Real-World Machine Learning

De: Henrik Brink, Joseph Richards, Mark Fetherolf

Narrado por: Lisa Farina

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

PLEASE NOTE: When you purchase this title, the accompanying PDF will be available in your Audible Library along with the audio.

©2016 Manning Publications (P)2016 Manning Publications

Full of mistakes

Terrible as an audiobook, major mistakes all over. They skip the equations entirely and don’t make distinctions verbally that would have been visible in the text. Shame.