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ABOUT THIS COURSE
Machine learning uses computers to run predictive models that learn from existing data in order to forecast future behaviors, outcomes, and trends.
In this data science course, you will be given clear explanations of machine learning theory combined with practical scenarios and hands-on experience building, validating, and deploying machine learning models. You will learn how to build and derive insights from these models using R, Python, and Azure Machine Learning.
- Explore classification
- Regression in machine learning
- How to improve supervised models
- Details on non-linear modeling
- Recommender systems
- The hands-on elements of this course leverage a combination of R, Python, and Microsoft Azure Machine Learning
- AI developer
- Machine Learning Engineer
- There are no prerequisite required for this course
Self-Paced Learning Outline
Module 1: Classification
- Lesson 1: Introduction to Classification
- Lesson 2: Building Classification Models
Module 2: Regression
- Lesson 1: Introduction to Regression
- Lesson 2: Creating Regression Models
Module 3: Improving Machine Learning Models
- Lesson 1: Principles of Model Improvement
- Lesson 2: Techniques for Improving Models
Module 4: Tree and Ensemble Methods
- Lesson 1: Introduction to Decision Trees
- Lesson 2: Ensemble Methods
Module 5: Optimization-Based Methods
- Lesson 1: Neural Networks
- Lesson 2: Support Vector Machines (SVMs)
Module 6: Clustering and Recommenders
- Lesson 1: Clustering
- Lesson 2: Recommenders
- Final Challenge
|Learning Style||Self-Paced Learning|
|Course Duration||24 Hours|
|VPA Discount||VPA Discount|
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