AI in Practice
AI in Practice, Learn how to apply AI in the real world!
The AI in Practice Bootcamp consists of 5 chapters that are divided into 3 or 4 video lectures per chapter and 1 Capstone project. Every video lecture comes with a corresponding notebook with exercises. You will work your way through the videos and notebooks and learn the essentials of using AI on Real-World datasets.
Content
We will tackle problems that occur to AI engineers and data scientists in their everyday work, and prepare you for the real world!
Requirements:
Since we will be diving deeper into the practicalities of AI, participants need some background in programming. Don’t worry, this is merely basic Python knowledge, no significant data science skill is required.
You will consume knowledge in the form of lectures, assignments, and a Capstone project. The first 5 lessons will be dedicated to video lectures and assignments. An assignment will take up between 2-3 hours of your time. Once you joined the Bootcamp you’ll be added to the Slack Channel, where you can ask questions to our mentors.
After the lectures and assignments, you are ready to head out into the wild. You will choose a real-world AI problem to tackle.
We have 5 very exciting topics in store for you:
Introduction to AI: In this introductory session we will go over the history and introduce you to the rapidly changing field of artificial intelligence.
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.
Data Exploring & Engineering: 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. Followed by tips and tricks for wrangling, merging, and parsing your data to create usable datasets.
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.
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.
