1+ 2 level

Artificial Intelligence

Artificial intelligence involves the creation of systems capable of performing tasks that typically require human intelligence. These systems can now mimic routine, non-creative behavior and automate certain processes. The knowledge of artificial intelligence, titled as the technology of the future, provides a tremendous advantage to a specialist. Despite having a rather long history, artificial intelligence technology continues to grow and evolve rapidly. Within the realm of artificial intelligence lies tremendous potential – it has the capability to expand human capabilities in ways that are difficult to fathom today.

  • 6047€
    The average salary of an Artificial Intelligence specialist in Europe
  • 97%
    Artificial Intelligence specialists in Europe are satisfied with their work
  • 81%
    Students successfully complete the Artificial Intelligence course

Employment opportunities


Ernst Frei

RPA DevOps Engineer @Telia

I chose CodeAcademy because it had clear communication about the choices and possibilities of the course directions. Thank you to the lecturers who were patient, always answered the questions and informed that each problem can be solved in several ways.


6 months
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Python Crash Course

We will start the course with Python crash course. We will ensure that every student has the basic Python knowledge required to proceed with the course. We will cover the language syntax, iterators, generators, comprehensions, object-oriented programming patterns, algorithms and data structures.

Numeric Python with Numpy

In this section we will learn how to handle numeric information in Python using Numpy library. We will about two of the most important data science concepts – code vectorization and broadcasting as well as Numpy array methods and operations.

Tabular Data Analysis with Pandas

In this part of the course we will learn how to use Pandas library to work with tabular data. We will learn how to create, write, read and index Pandas dataframes. We will also learn dataframe methods and how to use them for analysing and visualizing tabular data.

Fundamentals of Machine Learning

In this section we will learn the fundamentals of machine learning. We will focus on random forests – one of the most powerful and versatile machine learning algorithms. We will also learn how to explore your data, validate your models, handle missing values and other machine learning essentials.

Introduction to Deep Learning

In this section we will learn the basics of deep learning. We will learn about the types of neural networks, activation functions, loss functions and optimizers. We’ll also spend some time learning about the current applications of deep learning in artificial intelligence and why they are behind the current artificial intelligence revolution.

Regression with Neural Networks

In this part of the course we will move our focus to structured data, which is extremely important in business, but often neglected in most of the deep learning courses. We will do a portfolio project classifying a binary variable.

Image Classification

In this section we will start tackling the most important and the most useful application of artificial intelligence – computer vision. We will concentrate our attention to convolutional neural networks. The main focus of this section are the portfolio projects: you will build image classifiers with vastly different architectures, formats and number of classes. While working on the projects you’ll learn the most advanced architectures, and will practice the most modern training methods.

Inverse Image Search

In this section of the course we will be diving deeper into computer vision and build a reverse image search model capable of finding similar items to the one provided by the user. This project will help us understand the underlying meaning of the weights in the deep learning models and prepare us for the natural language processing and recommender systems sections.

Sequential Data Analysis

Finally it is time to make some money! We will try to predict stock market movements using recurrent neural networks. While working on this portfolio project we will learn the differences between recurrent neural networks, long short-term memory networks and gated recurrent units, when to use each of those architectures and their strengths and weaknesses.

Natural Language Processing

In this section we will learn how neural networks learn the representations of natural language. While natural language processing (NLP) is totally new to us, we will use the familiar recurrent neural networks to tackle this problem. We will learn the most important NLP concepts and use them to create two NLP portfolio projects.

Recommender Systems

In this section of the course we will build a recommender system. While not new, recommender systems saw a huge improvement in accuracy with the coming of the deep learning models. While working on the recommender systems we will learn about embeddings and collaborative filtering.

Generative Deep Learning

In this section we will return to computer vision once again. We will learn about generative deep learning models and create a convolutional neural network capable of generating images aka deep dreaming.

Advanced Computer Vision

In this section we will focus on the advanced computer vision topics such as object detection and segmentation. You will learn how to build and apply state of the art computer vision algorithms.

Capstone Project

During the final part of the course you will work on your capstone project. You will be able to apply everything that you learned during the course to create a great AI project. While you are working on the project we will also review your Github portfolio, LinkedIn profile and conduct mock interviews to prepare you for getting a job as a deep learning/machine learning/artificial intelligence engineer.

Soft skills

CV; LinkedIn; job interview workshops; IT professional competences.


Our team of lecturers is a mixture of different IT specialists. Some, like superheroes 🦸, take Top positions in their companies during the day and respond to student calls in the evenings, while others work as freelancers, juggling between clients and students on a daily basis. But they are all 100% ready with the knowledge and experience to help you!🧑‍🎓

Artificial intelligence

Fabio Ferreira

Machine Learning Engineer @ZF Group

Artificial intelligence

Gustav von Zitzewitz

Senior Machine Learning Engineer @DataRobot

Course calendar


27 May - 25 November


18:00 - 22:00


504 hours




Payment options

We offer so many different payout options and benefits that we have created a fee calculator for you to calculate your abilities yourself - just like in a bank. 💸

CodeAcademy Financing

  • Pay by installments for all courses – ONLY 100 €/month

Income share agreement

  • Pay 10% of your Net monthly Income / per month only AFTER SECURING  your dream career!

Full price

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