Machine Learning 1

5.0 - - 2 review

You'll be able to :

Apply simple machine learning techniques in your area of expertise;

Understand the potential and the limitations of some of the most used machine learning algorithms;

Use machine learning packages and libraries, understanding the fundamental mechanisms behind them.

Requirements :

Beginner programming skills in Python, or in another object-oriented language;

Possess a general quantitative proficiency and understand the basics of linear algebra;

Laptop with minimal computing power: RAM>2GB, CPU clock>1.2GHz.

Course features

Lecturer	Valerio Rossetti
Starting	03/September/2019
Ending	19/October/2019
Training Sessions	Tuesday and Thursday, 7pm-9pm
Full-Immersion Saturdays	21/September/2019, 10am-7pm 19/October/2019, 10am-7pm
Location	Coworking Voisins , Geneva
# hours	40
Max Students	15
Language	English
Full Price Registration	1600 CHF
Early Bird Registration	1200 CHF (until 15/Aug.)

Description

With this course, you will make your first steps into the world of machine learning. This course will gradually bring you from simple analytical techniques (Linear and Logistic Regression) to more complex and powerful algorithms such as Artificial Neural Networks. A large fraction of the course is devoted to hands-on sessions in which you will apply theoretical concepts to simple but realistic problems.

The course includes two full-immersion Saturday of 8 hours each. These sessions will be dedicated to more complex projects in various sectors such as banking, insurance, engineering or marketing.

This course is tailored to professionals working in the private industry, who want to enrich their quantitative skills with very hot and powerful tools. It can also be suitable for academic researchers, who are looking for career opportunities in the private sector.

How it works

Registration

You can pay immediately if you think you have the right skills for the course. You will receive a confirmation message. If you are not sure, you can ask for an assessment. We will meet you and discuss how you fit the requirements.

Connect with amazing peers

During the course, you will interact with professionals coming from different sectors of the professional world and academia. Coding together and making small hackathons is the best way to learn.

Certificate

Your progress will be monitored throughout the course and you will present one of your projects to experts in the sector. In case of positive results, you will receive a certificate of completion that shows that you have mastered the subjects of the course and that you are able to apply machine learning techniques to a simple real-world problem.

Ask for information

Content

Introduction

Introduction to Machine Learning algorithms: what, why, how

Module 1:

Multivariate Analysis

Import, manipulate and visualize data in Python

Module 2:

Linear and logistic regression

Cost function, gradient descent

Manipulation of variables

Regularization

Module 3:

Neural Networks

Design, model optimization

Module 4:

Design, Diagnostics and Best Practices

Learning curves

Overfitting and underfitting

Full-Immersion 1

Build a real-world machine learning solution

Module 5:

Unsupervised Learning

Clustering algorithms

Full-Immersion 2

Build a real-world machine learning solution

Reviews

- Sebastien Murphy - ETH Zurich, CERN

Machine Learning 1 - May 2019

As a physicist at ETH Zurich based at CERN, I took this course in order to get knowledgeable with tools which are increasingly used in scientific research and acquire experience for the industry. The team is great, they are passionate about the subject and the working atmosphere is really friendly which make the learning process very efficient. The course is well balanced between theoretical explications and hands-on session. The two full immersion days with “real world” data-sets, are especially useful.

Overall I highly recommend this course for anyone getting started with Machine Learning, it will give you solid foundations.

- Jonathan Edwards - So Money

Machine Learning 1 - May 2019

I thoroughly enjoyed the course and would recommend it to anyone wanting to get a solid introduction to machine learning at an unbeatable price point. The instructors managed to balance a relaxed atmosphere with an extremely productive and intense learning experience.

Valerio and Fabio are very cool but don't let that fool you, in a field where it is all too easy to use the techniques incorrectly, not only will they show you the best practices, but their deep understanding of the field means you will actually understand why and when to use them. You will be amazed at the amount you will learn in the space of two months. Their big network in the field in industry is also a definite plus.

FAQs

Should I take this course ?

Yes, you should. Machine learning and data analytics are changing the way things are done in the professional world. This course gives you a unique opportunity to understand this change and start taking part in it. By the end of the course, you will be able to build simple machine learning solutions in your area of expertise, thus making your first steps into the world of advanced analytics.

Can I just enroll in a few modules, instead of taking the full course?

We, typically, do not accept people only for a few modules, as we think that a full grasp of the course is necessary to be able to understand the techniques to apply in the real world. Moreover, by only taking up a selective number of modules, we will not be able to give you the certificate at the end.

Do I need special hardware for this course?

You will need a laptop with Windows, MacOS, or Linux installed. The minimum requirements for the hardware are RAM>2Gb and CPU clock>1.2GHz. Please contact us if you have any doubts about your hardware.

How do I know if this is the right course for me?

This course is an excellent opportunity to understand what people exactly mean by machine learning and how it is applied in fields such as banking, insurance, marketing, and engineering. If you are interested in such topics and in advanced analytics, then please check out the enrollment prerequisites.