Fundamentals of Machine Learning
Book Now - 3 Delivery Methods Available:
Classroom Virtual Classroom Private Group - Virtual Self-Paced OnlineOverview
An introduction to R and data analytics, with a deeper Introduction to Machine Learning.
This 3-day course will enable you to better understand what Machine Learning is, including the fundamental practices and principles. At the end of the course you'll be able to create simple Machine Learning models using Python and R.
Objectives
At the end of this course attendees will know:
- The fundamentals of Machine Learning methodologies and algorithms
- The mathematics required for understanding and using Machine Learning algorithms
- R Machine Learning packages
At the end of this course attendees will be able to:
- Build Machine Learning models using R
- Perform regression analysis
- Perform computer simulations using R
- Perform validation of Machine Learning models using R to evaluate the quality of models
Target Audience
Aimed at people with existing technological and mathematical background looking to get a quick exposure to mathematics and techniques of Machine Learning.
Training Partners
We work with the following best of breed training partners using our bulk buying power to bring you a wider range of dates, locations and prices.
Modules
Prerequisites
Delegates wish to take this course should have already completed 'Fundamentals of Data Science' or have the equivalent level of knowledge.
Additional Learning
The courses below may help you meet the knowledge level required to take this course.
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Fundamentals of Data Science
This 3-day course blends discussion and group exercises to explore the field of data science with applied real world examples.
Scheduled Dates
Please select from the dates below to make an enquiry or booking.
Pricing
Different pricing structures are available including special offers. These include early bird, late availability, multi-place, corporate volume and self-funding rates. Please arrange a discussion with a training advisor to discover your most cost effective option.
| Code | Location | Duration | Price | Jan | Feb | Mar | Apr | May | Jun |
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| Later scheduled dates may be available for this course. | |||||||||