If you are just getting started with Machine Learning, or you have used some of the techniques but do not have a thorough understanding of they key concepts, then this course is for you. You will gain an understanding of the key building blocks in Machine Learning, enabling you to better understand and apply the many techniques available.
Machine Learning Concepts is a popular course often booked in conjunction with our Penalised Regression and Trees, Random Forests and Gradient Boosting Machines courses. This interactive course is delivered over one day, and will cover the basics of good programming practice in R.
WHAT WILL I LEARN?
At the end of this course, you will have a good understanding of the following high level concepts and how they can be applied to make key decisions in your modeling process:
• Performance measurement and choice of metric
• Loss functions
• Generalisation error
• Bias and variance
• Training and validation curves
• Hyper-parameter tuning
• Feature engineering
You will also understand the principles of, and when to use:
• Gradient descent
• Use of kernels
• Unsupervised learning
WHO IS IT FOR?
Prior knowledge of R, up to the level of our Introduction to R for Machine Learning course is required. Prior knowledge of Machine Learning is not required.