introduction
- Lesson 1: Software Background
- Topics
- 1.1 What Is Machine Learning?
- 1.2 Building Learning Systems
- 1.3 Environment Installation
- 1.4 Three Things You Can do with NumPy and matplotlib
- 1.5 Three Things You Can do with Pandas
- 1.6 Three Things You Can do with scikit-learn and Friends
- 1.7 Getting Help
- Lesson 2: Mathematical Background
- Topics
- 2.1 Probability
- 2.2 Distributions
- 2.3 Linear Combinations
- 2.4 Geometry, Part 1
- 2.5 Geometry, Part 2
- 2.6 Geometry, Part 3
- 2.7 When Computers and Math Meet
- Lesson 3: Beginning Classification (Part I)
- Topics
- 3.1 Setup and the Iris Dataset
- 3.2 Classification, Accuracy, and Splitting
- 3.3 Accuracy
- 3.4 Introduction to Nearest Neighbors and Naive Bayes
- 3.5 k-Nearest Neighbors
- 3.6 Train-Test Split and Nearest Neighbors (Part 1)
- 3.7 Train-Test Split and Nearest Neighbors (Part 2)
- 3.8 Naive Bayes
- Lesson 4: Beginning Classification (Part II)
- Topics
- 4.1 Learning Evaluation, Part 1
- 4.2 Learning Evaluation, Part 2
- 4.3 Resource Evaluation: Time
- 4.4 Resource Evaluation: Memory
- 4.5 Scripts
- Lesson 5: Beginning Regression (Part I)
- Topics
- 5.1 Setup and the Diabetes Dataset
- 5.2 Introducing Regression
- 5.3 Measures of Center
- 5.4 k-Nearest Neighbors for Regression
- 5.5 Introducing Linear Regression and NN Regression
- 5.6 Linear Regression, Part 1
- 5.7 Linear Regression, Part 2
- Lesson 6: Beginning Regression (Part II)
- Topics
- 6.1 Optimization, Part 1
- 6.2 Optimization, Part 2
- 6.3 Optimization, Part 3
- 6.4 Learning Performance
- 6.5 Resource Evaluation
- Summary
- Machine Learning with Python for Everyone: Summary
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