ML Book 2 - Supervised Learning Techniques with Python for Novices

1. Introduction

1.1 What is Supervised Learning?

1.2 Types of Supervised Learning Problems

1.3 Applications of Supervised Learning

1.4 Challenges and Limitations of Supervised Learning

1.5 Summary and Key Points

2. Python Basics for Data Science

2.1 Installing Python and Required Libraries

2.2 Data Structures and Operations in Python

2.3 Working with Data Files and APIs in Python

2.4 Data Visualization with Python

2.5 Summary and Key Points

3. Data Preprocessing and Feature Engineering

3.1 Exploratory Data Analysis

3.2 Handling Missing Values and Outliers

3.3 Encoding Categorical Variables

3.4 Scaling and Normalizing Numerical Variables

3.5 Feature Selection and Dimensionality Reduction

3.6 Summary and Key Points

4. Regression Analysis

4.1 Linear Regression

4.2 Polynomial Regression

4.3 Regularization Techniques

4.4 Evaluation Metrics for Regression

4.5 Summary and Key Points

5. Classification Analysis

5.1 Logistic Regression

5.2 K-Nearest Neighbors

5.3 Decision Trees and Random Forests

5.4 Support Vector Machines

5.5 Evaluation Metrics for Classification

5.6 Summary and Key Points

6. Neural Networks and Deep Learning

6.1 Introduction to Neural Networks

6.2 Building and Training Neural Networks with TensorFlow and Keras

6.3 Convolutional Neural Networks for Image Classification

6.4 Recurrent Neural Networks for Sequence Modeling

6.5 Summary and Key Points

7. Conclusion

7.1 Review of the Main Concepts and Techniques

7.2 Future Trends and Directions of Supervised Learning

7.3 Resources and References for Further Learning

7.4 Summary and Key Points