Applied Data Science: Machine Learning

Ready to start learning?

Download the course brochure

Join our Discovery Webinar

This complimentary online session will be a high-level overview of how to use machine learning to solve problems, make decisions and build smart programs, including an overview of the online courses available to gain these sought-after skills.

Join our next session

Why you should take
this course

From Siri to smart cars, the ability to make computers learn from data without being programmed to do so is reshaping the modern world—making it a highly sought-after skill across industries.

Machine Learning
is growing rapidly

As one of the fastest-growing fields in data science, machine learning is increasingly used to make high-level decisions and develop innovative new technologies. It’s why there are 50% more jobs requiring machine learning skills than job seekers who have them. It’s also the 3rd highest paid analytical skill.

Machine learning is
disrupting every sector

Diagnostics
Marketing & content
Financial trading
Optimizing inventory
Screening & security

Who is it for?

Applied Data Science: Machine Learning is for anyone who wants to gain the skills and understanding to oversee or implement a project that covers the full data science pipeline, including modelling and evaluation. You either want to switch careers or open up new career opportunities in your current workplace with a formal recognition of your skills and an EPFL certification.

" The course structure and content are amazing. You cannot find anything else like it online. "

Pascal, EPFL Extension School Alumnus

Program curriculum

The Applied Data Science: Machine Learning program includes four certified courses and a customizable capstone project. Learners who successfully complete the program earn an EPFL Certificate of Open Studies Diploma and 15 ECTS credits.

01. Introduction to Data Analysis with Python

Foundational concepts of data analysis:

Getting started with Python
Explore your first data set
The Jupyter notebook
A first look at NumPy & Pandas
The basics of data visualization
Probability for data science
Linear algebra for data science

02. Applied Data Analysis

Working across the entire data analysis pipeline

Getting, cleaning & manipulating the data
In-depth NumPy & Pandas
Working with textual data
Working with time-series data
Databases in Python
Statistical data analysis

03. Applied Machine Learning 1

Basics of machine learning with Scikit-learn

Introduction to machine learning
Fitting a first model
Cost functions & outliers
Linear regressions
Gradient descent
Feature engineering
Regularization

04. Applied Machine Learning 2

Advanced machine learning techniques

K-nearest neighbors
Bias-variance trade-off
Logistic regressions
Decision trees & SVMs
Clustering & dimensionality reduction
Introduction to deep learning
Convolutional neural networks
Machine learning with different data types

05. Capstone Project

Showcase the skills and knowledge you’ve learnt on the program by applying them to your own data science project.

These program details are subject to change; please refer to the program outline at the time of registration.

Prerequisites

This program is taught at intermediate-level. You should have the following tools, skills and abilities prior to registering for this course:

English at B2 level
A computer with a webcam, microphone and a minimum internet connection of 2Mbps download / 512kbps upload, enabling you to stream videos with sound and to effectively participate in video chats
Basic understanding of algebra, geometry, calculus (derivatives), probability and statistics
Familiarity with computer environments (what is a program, file system, file formats, terminal, programming language library)
Prior experience with any programming language