Duration: 3 days   
Cost: €1,650
Early-Bird Rate: €1,450

 

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
Fundamentals of Machine Learning for Predictive Data Analytics

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.

Each delegate will receive a fee copy of Fundamentals of Machine Learning for Predictive Data Analytics

Download Info PDF

About The Trainers

Dr. Brian Mac Namee
Dr. Brian Mac Namee
Dr. Brian Mac Namee, Director of Training at the Analytics Store, has over 14 years experience in analytics lecturing, training, and consultancy. With particular expertise in analytics fundamentals Dr. Mac Namee has delivered analytics training nationally and internationally for large organisations.
In addition Dr Mac Namee is a lecturer in the School of Computer Science at the University College Dublin (UCD), and was previously a founding member of the Applied Intelligence Research Centre (AIRC) at Dublin Institute of Technology (DIT).
Brian is co-author of the text book Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies published by MIT Press in 2015.
Dr. John Kelleher
Dr. John Kelleher
Dr. John Kelleher is a lecturer in the School of Computing at the Dublin Institute of Technology (DIT), and is a founding member of the Applied Intelligence Research Centre at DIT. John is co-author of the text book Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies published by MIT Press in 2015. John has conducted research and presented work internationally in the broad areas of Artificial Intelligence, Data Analytics, Natural Language Processing and Computational Linguistics. Some of the specific topics in which John has worked are:
• machine learning
• machine translation
• activity recognition
• grounding language in perception
• reference resolution and generation
• dialog systems and human robot interaction
• spatial cognition and computational models of spatial semantics