An introduction to R and Python programming languages, plus a deep coverage of the mathematics, algorithms and technology of Machine learning.

This 5-day course is designed for people who are already working with Big Data and analysing large statistical sets. By attending this course you'll learn what an effective Machine Learning approach looks like in an organisation.

You'll learn to understand different models of Machine Learning and how to implement them. Also covered is how to validate the statistical quality and the metrics attached to that data and how to implement them practically using Python and R.

Target Audience

This course is aimed at fledging Machine Learning practitioners, and data analysts who wish to gain more in depth knowledge of Machine Learning.

  • GCSE Mathematics or above. Must be comfortable with mathematical and logical way of thinking
  • Familiar with basic programming knowledge: variables, control flow, scope and functions
  • Prior experience with Python or R will be an advantage

At the end of this course attendees will know:

  • Fundamental mathematics and statistics required for understanding Machine Learning
  • The workings of some the most commonly used Machine Learning algorithms
  • Cross validation techniques and the different metrics used to determine the quality of Machine Learning models built
  • How to pick the best Machine Learning model for a given task

At the end of this course attendees will be able to:

  • Perform data preparation using R or Python
  • Build machine learning models using R or Python
  • Perform simulation using R or Python
  • Perform cross validation and evaluate a machine learning model
  • Machine Learning: Method
  • Introduction to Python
  • Data Preparation in Python
  • Machine Learning: Regression
  • Introduction to Mathematics
  • Mathematics of Machine Learning
  • Machine Learning Algorithms
  • Probability and Statistics
  • Python Machine Learning
  • Clustering in Python
  • Decision Trees in Python
  • Deep Learning in Python
  • Evaluating ML algorithms
  • Choosing ML algorithms: Cross Validation
  • Ensemble Modelling