Python Book 6 - Python for Machine Learning for Absolute Beginners/ Scikit-learn and TensorFlow

Table of Contents

1. Introduction

1.1 What is Machine Learning?

1.2 Why Python for Machine Learning?

1.3 What are Scikit-learn and TensorFlow?

1.4 How to Install and Use Scikit-learn and TensorFlow?

1.5 How to Follow this Book?

2. Basic Concepts of Machine Learning

2.1 Data and Features

2.2 Supervised and Unsupervised Learning

2.3 Classification and Regression

2.4 Training and Testing

2.5 Evaluation and Validation

2.6 Bias and Variance

2.7 Overfitting and Underfitting

3. Linear Models for Regression and Classification

3.1 Linear Regression

3.2 Logistic Regression

3.3 Regularization

3.4 Polynomial Features

3.5 Hands-on Example: Predicting House Prices

4. Support Vector Machines

4.1 What are Support Vector Machines?

4.2 Linear and Non-linear SVMs

4.3 Kernel Trick and Kernel Functions

4.4 Hyperparameters and Grid Search

4.5 Hands-on Example: Classifying Handwritten Digits

5. Decision Trees and Random Forests

5.1 What are Decision Trees?

5.2 How to Build and Prune a Decision Tree?

5.3 How to Visualize and Interpret a Decision Tree?

5.4 What are Random Forests?

5.5 How to Train and Tune a Random Forest?

5.6 Hands-on Example: Detecting Spam Emails

6. K-Means Clustering

6.1 What is Clustering?

6.2 How does K-Means Clustering Work?

6.3 How to Choose the Number of Clusters?

6.4 How to Evaluate the Quality of Clustering?

6.5 Hands-on Example: Segmenting Customers

7. Principal Component Analysis

7.1 What is Dimensionality Reduction?

7.2 How does Principal Component Analysis Work?

7.3 How to Perform PCA using Scikit-learn?

7.4 How to Interpret and Visualize the PCA Results?

7.5 Hands-on Example: Reducing the Dimensionality of Iris Dataset 8. Neural Networks and Deep Learning

8.1 What are Neural Networks?

8.2 How to Build and Train a Neural Network using TensorFlow? 8.3 What are Convolutional Neural Networks?

8.4 How to Build and Train a CNN using TensorFlow?

8.5 Hands-on Example: Recognizing Facial Expressions

9. Natural Language Processing

9.1 What is Natural Language Processing?

9.2 How to Preprocess Text Data using Scikit-learn?

9.3 How to Extract Features from Text Data using Scikit-learn? 9.4 How to Perform Sentiment Analysis using TensorFlow? 9.5 Hands-on Example: Analyzing Movie Reviews

10. Conclusion

10.1 Summary of the Main Concepts and Techniques

10.2 Further Resources and References

10.3 Final Project: Building a Machine Learning Pipeline