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

Description

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. What You'll Learn Understand machine learning development and frameworksAssess model diagnosis and tuning in machine learningExamine text mining, natuarl language processing (NLP), and recommender systemsReview reinforcement learning and CNN Who This Book Is For Python developers, data engineers, and machine learning engineers looking to expand their knowledge or career into machine learning area. Front Matter ....Pages i-xvii Step 1: Getting Started in Python 3 (Manohar Swamynathan)....Pages 1-64 Step 2: Introduction to Machine Learning (Manohar Swamynathan)....Pages 65-143 Step 3: Fundamentals of Machine Learning (Manohar Swamynathan)....Pages 145-262 Step 4: Model Diagnosis and Tuning (Manohar Swamynathan)....Pages 263-323 Step 5: Text Mining and Recommender Systems (Manohar Swamynathan)....Pages 325-381 Step 6: Deep and Reinforcement Learning (Manohar Swamynathan)....Pages 383-442 Conclusion (Manohar Swamynathan)....Pages 443-448 Back Matter ....Pages 449-457