Machine learning is transforming the world around us. To become successful, you’d better know what kinds of problems can be solved with machine learning, and how they can be solved. Don’t know where to start? The answer is one button away.
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During this course you will:
- Identify practical problems which can be solved with machine learning
- Build, tune and apply linear models with Spark MLLib
- Understand methods of text processing
- Fit decision trees and boost them with ensemble learning
- Construct your own recommender system.
As a practical assignment, you will
- build and apply linear models for classification and regression tasks;
- learn how to work with texts;
- automatically construct decision trees and improve their performance with ensemble learning;
- finally, you will build your own recommender system!
With these skills, you will be able to tackle many practical machine learning tasks.
We provide the tools, you choose the place of application to make this world of machines more intelligent.
Syllabus
WEEK 1: Welcome; (Optional) Machine Learning: Introduction; Spark MLLib and Linear Models
WEEK 2: Machine Learning with Texts & Feature Engineering
WEEK 3: Decision Trees & Ensemble Learning
WEEK 4: Recommender Systems
WEEK 5: Recommender Systems (practice week)
