**Instructor(s):**

Vincent has a Master's degree in Artificial Intelligence, and has more than 3 years of experience with machine learning problems of different kinds. He experienced first-hand the difficulties that come with building and assessing machine learning systems. This made him passionate about teaching people how to do machine learning the right way.

Even though Gilles has recently graduated with a degree in Fundamental Mathematics, he knows that there's more to be done than mathematics. With a solid knowledge in classical statistics, he now pursues a PhD in parallelizing regression modeling techniques.

Filip Schouwenaars

Sebastian Perez Saaibi

This online machine learning course is perfect for those who have a solid basis in R and statistics, but are complete beginners with machine learning. After a broad overview of the discipline's most common techniques and applications, you'll gain more insight into the assessment and training of different machine learning models. The rest of the course is dedicated to a first reconnaissance with three of the most basic machine learning tasks: classification, regression and clustering.

In this first chapter, you get your first intro to machine learning. After learning the true fundamentals of machine learning, you'll experiment with the techniques that are explained in more detail in future chapters.

- Machine Learning: What's the challenge? 50 xp
- Acquainting yourself with the data 100 xp
- What is, what isn't? 50 xp
- What is, what isn't? (2) 50 xp
- Basic prediction model 100 xp
- Classification, Regression, Clustering 50 xp
- Classification, Regression or Clustering? 50 xp
- Classification: Filtering spam 100 xp
- Regression: LinkedIn views for the next 3 days 100 xp
- Clustering: Separating the iris species 100 xp
- Supervised vs. Unsupervised 50 xp
- Getting practical with supervised learning 100 xp
- How to do unsupervised learning (1) 100 xp
- How to do unsupervised learning (2) 100 xp
- Tell the difference 50 xp

You'll learn how to assess the performance of both supervised and unsupervised learning algorithms. Next, you'll learn why and how you should split your data in a training set and a test set. Finally, the concepts of bias and variance are explained.

You'll gradually take your first steps to correctly perform classification, one of the most important tasks in machine learning today. By the end of this chapter, you'll be able to learn and build a decision tree and to classify unseen observations with k-Nearest Neighbors.

Although a traditional subject in classical statistics, you can also consider regression from a machine learning point of view. You'll learn more about the predictive capabilities and performance of regression algorithms. At the end of this chapter you'll be acquainted with simple linear regression, multi-linear regression and k-Nearest Neighbors regression.

As an unsupervised learning technique, clustering requires a different approach than the ones you have seen in the previous chapters. How can you cluster? When is a clustering any good? All these questions will be answered; you'll also learn about k-means clustering and hierarchical clustering along the way. At the end of this chapter and our machine learning video tutorials, you’ll have a basic understanding of all the main principles.