Training Programmes
Fundamentals of Machine Learning with R or Python

Duration : 3 Days

Predictive analytics applications use machine learning to build predictive models for applications including price prediction, risk assessment, and predicting customer behaviour. Based on the trainers’ book, “Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples and Case Studies” (www.machinelearningbook.com) this course presents a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications.

Benefits of this Course :

• Guides delegates through the most important topics in machine learning, and how they should be applied to build real-world relevant predictive analytics models
• Based on the instructors’ book “Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples and Case Studies” published by MIT Press in 2015 (machinelearningbook.com)
• Uses a wide range of detailed examples and real-world case studies
• Expert, instructor led tuition delivered in small groups
• Presents sophisticated machine learning theories, and how they are applied to enable best business practice
• All delegates receive a free copy of the book “Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples and Case Studies”

At Course Completion : This course has been designed to guide delegates through the most important topics in machine learning, and how they should be applied to build real-world relevant predictive analytics models. After completing the course delegates will:

• Understand how to frame a business problem as a predictive analytics problem
• Understand the fundamental theories of machine learning, and the most important machine learning approaches
• Be familiar with a wide range of applications of predictive data analytics and machine learning, including the limitations of machine learning
• Be comfortable analysing the quality of datasets for machine learning models
• Have an awareness of the strengths and weaknesses of different technologies that can be used to build predictive analytics models using machine learning techniques
• Be fully prepared to understand newly emerging advanced topics in machine learning

Course Outline : The course will cover the following key topics through a series of interactive workshop sessions:

• What is predictive data analytics and what is it used for?
• What is machine learning?
• Training machine learning models – inductive bias, generalisation, overfitting and underfitting
• Fundamentals of data analysis and data visualisation
• Developing predictive data analytics solutions for business problems
• Deep dive on four key approaches to machine learning: Information-based, Error-based, Probability-based, and Similarity-based
• Evaluating predictive models

Who Should Attend : This course is aimed at people in a technical role who want to fully understand and use predictive analytics techniques.

Prerequisites : To attend this course delegates should be familiar with basic statistical concepts (such as mean, standard deviation, and correlation) and comfortable with data manipulation tools such as spreadsheets and databases.