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.
Key Features
● Basic understanding of machine learning algorithms via MATLAB, R, and Python.
● Inclusion of examples related to real-world problems, case studies, and questions related to futuristic technologies.
● Adding futuristic technologies related to machine learning and deep learning.
What you will learn
● Ability to tackle complex machine learning problems.
● Understanding of foundations, algorithms, ethical issues, and how to implement each learning algorithm for their own use/ with their data.
● Efficient data analysis for real-time data will be understood by researchers/ students.
● Using data analysis in near future topics and cutting-edge technologies.
Who this book is for
This book is ideal for students, professors, and researchers. It equips industry experts and academics with the technical know-how and practical implementations of machine learning algorithms.
Table of Contents
1. Introduction to Machine Learning
2. Statistical Analysis
3. Linear Regression
4. Logistic Regression
5. Decision Trees
6. Random Forest
7. Rule-Based Classifiers
8. Naïve Bayesian Classifier
9. K-Nearest Neighbors Classifiers
10. Support Vector Machine
11. K-Means Clustering
12. Dimensionality Reduction
13. Association Rules Mining and FP Growth
14. Reinforcement Learning
15. Applications of ML Algorithms
16. Applications of Deep Learning
17. Advance Topics and Future Directions