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AI in Practice
Rating: 4.3 out of 5(35 ratings)
1,464 students

AI in Practice

Learn how to apply AI in the real world!
Created bySako Arts
Last updated 4/2024
English
English [Auto],

What you'll learn

  • How to use AI in Pracktice
  • The basics of how to setup AI experiments and training pipelines
  • The developer skills required to deploy AI solutions and APIs
  • How to explore a and analyise a real data set and what to look out for (EDA)
  • What pitfalls to look out for and how to circumvent biases
  • Using State of the Art AI without being an expert (zero-shot and transfer learning)
  • How to solve AI projects with a sustainable mindset

Course content

5 sections • 12 lectures • 1h 57m total length

  • Introduction to AI11:24

    Understand artificial intelligence, machine learning, and deep learning, and how neural networks drive data-driven solutions. Learn about supervised and unsupervised learning, including classification, regression, clustering, and apps like spam filters.

  • Training classic ML10:05

    Discover classical machine learning with sklearn, covering linear models, trees, ensembles, and proper training practices—train-test splits, encoding, and key metrics for classification and regression.

  • Deep Learning9:16

    Learn how deep learning uses neural networks and layers with activation and loss functions, training via backpropagation and sgd, and architectures like cnns, rrns, and transformers for real-world ai tasks.

Requirements

  • Basic programming skills required, preferebly in python

Description

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.

Who this course is for:

  • Engineers who want to learn how to quickly use AI in real-world projects
  • Engineers who want to use their skill to do good
  • Engineers that want to built a portfolio with real-world AI for Good Challenges
  • Challenge Based Learners

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