Thinking of learning more in Machine Learning? Then you have landed in the right place.The overall aim of this book is to explore and examine key concepts, methods and techniques used in the Machine Learning.Machines have come a part of our lives. No matter where you go, you will always find a machine around you, but it is only over the last few years that we have understood and enhanced the capabilities of machines. Machines can now perform tasks that involve the simulation of cognition, which is a task that until recently only human beings could accomplish. The ever-growing capabilities of machines are instilling a sense of fear among observers. What if machines became more intelligent than human beings become and decided to eliminate any human being who was not so smart as them?Machine learning is not as simple as turning a switch on and off. It is not an out-of-the-box solution either. Machines and statistical algorithms work in parallel with each other. This book will help you explore exactly what Machine learning is and will introduce the reader the concepts, techniques, application of Machine Learning Algorithms with the practical case studies and walk-through examples to practice. By the time you are done reading this book, you will have a complete understanding as to what Machine Learning is. Following are the important points discussed in this book: First Part: Introduction to Machine Learning Definition of Machine Learning and Artificial Intelligence Goals and importance of Machine Learning Using of Machine Learning Workkflow of Machine Learning Subjects involved in Machine Learning Second Part: Types of Machine Learning Supervised Learning Unsupervised Learning Semi-Supervised Learning Reinforcement Learning Third Part: Techniques and Algorithms of Machine Learning Linear Regression Modeling Decision Trees Bagging, Random Forest and Boosting Algorithms Principal Component Analysis K means K Nearest Neighbors Logistic Regression Na�ve Bayes Estimation Support Vector Machines Hierarchical Clustering Association Rules and Frequent Patterns Analysis Part 4: Problems in Machine Learning Overfitting and Underfitting Problems Bias and Variance Tradeoff The sparcity Problem Dinensionality Problem Data Problem Simplicity and Accuracy Book Objectives To have an appreciation for Machine Learning and an understanding of their fundamental principles. To have an elementary adeptness in a Machine Learning Concepts and terms which includes an ability to understand the algorithms. Target Users This book designed for a variety of target audiences. The most suitable users would include: Newbies in Computer Science Techniques and Artificial Intelligence Professionals in Data scientist and Social Sciences Professors, lecturers, or tutors to be in position to find better ways to explain the content to their students with simples and easiest way The students and Academicians, especially those that are focusing on Machine Learning as their professions Scroll to the top and click on 'buy now' to get started.