Machine Learning with Python for Everyone Part 1 Learning Foundations, 2nd Edition

Genre: eLearning | Language: English | Duration: 5h 54m | Size: 1.65 GB
Table of contents
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
https://www.oreilly.com/library/view/machine-learning-with/9780137932962/