Includes Index.

1. Introduction to Machine Learning
2. Data Pre-processing
3. Supervised Learning: Regression
4. Supervised Learning: Classification
5. Unsupervised Learning: Clustering
6. Dimensionality Reduction and Feature Selection
7. Association Rule Mining
8. Artificial Neural Network
9. Reinforcement Learning
10. Project
Appendix
Bibliography

"Simplified Machine Learning" is a comprehensive guide that navigates readers through the intricate landscape of Machine Learning, offering a balanced blend of theory, algorithms, and practical applications.
The first section introduces foundational concepts such as supervised and unsupervised learning, regression, classification, clustering, and feature engineering, providing a solid base in Machine Learning theory. The second section explores algorithms like decision trees, support vector machines, and neural networks, explaining their functions, strengths, and limitations, with a special focus on deep learning, reinforcement learning, and ensemble methods. The book also covers essential topics like model evaluation, hyperparameter tuning, and model interpretability. The final section transitions from theory to practice, equipping readers with hands-on experience in deploying models, building scalable systems, and understanding ethical considerations.
By the end, readers will be able to leverage Machine Learning effectively in their respective fields, armed with practical skills and a strategic approach to problem-solving.