Mastering Machine Learning with Python in Six Steps

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<div><p>Master machine learning with Python in six steps and explore fundamental to advanced topics, all designed to make you a worthy practitioner. </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></div><div><b>What You'll Learn</b> </div><div><div><ul><li>Examine the fundamentals of Python programming language </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><div></div><div> </div><div><b>Who This Book Is For</b> </div></div></div><div> </div><div>Python developers or data engineers looking to expand their knowledge or career into machine learning area. </div><div><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></div><div><div> </div></div>

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<div>Manohar Swamynathan is a data science practitioner and an avid programmer with over 13 years of experience in various data science related areas that include data warehousing, Business Intelligence (BI), analytical tool development, ad-hoc analysis, predictive modeling, data science product development, consulting, formulating strategy and executing analytics program. He's had a career covering life cycle of data across different domains, such as US mortgage banking, retail, insurance, and industrial IoT. He has a bachelor's degree with a specialization in physics, mathematics, computers, and a master's degree in project management. He's currently living in Bengaluru, the Silicon Valley of India, working as Staff Data Scientist with GE Digital, contributing to the next big digital industrial revolution. </div><div><p></p></div>

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