Course 2 of 4 in this specialization
Implement and Evaluate Machine Learning Models
Coming soon 29 topics Self-paced · Online
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Where this comes from
Built from the Wiley title of the same name — professionally edited, technically reviewed, and already relied on by people doing this work.
Source ISBN 9781119724056
How it was built
Sejal Learning Systems rebuilt the book as a course: objectives first, then short video, worked examples, and practice with real feedback. It runs on Coursera at your own pace, with a certificate on completion.
Implement and Evaluate Machine Learning Models is a self-paced online course built from the Wiley title of the same name.
What this course covers
- Introduction
- Part 1: Introducing How Machines Learn
- Chapter 1: Getting the Real Story about AI
- Chapter 2: Learning in the Age of Big Data
- Chapter 3: Having a Glance at the Future
- Part 2: Preparing Your Learning Tools
- Chapter 4: Installing a Python Distribution
- Chapter 5: Beyond Basic Coding in Python
- Chapter 6: Working with Google Colab
- Part 3: Getting Started with the Math Basics
- Chapter 7: Demystifying the Math Behind Machine Learning
- Chapter 8: Descending the Gradient
- Chapter 9: Validating Machine Learning
- Chapter 10: Starting with Simple Learners
- Part 4: Learning from Smart and Big Data
- Chapter 11: Preprocessing Data
- Chapter 12: Leveraging Similarity
- Chapter 13: Working with Linear Models the Easy Way
- Chapter 14: Hitting Complexity with Neural Networks
- Chapter 15: Going a Step Beyond Using Support Vector Machines
- Chapter 16: Resorting to Ensembles of Learners
- Part 5: Applying Learning to Real Problems
- Chapter 17: Classifying Images
- Chapter 18: Scoring Opinions and Sentiments
- Chapter 19: Recommending Products and Movies
- Part 6: The Part of Tens
- Chapter 20: Ten Ways to Improve Your Machine Learning Models
- Chapter 21: Ten Guidelines for Ethical Data Usage
- Chapter 22: Ten Machine Learning Packages to Master