This book focuses on advanced sub-domains of machine learning, such as Class Imbalance strategies, Hidden Markov Models, HMM, Reinforcement Learning, RNN, and LSTM, along with a few more advanced level topics. With its high power and ease of use, we will use the Scikit Machine Learning Library in Python. Unlike statistics, where models are used to understand data, different modeling in machine learning focuses on developing models that make more accurate predictions. Unlike the broader area of machine learning that can be used with data of any format, Hidden Markov models focus on robotics (e.g., controlling the robots by programming). This book is designed to introduce you to the most important and powerful methods of machine learning used by leading computer experts. It contains clear examples and detailed code samples to demonstrate deep learning, semi-directed learning, and other techniques. The methods discussed in this book will help you get started in this profitable and growing industry. Compete with the best data professionals and gain practical and theoretical insight into the latest in-depth training algorithms. Use your new skills to solve real-world problems. Automation of large and complex data sets and overcoming complex and time- consuming practices. Increase the accuracy of existing models and their input using object design methods. Sharing of different training methods to improve the consistency of results. Understand the hidden structure of documents using various unmanaged methods. To further improve the effectiveness of training models by using consistent methods to combine different models. In addition, the book is designed in such a way that any student, researcher, or technologist who conducts various experiments using large data sets and combines them into a predictive output can use a variety of machine learning tools offered by the programming language.