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

✍ Scribed by Manohar Swamynathan

• Publisher: Apress
• Year: 2017
• Tongue: English
• Leaves: 370
• Category: Library

✦ Synopsis

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.

✦ Table of Contents

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