Machine Learning

Applied Machine Learning

Get an in-depth, hands-on experience in solving Machine Learning use-cases from a beginner to advanced level. Learn to define projects and their requirements with a particular focus on quality testing of such algorithms.

Apply now
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Part-Time

6

6 weeks

remote

Remote

language

English

Program overview

Machine Learning is one of the fundamental blocks of Data Science. Its methods are being actively applied across various industries. The goal of this course is to teach you how to successfully apply Machine Learning to real-world business problems while avoiding common pitfalls. By the end of the course you will be able to:
  • ● Convert a business problem into a Machine Learning problem.
  • ● Define requirements for a Machine Learning project (including key performance indicators) using an ML canvas.
  • ● Create different types of Machine Learning pipelines (supervised and unsupervised), including data transformation, feature engineering, building a data pipeline, hyper-parameter tuning, loss functions, and cross-validations on several regression and classification tasks.
  • ● Identify bias and fairness of Machine Learning problems and Machine Learning model explainability and interpretability.
  • ● Design and solve several real-world Machine Learning use-cases, e.g., predictive maintenance, churn prediction, customer segmentation.

Upcoming dates

Schedule: Mon & Wed, 16:00 - 19:00 (remote)

Apply by
Course dates
Tuition
22. Oct 21
25. Oct 21 - 01. Dec 21
CHF 1'800

What you'll learn

Weekly schedule

Mo

Tue

Wed

Thu

Fr

Sat

09H00

12H00

13H00

15H40

16H00

17H00

18H00

19H00

Remote
Remote
Remote
Remote
Remote
Remote
Remote

Q&A Session

During these sessions, you are totally free to connect and ask any questions about the covered topics.

Lecture

Learn from our instructors who are experts in their respective fields and get introduced to new topics during live lectures.

Practice

Work on a set of interesting and challenging exercises related to the topics covered in the previous lesson.

Tools we teach

  • Python

  • Jupyter notebooks

  • Pandas

  • Matplotlib

  • Seaborn

  • Scikit-Learn

  • Auto-ML (TPOT, PyCaret, MLJAR)

  • Evidently

  • Flask

  • AWS

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Application process and prerequisites

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

FAQs

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Your instructors

Team Member

Dipanjan Sarkar

Lead Data Scientist & Instructor
Dipanjan (DJ) is a Lead Data Science Consultant & Instructor, leading advanced analytics efforts aro...
Team Member

Albin Plathottathil

Data Science Consultant
Soon after graduating with a Bachelor’s in Computer Engineering, Albin found his interest in working...

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