Python Machine Learning A Beginner's Guide to Scikit-Learn: A Hands-On Approach

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What are the requirements? (Pre-requisites)
Preface
Why Should You Read This Book?
Python Machine Learning: A Beginner's Guide to Scikit-learn
1 Introduction to Machine Learning
1.1 Background on machine learning
1.2 Why Python for Machine Learning
1.3 Overview of scikit-learn
1.4 Setting up the development environment
1.5 Understanding the dataset
1.6 Type of Data
1.7 Types of machine learning models
1.8 Summary
1.9 Test Your Knowledge
1.10 Answers
2 Python: A Beginner's Overview
2.1 Python Basics
2.2 Data Types in Python
2.3 Control Flow in Python
2.4 Functio in Python
2.5 Anonymous (Lambda) Function
2.6 Function for List
2.7 Function for Dictionary
2.8 String Manipulation Function
2.9 Exception Handling
2.10 File Handling in Python
2.11 Modlues in Python
2.12 Style Guide for Python Code
2.13 Docstring Conventions in python
2.14 Python library for Data Science
2.15 Summary
2.16 Test Your Knowledge
2.17 Answers
3 Data Preparation
3.1 Importing data
3.2 Cleaning data
3.3 Exploratory data analysis
3.4 Feature engineering
3.5 Splitting the data into training and testing sets
3.6 Summary
3.7 Test Your Knowledge
3.8 Answers
4 Supervised Learning
4.1 Linear regression
4.2 Logistic Regression
4.3 Decision Trees
4.4 Random Forests
4.5 Confusion Matrix
4.6 Support Vector Machines
4.7 Summary
4.8 Test Your Knowledge
4.9 Answers
5 Unsupervised Learning
5.1 Clustering
5.2 K-Means Clustering
5.3 Hierarchical Clustering
5.4 DBSCAN
5.5 GMM (Gaussian Mixture Model)
5.6 Dimensionality Reduction
5.7 Principal Component Analysis (PCA)
5.8 Independent Component Analysis (ICA)
5.9 t-SNE
5.10 Autoencoders
5.11 Anomaly Detection
5.12 Summary
5.13 Test Your Knowledge
5.14 Answers
6 Deep Learning
6.1 What is Deep Learning
6.2 Neural Networks
6.3 Backpropagation
6.4 Convolutional Neural Networks
6.5 Recurrent Neural Networks
6.6 Generative Models
6.7 Transfer Learning
6.8 Tools and Frameworks for Deep Learning
6.9 Best Practices and Tips for Deep Learning
6.10 Summary
6.11 Test Your Knowledge
6.12 Answers
7 Model Selection and Evaluation
7.1 Model selection and evaluation techniques
7.2 Understanding the Bias-Variance trade-off
7.3 Overfitting and Underfitting
7.4 Splitting the data into training and testing sets
7.5 Hyperparameter Tuning
7.6 Model Interpretability
7.7 Feature Importance Analysis
7.8 Model Visualization
7.9 Simplifying the Model
7.10 Model-Agnostic Interpretability
7.11 Model Comparison
7.12 Learning Curves
7.13 Receiver Operating Characteristic (ROC) Curves
7.14 Precision-Recall Curves
7.15 Model persistence
7.16 Summary
7.17 Test Your Knowledge
7.18 Answers
8 The Power of Combining: Ensemble Learning Methods
8.1 Types of Ensemble Learning Methods
8.2 Bagging (Bootstrap Aggregating)
8.3 Boosting: Adapting the Weak to the Strong
8.4 Stacking: Building a Powerful Meta Model
8.5 Blending
8.6 Rotation Forest
8.7 Cascading Classifiers
8.8 Adversarial Training
8.9 Voting Classifier
8.10 Summary
8.11 Test Your Knowledge
8.12 Practical Exercise
8.13 Answers
8.14 Exercise Solutions
9 Real-World Applications of Machine Learning
9.1 Natural Language Processing
9.2 Computer Vision
9.3 Recommender Systems
9.4 Time series forecasting
9.5 Predictive Maintenance
9.6 Speech Recognition
9.7 Robotics and Automation
9.8 Autonomous Driving
9.9 Fraud Detection
9.10 Other Real-Life applications
9.11 Summary
9.12 Test Your Knowledge
9.13 Answers
A. Future Directions in Python Machine Learning
B. Additional Resources
Websites & Blogs
Online Courses and Tutorials
Conferences and Meetups
Communities and Support Groups
Podcasts
Research Papers
C. Tools and Frameworks
D. Datasets
Open-Source Datasets
E. Career Resources
Companies and Startups working in the field of Machine Learning
Research Labs and Universities with a focus on Machine Learning
Government Organizations and Funding Agencies supporting ML Research and Development
F. Glossary