Practical Machine Learning With Python A Problem Solver’s Guide To Building Real World Intelligen
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- Contents
- About the Authors
- About the Technical Reviewer
- Acknowledgments
- Foreword
- Introduction
- Part I: Understanding Machine Learning
- Chapter 1: Machine Learning Basics
- The Need for Machine Learning
- Understanding Machine Learning
- Computer Science
- Data Science
- Mathematics
- Statistics
- Data Mining
- Artificial Intelligence
- Natural Language Processing
- Deep Learning
- Machine Learning Methods
- Supervised Learning
- Unsupervised Learning
- Semi-Supervised Learning
- Reinforcement Learning
- Batch Learning
- Online Learning
- Instance Based Learning
- Model Based Learning
- The CRISP-DM Process Model
- Building Machine Intelligence
- Real-World Case Study: Predicting Student Grant Recommendations
- Challenges in Machine Learning
- Real-World Applications of Machine Learning
- Summary
- Chapter 2: The Python Machine Learning Ecosystem
- Python: An Introduction
- Introducing the Python Machine Learning Ecosystem
- Summary
- Chapter 1: Machine Learning Basics
- Part II: The Machine Learning Pipeline
- Chapter 3: Processing, Wrangling, and Visualizing Data
- Chapter 4: Feature Engineering and Selection
- Features: Understand Your Data Better
- Revisiting the Machine Learning Pipeline
- Feature Extraction and Engineering
- Feature Engineering on Numeric Data
- Feature Engineering on Categorical Data
- Feature Engineering on Text Data
- Feature Engineering on Temporal Data
- Feature Engineering on Image Data
- Feature Scaling
- Feature Selection
- Dimensionality Reduction
- Summary
- Chapter 5: Building, Tuning, and Deploying Models
- Part III: Real-World Case Studies
- Chapter 6: Analyzing Bike Sharing Trends
- Chapter 7: Analyzing Movie Reviews Sentiment
- Problem Statement
- Setting Up Dependencies
- Getting the Data
- Text Pre-Processing and Normalization
- Unsupervised Lexicon-Based Models
- Classifying Sentiment with Supervised Learning
- Traditional Supervised Machine Learning Models
- Newer Supervised Deep Learning Models
- Advanced Supervised Deep Learning Models
- Analyzing Sentiment Causation
- Summary
- Chapter 8: Customer Segmentation and Effective Cross Selling
- Chapter 9: Analyzing Wine Types and Quality
- Chapter 10: Analyzing Music Trends and Recommendations
- The Million Song Dataset Taste Profile
- Exploratory Data Analysis
- Recommendation Engines
- A Note on Recommendation Engine Libraries
- Summary
- Chapter 11: Forecasting Stock and Commodity Prices
- Chapter 12: Deep Learning for Computer Vision
- Index