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. **What You'll Learn*** Examine the fundamentals of Python programming language * Review machine Learning history and evolution * Understand machine learning system development frameworks * Implement supervised/unsupervised/reinforcement learning techniques with examples * Explore fundamental to advanced text mining techniques * Implement various deep learning frameworks **Who This Book Is For** Python developers or data engineers looking to expand their knowledge or career into machine learning area. Non-Python (R, SAS, SPSS, Matlab or any other language) machine learning practitioners looking to expand their implementation skills in Python. 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. Brief Contents Contents Intro 1 Start in Python The Best Things in Life Are Free The Rising Star Python 2.7.x or Python 3.4.x? Key Concepts Endnotes 2 Intro to Machine Learning History and Evolution Artificial Intelligence Evolution Different Forms Machine Learning Categories Frameworks for Building Machine Learning Systems Machine Learning Python Packages Data Analysis Packages Machine Learning Core Libraries Endnotes 3 Fundamentals of Machine Learning Machine Learning Perspective of Data Scales of Measurement Feature Engineering Exploratory Data Analysis (EDA) Supervised Learning– Regression Supervised Learning – Classification Unsupervised Learning Process Flow Endnotes 4 Model Diagnosis & Tuning Optimal Probability Cutoff Point Rare Event or Imbalanced Dataset Bias and Variance K-Fold Cross-Validation Stratified K-Fold Cross-Validation Ensemble Methods Bagging Boosting Ensemble Voting – Machine Learning’s Biggest Heroes United Stacking Hyperparameter Tuning Endnotes 5 Text Mining & Recommender Systems Text Mining Process Overview Data Assemble (Text) Data Preprocessing (Text) Data Exploration (Text) Model Building Text Similarity Text Clustering Topic Modeling Text Classification Sentiment Analysis Deep Natural Language Processing (DNLP) Word2Vec Recommender Systems Endnotes 6 Deep & Reinforcement Learning Artificial Neural Network (ANN) What Goes Behind, When Computers Look at an Image? Why Not a Simple Classification Model for Images? Perceptron – Single Artificial Neuron Multilayer Perceptrons (Feedforward Neural Network) Restricted Boltzman Machines (RBM) MLP Using Keras Autoencoders Convolution Neural Network (CNN) Recurrent Neural Network (RNN) Transfer Learning Reinforcement Learning Endnotes Conclusion Index Exploring fundamental to advanced topics, this book s approach is based on 'Six degrees of separation', which states that everyone and everything is a maximum of six steps away. It also covers advanced text mining techniques, neural networks and deep learning techniques, and their implementation. The code will be available as iPython notebooks.