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Once a niche set of tools for statisticians, programmers and quants, machine learning (sometimes also called data mining or statistical learning) has spread in popularity to a wide variety of applications and disciplines. Get a practical, hands-on introduction to machine learning using R, an open-source, statistical programming language without delving into too much theory. Real-world examples teach you how to solve problems using machine learning in your own careers and fields.

## Course Outline

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**Course Objectives **

- Distinguish fundamental aspects of machine-learning algorithms.
- Frame problems to enable suitable solutions via machine learning.
- Train and evaluate machine learning models.
- Deploy machine learning models into operations.
- Build prediction, categorization and recommendation APIs.
- Deploy tools for collaborative and social programming.
- Generate high-quality graphical and textual results.

**What You Learn **

- The R machine learning developer environment
- Machine learning fundamentals
- Regression and classification
- Supervised, unsupervised and semi-supervised learning
- Linear and logistic regression
- Partitioning methods: CART/Regression trees, clustering, k-nearest neighbor
- Bagging
- Random forest
- Boosting
- Neural networks
- Support Vector Machines
- Deployment: data lakes, optimization, delivery and deployment

**How You Learn **

- Lectures and in-class discussions and exercises
- Homework assignments
- In-class exams
- Online discussions

**Is This Course Right for You? **

If you want to learn the fundamentals of machine learning and use R to build, evaluate or deploy machine learning models, then this course is geared to your needs. This course is also designed for scientists, engineers, business analysts and researchers who want to explore and analyze data and then present their findings in well-formatted textual and graphical forms.