Course 1 of 4 in this specialization
Apply Machine Learning Techniques to Real Problems
Coming soon 27 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 9781394373239
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.
Apply Machine Learning Techniques to Real Problems 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 Computers
- Chapter 3: Having a Glance at the Future
- Part 2: Learning Machine Learning by Coding
- Chapter 4: Working with Google Colab
- Chapter 5: Understanding the Tools of the Trade
- Chapter 6: Getting Beyond Basic Coding in Python
- Part 3: Building the Foundations
- Chapter 7: Demystifying the Math Behind Machine Learning
- Chapter 8: Descending the Gradient
- Chapter 9: Validating Machine Learning
- Part 4: Learning from Smart Algorithms
- Chapter 10: Starting with Simple Learners
- Chapter 11: Leveraging Similarity
- Chapter 12: Working with Linear Models the Easy Way
- Chapter 13: Going Beyond the Basics with Support Vector Machines
- Chapter 14: Tackling Complexity with Neural Networks
- Chapter 15: Resorting to Ensembles of Learners
- Part 5: Applying Learning to Real Problems
- Chapter 16: Classifying Images
- Chapter 17: Scoring Opinions and Sentiments
- Chapter 18: Recommending Products and Movies
- Part 6: The Part of Tens
- Chapter 19: Ten Ways to Improve Your Machine Learning Models
- Chapter 20: Ten Guidelines for Ethical Data Usage