Beginning with Machine Learning

Should I choose supervised learning or reinforcement learning? Which algorithm is best suited for my application? How does deep learning advance the capacities of problem-solving? If you have found yourself asking these questions, this book is specially developed for you.

The book will help readers understand the core concepts of machine learning and techniques to evaluate any machine learning model with ease. The book starts with the importance of machine learning by analyzing its impact on the global landscape. The book also covers Supervised and Unsupervised ML along with Reinforcement Learning. In subsequent chapters, the book explores these topics in even greater depth, evaluating the pros and cons of each and exploring important topics such as Bias-Variance Tradeoff, Clustering, and Dimensionality Reduction. The book also explains model evaluation techniques such as Cross-Validation and GridSearchCV. The book also features mind maps which help enhance the learning process by making it easier to learn and retain information.

This book is a one-stop solution for covering basic ML concepts in detail and the perfect stepping stone to becoming an expert in ML and deep learning and even applying them to different professions.

KEY FEATURES

* Understand different types of Machine Learning like Supervised, Unsupervised, Semi-supervised, and Reinforcement learning.
* Learn how to implement Machine Learning algorithms effectively and efficiently.
* Get familiar with the various libraries & tools for Machine Learning.

WHAT YOU WILL LEARN

* Understand important concepts to fully grasp the idea of supervised learning.
* Get familiar with the basics of unsupervised learning and some of its algorithms.
* Learn how to analyze the performance of your Machine Learning models.
* Explore the different methodologies of Reinforcement Learning.
* Learn how to implement different types of Neural networks.

WHO THIS BOOK IS FOR

This book is aimed at those who are new to machine learning and deep learning or want to extend their ML knowledge. Anyone looking to apply ML to data in their profession will benefit greatly from this book.

1. Introduction to Machine Learning
2. Supervised Learning
3. Unsupervised Learning
4. Model Evaluation
5. Reinforcement Learning
6. Neural Networking and Deep Learning
7. Appendix: Machine Learning Questions

Dr. Amit Dua is currently working as an Assistant professor in the Computer Science Department of BITS Pilani, Pilani, where he has been teaching for over six years. Dr Amit is the Honorary Adjunct Distinguished Scientist-Professor and Head of the Blockchain Branch (India) at SIRG. As an educator, he has trained over 6000 students. He is the founder and CEO of Yushu Excellence Technologies Private Limited. Amit’s book on Blockchain Technology and Applications is highly acclaimed amongst academia and research. Dr Amit is the co-author of the Amazon national best-selling books on "Machine Learning" and "Cybercrime and Cyber Hygiene". He has published over 50 international publications and filed Indian patents and copyright for Blockchain innovation. Dr Amit is a certified life coach and was awarded as one of the top 10 coaches in 2022. Dr Amit is living to fulfil his mission to help 10 million people realize their true potential.
You can contact Dr. Amit Dua at mail.amitdua@gmail.com. He is also active on Facebook, Instagram, Youtube, and Linkedin.

Umair Ayub is a Masters student in Data Science at New York University. He holds a Bachelors degree in Computer Science and Engineering from the National Institute of Technology in Srinagar. He has worked on a number of projects, including the creation of real-time traffic light coordination using Reinforcement Learning. Hes written a book chapter about the Taxonomy of E-Healthcare Systems Using Machine Learning. He is currently using data science to solve real-world business problems.
You can contact Mr. Umair Ayub at mail umairayub110@gmail.com. He is also active on Linkedin.