Real-World Machine Learning (Unabridged‪)‬

Real-World Machine Learning will teach you the concepts and techniques you need to be a successful machine learning practitioner without overdosing you on abstract theory and complex mathematics. By working through immediately relevant examples in Python, you'll build skills in data acquisition and modeling, classification, and regression. You'll also explore the most important tasks like model validation, optimization, scalability, and real-time streaming. When you're done, you'll be ready to successfully build, deploy, and maintain your own powerful ML systems.  Machine learning systems help you find valuable insights and patterns in data, which you'd never recognize with traditional methods. In the real world, ML techniques give you a way to identify trends, forecast behavior, and make fact-based recommendations. It's a hot and growing field, and up-to-speed ML developers are in demand.  What's inside:


Predicting future behavior
Performance evaluation and optimization
Analyzing sentiment and making recommendations


No prior machine learning experience assumed, but you should know Python.  Henrik Brink, Joseph Richards, and Mark Fetherolf are experienced data scientists engaged in the daily practice of machine learning.  Table of Contents: The Machine-Learning Workflow 


1. What is machine learning?
2. Real-world data 
3. Modeling and prediction 
4. Model evaluation and optimization 
5. Basic feature engineering PRACTICAL APPLICATION 
6. Example: NYC taxi data 
7. Advanced feature engineering 
8. Advanced NLP example: movie review sentiment 
9. Scaling machine-learning workflows 
10. Example: digital display advertising