INTRODUCTION TO MACHINE LEARNING USING PYTHON

Over the past forty to fifty years, machine learning has evolved from a science fiction concept to a necessary tool for our daily life. From autonomous vehicles to Amazon’s virtual assistant Alexa, machine learning is simplifying our daily life activities. Machine learning is necessary because it can execute projects that would be too complicated for a human to handle alone. Because humans can’t possibly access massive volumes of data by hand, we rely on computer systems and that’s where the machine learning comes into streamlining our processes. Current applications of AI include self-driving cars, digital deception detection, facial recognition, Facebook’s companion concept, and many more which highlights the relevance of AI in present scenario. Companies like Amazon and Netflix have built AI algorithms that sift through mountains of data to determine what customers want and then recommend products accordingly.

With machine learning, tasks that usually require huge processing power are now possible on desktop machines. Machine learning is a collection of algorithms and techniques used to design systems that learn from data. These systems are then able to perform predictions or deduce patterns from the supplied data. It further covers all important concepts such as exploratory data analysis, data pre-processing, feature extraction, data visualization and clustering, classification, regression and model performance evaluation.

This book is an outcome of our experience of teaching and conducting research activities on machine learning applications. It can be useful for researchers, practitioners, educators and students. It is written such that all the basic concepts are explained in simplified manner. This book provides a strong foundation in machine learning using Python libraries. It discusses about topics such as foundations of machine learning, introduction to Python, descriptive analytics and predictive analytics, decision tree learning, random forest, boosting, recommended systems and text analytics. This book includes a total of five chapters. It covers fundamentals of machine learning, end-to-end machine learning projects, classification, and training of models, support vector machines, dimensionality reduction, artificial neural networks and convolutional neural networks.