Applied Machine Learning

Machine Learning is one of the fundamental blocks of Data Science. Its methods are being actively applied across various industries, (ex: pharma, insurance, finance, marketing, and manufacturing (IoT data)). However, most Machine Learning projects fail. For this reason, the goal of this course is to teach you how to successfully apply Machine Learning to business problems while avoiding common pitfalls. Defining and solving data problems using Machine Learning remains a considerable challenge. In this course, not only will you get an in-depth, hands-on experience in solving Machine Learning use-cases from a beginner to advanced level, you will also be able to define Machine Learning use-cases and their requirements with a particular focus on quality testing of such algorithms. This course is for anyone with a strong interest in Data Science with beginner to intermediate Python programming experience.

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Program Overview

You will start with an introduction on how to link business to data, and data to Machine Learning, followed by a deep-dive into Supervised and Unsupervised Machine Learning algorithms. Over 6 weeks, you will work on several use-cases and acquire experience on the following topics:

1. How to convert a business problem into a Machine Learning problem?
2. How to define requirements for a Machine Learning project (including key performance indices) using an ML canvas?
3. In-depth, hands-on experience in creating different types of Machine Learning pipelines (supervised vs. unsupervised), including data transformation, feature engineering, building a data pipeline, hyper-parameter tuning, loss functions, and cross-validations on several regression and classification tasks
4. Hands-on experience in identifying bias and fairness of Machine Learning problems and Machine Learning model explainability
5. Designing and solving several Machine Learning use-cases, (ex: predictive maintenance, churn prediction, customer segmentation).

Schedule:

  • ● This remote course runs for 6 weeks on Monday and Wednesday from 16:00 to 19:00.
  • ● Every week will contain lecture and exercise modules designed to give you as much hands-on experience as possible.

What you'll learn

Tuition

CHF 1'800

Next Early Apply discount deadline is 28. Feb 21

Reserve your spot in advance and get CHF 300 off tuition.

Upcoming Dates

Part-Time Online Programs

Schedule
Apply by
Start
End
Mon & Wed: 16:00 - 19:00
26. Mar 21
29. Mar 21
5. May 21

Application Process

Simply apply to the program here. This course is suitable for beginners and intermediate Python programmers.

FAQs

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Your Instructors

Team Member

Dr. Nitin Kumar

COO
As a Data Scientist with more than a decade of experience in data mining, pattern recognition, and p...
Team Member

Badru Stanicki

Data Science Program Manager & Instructor
With a Masters in Physics, Badru got into scientific programming and data science during his time at...
Team Member

Dipanjan Sarkar

Instructor
Dipanjan (DJ) is a Data Science Lead at Applied Materials, leading advanced analytics efforts around...
Team Member

Patrick Senti

Freelance Analytics Consultant
Patrick has been building analytics solutions since 1995, applying machine learning, data engineer...

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About Us

Our motto is {CodeHard: PlayHard}

We aspire to boost Switzerland’s startup scene and tech industry by educating the next generation of Data Scientists, Full-Stack Developers, and Big Data Engineers.

We believe that the latest technologies should be accessible to everyone. We create learning environments that are inspiring and overcome doubts of the impossible. Everyone has the ability to learn tech skills, which is why we want to guide motivated and talented people towards the future of their dreams.

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