Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python
<p>Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner. <br /></p><p>This book’s approach is based on the “Six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away. <i>Mastering Machine Learning with Python in Six Steps</i> presents each topic in two parts: theoretical concepts and practical implementation using suitable Python packages. </p><p>You’ll learn the fundamentals of Python programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as feature dimension reduction, regression, time series forecasting and their efficient implementation in Scikit-learn are also covered. Finally, you’ll explore advanced text mining techniques, neural networks and deep learning techniques, and their implementation. </p><p> </p><p>All the code presented in the book will be available in the form of iPython notebooks to enable you to try out these examples and extend them to your advantage.</p><b>What You'll Learn</b><br /><ul><li>Examine the fundamentals of Python programming language<br /></li><li>Review machine Learning history and evolution</li><li>Understand machine learning system development frameworks</li><li>Implement supervised/unsupervised/reinforcement learning techniques with examples</li><li>Explore fundamental to advanced text mining techniques</li><li>Implement various deep learning frameworks</li></ul><br /><b>Who This Book Is For</b><br /><br />Python developers or data engineers looking to expand their knowledge or career into machine learning area.<br /><p></p> <p>Non-Python (R, SAS, SPSS, Matlab or any other language) machine learning practitioners looking to expand their implementation skills in Python.</p> <p>Novice machine learning practitioners looking to learn advanced topics, such as hyperparameter tuning, various ensemble techniques, natural language processing (NLP), deep learning, and basics of reinforcement learning.</p><br />