Workshop Details

ML Summit
The big training event for Machine Learning
October 11 - 13, 2021 | Online April 2022 | Munich

Dr. Pieter Buteneers

en

Bis zum 18. Februar anmelden und bis zu 200 € pro Ticket sparen! Jetzt anmelden
Register until
and save
up to € 100!
Secure your ticket now
November 4
and save
up to €100!
Secure your ticket now

Workshop: Machine Learning 101++ Using Python

Machine learning is often hyped, but how does it work? In this workshop, Pieter Buteneers will show you hands-on how you can build your own machine learning models. We will cover basic machine learning concepts such as regression, classification, over-fitting, cross-validation, and many more. After the workshop, you will go home with the basics of machine learning so you can start off on your own projects.

Throughout the workshop you will learn the basic concepts of machine learning. Each participant will work individually on his or her own computer in a Jupyter notebook which you will be able to access through a URL provided at the beginning of the workshop.The workshop consists of a bunch of exercises around:

  1. The basics of Jupyter notebooks and processing data in Python
  2. The basic concepts of linear regression
  3. Non-linear regression
  4. Over-fitting
  5. Classification
On top of that there are a bunch of optional exercises on:
  1. Time series prediction
  2. Recommender systems
  3. Anomaly detection

The goal of each exercise is to learn the basic concepts behind the topics above. Most of the code will be provided for you so this is not a coding exercise. So if you don’t have a Python background this won’t be an issue. You will only need to implement the essential parts of the code to really understand the concepts. Typically this is just around 5 lines of code per exercise.

At the beginning of each exercise I will introduce you to the task at hand and explain what we are trying to accomplish. During the exercise I will answer all the questions you might have so you don’t need to be stuck on silly coding mistakes. And once the majority of people have finished the exercise I will go over the solutions and I will provide a bit more context to how to apply this in real life tasks.

At the end of the workshop I will provide you with access to the source code of the workshop that contains the solutions and I will finish with a short list of guidelines and best practises in machine learning. So after the workshop you should be ready to tackle machine learning problems on your own.

Workshop Short Label: ML Intro Day

Session Tracks

#ML Conference
Workshop Requirements:

Anybody who can write some code in whatever programming language should be able to follow the workshop. We make use of an iPython/Jupyter Notebook running on a dedicated server, so nothing but a laptop with an internet connection is required to participate.

Throughout the workshop you will learn the basic concepts of machine learning. Each participant will work individually on his or her own computer in a Jupyter notebook which you will be able to access through a URL provided at the beginning of the workshop.The workshop consists of a bunch of exercises around:

  1. The basics of Jupyter notebooks and processing data in Python
  2. The basic concepts of linear regression
  3. Non-linear regression
  4. Over-fitting
  5. Classification
On top of that there are a bunch of optional exercises on:
  1. Time series prediction
  2. Recommender systems
  3. Anomaly detection

The goal of each exercise is to learn the basic concepts behind the topics above. Most of the code will be provided for you so this is not a coding exercise. So if you don’t have a Python background this won’t be an issue. You will only need to implement the essential parts of the code to really understand the concepts. Typically this is just around 5 lines of code per exercise.

At the beginning of each exercise I will introduce you to the task at hand and explain what we are trying to accomplish. During the exercise I will answer all the questions you might have so you don’t need to be stuck on silly coding mistakes. And once the majority of people have finished the exercise I will go over the solutions and I will provide a bit more context to how to apply this in real life tasks.

At the end of the workshop I will provide you with access to the source code of the workshop that contains the solutions and I will finish with a short list of guidelines and best practises in machine learning. So after the workshop you should be ready to tackle machine learning problems on your own.