In this one-day intermediate-level course, you will build upon the experience gained in the beginner course by learning about a wide range of machine learning algorithms. You will learn how to evaluate your models both numerically and visually. You will investigate how data can change over time and how to deal with it. And you will spend time investigating dimensionality reduction techniques to handle large dimensional datasets.

Throughout the day theory will be complemented by “peer-instruction”; a teaching method that improves your learning experience by learning from worked examples. This will provide you with valuable experience that you can apply to your own problems.

Who will benefit

This course is aimed towards developers, in which we will delve into the mathematics behind the code as well as developing real life algorithms in Python. One-to-one help will be provided for developers new to Python and all algorithms, frameworks and libraries used will be demonstrated by the instructor. This is an intermediate-level course, and it is expected that you will have had some experience to Python and Data Science. This can be achieved by attending the beginners course. I.e. understand overfitting, basic machine learning algorithms, basic statistics, data engineering and analysis, types of learning, etc.)

What you will achieve

The day will comprise of a series of sub-hour theoretical sessions separated by practical exercises. It will cover a range of topics, but it is expected that you will be able to:

• Evaluate models numerically
• Investigate and assess models visually
• Have practical experience in industrial statistics
• Understand the practical steps to design and deploy models
• Further enhance data pre-processing skills
• Work with high dimensional datasets
• Gain experience in a wide variety of Machine Learning algorithms

Topics covered in this training

• Numerical and visual model evaluation
• Introduction and application of statistics in data science
• Further experience with real-life messy data
• Dimensionality Reduction Techniques
• Use of intermediate-level machine learning algorithms: boosting, LDA/QDA, Gaussian processes, ARIMA models, Affinity propagation, Mean-Shift and Spectral Clustering
• Introduction to tooling, testing and deployment
• An in-depth practical example demonstrating the day’s concepts