FruitPunch AI Bootcamp - AI in practice


We are hosting another AI Bootcamp! This time everything will be in the light of AI in practice. We will tackle problems that occur to AI engineers and data scientists in their everyday work, and prepare you for the real world!

Over the duration of 8 weeks (27 September till 22 November), you will consume knowledge in the form of lectures, assignments, and a 2-week Capstone project. The first 6 weeks will be dedicated to the lectures and challenges. Every Monday from 19:00-20:00 CEST, one of our AI engineers will give a live lecture. During the week you will come together with your subgroup (~4 people) to partake in the challenges that are given to you at the end of each lecture. A challenge will take up between 2-3 hours of your time. On Thursdays from 12:30-13:30 CEST, there will be an extra mentor hour where you will be free to ask questions.

After 6 weeks you are ready to head out into the wild. With your group, you will choose a real-world AI problem to tackle. You will have two weeks to finish your project and present your results to our Community!


Since we will be diving deeper into the practicalities of AI, participants need some basic background knowledge. But not to worry this is merely basic Python knowledge, no significant data science skill is required. If you want to test if you are ready to join this bootcamp you can make the following intake notebook and send us the results.


We have 6 very exciting lectures in store for you:

  1. Introduction to AI: In this introductory session we will go over the rapidly changing field of artificial intelligence and show what model families work well on what type of data. 
  2. Developer skills: Here, you will learn about computer basics, working with servers, and putting models in production. Which are very relevant but often forgotten skills of a data scientist.
  3. Exploratory Data Analysis: No data scientist should ever start working before exploring their data. In this lecture, we take you through all the essential steps before you start processing.
  4. Data Engineering: Often overlooked when working with ready-to-use datasets. But in the real world a necessary skill to possess. Here we take you through the hassle of wrangling, merging, and parsing your data to create usable datasets.
  5. AI pitfalls and biases: Ever trained a model that seemed too good to be true? It probably was. We will explain how to avoid common pitfalls! Furthermore, we dive into the growing field of fairness and bias and learn how to detect and mitigate biased data.
  6. Transfer learning and AutoML: Standing on the shoulders of giants. With pre-trained models with hundreds of layers laying around, why train your own?  Transfer learning and AutoML will take the work out of your hands. Learn to utilize this technology.


The applicants have to present the results of the projects in the 8th session. Certificates will be distributed at the end of the Bootcamp.

The deadline for registration is 22 September 23:59 CEST

Register here