AI in Practice

AI in Practice, available at Free, has an average rating of 4.33, with 12 lectures, based on 3 reviews, and has 645 subscribers.

You will learn about 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 This course is ideal for individuals who are Engineers who want to learn how to quickly use AI in real-world projects or Engineers who want to use their skill to do good or Engineers that want to built a portfolio with real-world AI for Good Challenges or Challenge Based Learners It is particularly useful for Engineers who want to learn how to quickly use AI in real-world projects or Engineers who want to use their skill to do good or Engineers that want to built a portfolio with real-world AI for Good Challenges or Challenge Based Learners.

Enroll now: AI in Practice

Summary

Title: AI in Practice

Price: Free

Average Rating: 4.33

Number of Lectures: 12

Number of Published Lectures: 12

Number of Curriculum Items: 12

Number of Published Curriculum Objects: 12

Original Price: Free

Quality Status: approved

Status: Live

What You Will 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

Who Should Attend

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

Target Audiences

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

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.

Course Curriculum

Chapter 1: Introduction to AI

Lecture 1: Introduction to AI

Lecture 2: Training classic ML

Lecture 3: Deep Learning

Chapter 2: Developer skills

Lecture 1: Linux servers and SSH

Lecture 2: Git

Lecture 3: Virtualization

Chapter 3: Data Exploring & Engineering

Lecture 1: Data encoding

Lecture 2: Merging & Pandas profiling

Lecture 3: Outliers & Missing values

Chapter 4: AI pitfalls and biases

Lecture 1: Pitfalls; Signal + Noise

Chapter 5: Transfer learning and AutoML

Lecture 1: What is Transfer Learning

Lecture 2: Transfer Learning applied

Instructors

Sako Arts
CTO @ FruitPuch AI

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4 stars: 3 votes

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Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!