Mastering Machine Learning with Python in Six Steps: A Practical Implementation Guide to Predictive Data Analytics Using Python

ISBN-13: 9781484249468 / Angielski / Miękka / 2019 / 457 str.

Manohar Swamynathan

Spis treści

Mastering Machine Learning with Python in Six Steps (2E)

Chapter 1: Step 1 – Getting Started with Python

Chapter 2 : Step 2 – Introduction to Machine Learning

Chapter 3: Step 3 – Fundamentals of Machine Learning

Chapter 4: Step 4 – Model Diagnosis and Tuning

Chapter 5: Step 5 – Text Mining, NLP AND Recommender Systems

Chapter 6: Step 6 – Deep and Reinforcement Learning

Chapter 7 : Conclusion

O autorze

Manohar Swamynathan is a data science practitioner and an avid programmer, with over 14+ 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/e-commerce, 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. 

Blurb

Explore fundamental to advanced Python 3 topics in six steps, all designed to make you a worthy practitioner. This updated version’s approach is based on the “six degrees of separation” theory, which states that everyone and everything is a maximum of six steps away and presents each topic in two parts: theoretical concepts and practical implementation using suitable Python 3 packages.

You’ll start with the fundamentals of Python 3 programming language, machine learning history, evolution, and the system development frameworks. Key data mining/analysis concepts, such as exploratory analysis, feature dimension reduction, regressions, time series forecasting and their efficient implementation in Scikit-learn are covered as well. You’ll also learn commonly used model diagnostic and tuning techniques. These include optimal probability cutoff point for class creation, variance, bias, bagging, boosting, ensemble voting, grid search, random search, Bayesian optimization, and the noise reduction technique for IoT data. 

Finally, you’ll review advanced text mining techniques, recommender systems, neural networks, deep learning, reinforcement learning techniques and their implementation. 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.