Mastering Machine Learning with Python in Six Steps : A Practical Implementation Guide to Predictive Data Analytics Using Python Master machine learning with Python in six steps and explore fundamental to advanced topics all designed to make you a worthy practitioner. 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. Mastering Machine Learning with Python in Six Steps presents each topic in two parts: theoretical concepts and practical implementation using suitable Python packages. 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. 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 Chapter 1: Getting Started in Python Chapter 2: Introduction to Machine Learning Chapter 3: Fundamentals of Machine Learning Chapter 4: Model Diagnosis and Tuning Chapter 5: Text Mining Chapter 6: Demystifying Neural Network Chapter 7: Conclusion