Overview
In this instructor-led, live training, participants will learn advanced techniques for Machine Learning with R as they step through the creation of a real-world application.
By the end of this training, participants will be able to:
- Understand and implement unsupervised learning techniques
- Apply clustering and classification to make predictions based on real world data.
- Visualize data to quicly gain insights, make decisions and further refine analysis.
- Improve the performance of a machine learning model using hyper-parameter tuning.
- Put a model into production for use in a larger application.
- Apply advanced machine learning techniques to answer questions involving social network data, big data, and more.
Audience
- Developers
- Analysts
- Data scientists
Format of the course
- Part lecture, part discussion, exercises and heavy hands-on practice
Requirements
- R programming experience
- An understanding of machine learning concepts
Course Outline
Introduction
Setting up the R Development Environment
Deep Learning vs Neural Network vs Machine Learning
Building an Unsupervised Learning Model
Case Study: Predicting an Outcome Using Existing Data
Preparing Test and Training Data Sets For Analysis
Clustering Data
Classifying Data
Visualizing Data
Evaluating the Performance of a Model
Iterating Through Model Parameters
Hyper-parameter Tuning
Integrating a Model with a Real-World Application
Deploying a Machine Learning Application
Troubleshooting
Summary and Conclusion