Learn how to apply Machine Learning concepts fast using the Scikit-Learn library. The course provides clear explanations and challenging assignments. It is a perfect balance between theory and implementation.
Created by: Kevyn Collins-Thompson
Produced in 2016
What you will learn
- Methods and techniques of applied machine learning
- Everyday application of machine learning, in credit cards fraud detection or showing ads
- How to use Scikit-learn Python library
- Understand data dimensionality and clustering algorithms
- Classify different types of fruit using a k-nearest neighbor classifier
- Optimize ML model's performance with evaluation and selection methods
- Implement Naive Bayes Classifiers and Random Forests algorithm
- Create predictive models to forecast outcomes in the future
- Much, Much more!
- Practical application of Machine Learning with Scikit-Learn.
- Good mixture of lectures, reading and practice.
- Insights into key Machine Learning techniques and tools.
- Well-organized content and stimulating assignments.
- Covers numerous topics in a short time.
- Lacks real world examples.
Kevyn Collins-Thompson is an Associate Professor of Information and Computer Science in the School of Information at the University of Michigan. He works on developing algorithms and systems for effectively connecting people with information, especially for educational goals. This involves bringing together methods from applied machine learning, human-computer interaction (HCI), and natural language processing. He also has more than a decade of industry experience as a software engineer, manager, and researcher.
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