Includes Index.

Introduction to Machine Learning
Statistical Analysis
Linear Regression
Logistic Regression
Decision Trees
Random Forest
Rule-Based Classifiers
Naïve Bayesian Classifier
K-Nearest Neighbors Classifiers
Support Vector Machine
K-Means Clustering
Dimensionality Reduction
Association Rules Mining and FP Growth
Reinforcement Learning
Applications of ML Algorithms
Applications of Deep Learning
Advance Topics and Future Directions

Our book is divided into several useful concepts and techniques of machine learning. This book serves as a valuable resource for individuals seeking to deepen their understanding of advanced topics in this field.
Learn about various learning algorithms, including supervised, unsupervised, and reinforcement learning, and their mathematical foundations. Discover the significance of feature engineering and selection for enhancing model performance. Understand model evaluation metrics like accuracy, precision, recall, and F1-score, along with techniques like cross-validation and grid search for model selection. Explore ensemble learning methods along with deep learning, unsupervised learning, time series analysis, and reinforcement learning techniques. Lastly, uncover real-world applications of the machine and deep learning algorithms.
After reading this book, readers will gain a comprehensive understanding of machine learning fundamentals and advanced techniques. With this knowledge, readers will be equipped to tackle real-world problems, make informed decisions, and develop innovative solutions using machine and deep learning algorithms.