Introduction to Machine Learning

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Course Description

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.

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.

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.

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.

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.

1
What is Machine Learning
Free

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.
View Chapter Details Play Chapter Now
Performance measures

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.
View Chapter Details Play Chapter Now
Classification

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.
View Chapter Details Play Chapter Now
Regression

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.
View Chapter Details Play Chapter Now
Clustering

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.
View Chapter Details Play Chapter Now

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Decision Science Analytics @ USAA

Vincent Vankrunkelsven

Data Science Instructor at DataCamp

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.

Gilles Inghelbrecht

Doctoral Student at Vrije Universiteit Brussel

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.

Course Instructor

Vincent Vankrunkelsven

Data Science Instructor at DataCamp

Gilles Inghelbrecht

Doctoral Student at Vrije Universiteit Brussel

Collaborator(s)

Filip Schouwenaars
Sebastian Perez Saaibi

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Loved by learners at thousands of top companies:

Course Description

What is Machine Learning

Machine Learning: What's the challenge?

Acquainting yourself with the data

What is, what isn't?

What is, what isn't? (2)

Basic prediction model

Classification, Regression, Clustering

Classification, regression or clustering?

Classification: Filtering spam

Regression: LinkedIn views for the next 3 days

Clustering: Separating the iris species

Supervised vs. Unsupervised

Getting practical with supervised learning

How to do unsupervised learning (1)

How to do unsupervised learning (2)

Tell the difference

Classification

Decision trees

Learn a decision tree

Understanding the tree plot

Classify with the decision tree

Pruning the tree

Interpreting the tree

Splitting criterion

k-Nearest Neighbors

Preprocess the data

The knn() function

K's choice

Interpreting a Voronoi diagram

The ROC curve

Creating the ROC curve (1)

Creating the ROC curve (2)

The area under the curve

Interpreting the curves

Comparing the methods

Clustering

Clustering with k-means

k-means: how well did you do earlier?

The influence of starting centroids

Making a scree plot!

What is your optimal k?

Performance and scaling issues

When to standardize your data?

Standardized vs non-standardized clustering (1)

Standardized vs non-standardized clustering (2)

Hierarchical Clustering

Did you get it all?

Single Hierarchical Clustering

Complete Hierarchical Clustering

Hierarchical vs k-means

Interpreting Dunn's Index

Clustering US states based on criminal activity

Performance measures

Measuring model performance or error

The Confusion Matrix

Deriving ratios from the Confusion Matrix

The quality of a regression

Adding complexity to increase quality

Let's do some clustering!

What to do with all these performance measures?

Training set and test set

Split the sets

First you train, then you test

Using Cross Validation

How many folds?

Bias and Variance

Overfitting the spam!

Increasing the bias

Interpretability

Regression

Regression: simple and linear!