Machine Learning Fundamentals with R Training Course

Course Code

MLFWR1

Duration

14 hours (usually 2 days including breaks)

Requirements

Knowledge of R programming language. Basic familiarity with statistics and linear algebra is recommended.

Overview

The aim of this course is to provide a basic proficiency in applying Machine Learning methods in practice. Through the use of the R programming platform and its various libraries, and based on a multitude of practical examples this course teaches how to use the most important building blocks of Machine Learning, how to make data modeling decisions, interpret the outputs of the algorithms and validate the results.

Our goal is to give you the skills to understand and use the most fundamental tools from the Machine Learning toolbox confidently and avoid the common pitfalls of Data Sciences applications.

Course Outline

Introduction to Applied Machine Learning

  • Statistical learning vs. Machine learning
  • Iteration and evaluation
  • Bias-Variance trade-off

Regression

  • Linear regression
  • Generalizations and Nonlinearity
  • Exercises

Classification

  • Bayesian refresher
  • Naive Bayes
  • Logistic regression
  • K-Nearest neighbors
  • Exercises

Cross-validation and Resampling

  • Cross-validation approaches
  • Bootstrap
  • Exercises

Unsupervised Learning

  • K-means clustering
  • Examples
  • Challenges of unsupervised learning and beyond K-means

Testimonials

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Bookings, Prices and Enquiries

Guaranteed to run even with a single delegate!

Private Classroom

From £2900

Private Remote

From £2600 (99)

Public Classroom

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