Artificial Intelligence & Deep Learning Studies

Artificial Intelligence & Deep Learning course is designed from the ground up to prepare you for a career as an artificial intelligence/deep learning engineer.



Artificial Intelligence & Deep Learning course is designed from the ground up to prepare you for a career as an artificial intelligence/deep learning engineer. The course is extremely practical and hands on – during the course you will create 28 portfolio items. During the course we will dive deep into the best practices of artificial intelligence and deep learning engineering and research while learning advanced Python and required libraries as they are needed. With this approach will never have to memorize some obscure syntax of the library to just forget it in a month – everything that you learn you will immediately put into practice.




Deep Learning is the driving force behind the recent explosion of popularity of Artificial intelligence – one of the most exciting technologies today. A lot of companies are starting to realize the importance of artificial intelligence in the future of their business. It is estimated that AI will greatly affect most of the industries from construction to scientific research to legal. We expect that this will create a huge shortage of AI specialists, who are able to use the most modern AI technologies, mainly deep learning.


The Artificial Intelligence & Deep Learning Course will prepare you for the current practical applications of artificial intelligence and deep learning in businesses. After completing the course you will be able to identify which problems can be solved using deep learning and AI methods, determine the requirements for solving these problems and actually solve the problems. Your area of expertise will include computer vision, natural language processing, time-series analysis, recommender systems and reinforcement learning.


Luminor gives you the opportunity to pay for your tuition in instalments.

You can pay for your studies with the consumer loan, which is suitable for learning, travel and other purposes, and a payment period of 1 to 5 years.

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General Financing gives the opportunity to pay for training by instalments. General Financing ensures highly flexible and convenient payment terms – everyone who wants to pay can lease up to 12 months without any price increases.

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The state can finance training and provide additional compensation during the learning period: a study grant and trips to and from the training location. Contact us to find out about the funding opportunities available to you.

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Career planning


The goal of CodeAcademy is not only to provide knowledge, but also to help you retrain. We organize career workshops for students, in which students analyze market and companies operating in Lithuania as well as plan their future career.
  • CV/Linkedin
  • Portfolio
  • Market analysis

Personal consultations

Each CodeAcademy student gets an individual time to help prepare for a job interview with a future employer.

Career opportunities


  1. Python Crash Course 18 hours

    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.

  2. Numeric Python with Numpy 18 hours

    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.

  3. Tabular Data Analysis with Pandas 18 hours

    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.


  4. Fundamentals of Machine Learning 18 hours

    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.

  5. Introduction to Deep Learning 18 hours

    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.

  6. Regression with Neural Networks 18 hours

    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.

  7. Image Classification 54 hours

    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.

  8. Inverse Image Search 18 hours

    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.

  9. Sequential Data Analysis 18 hours

    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.


  10. Natural Language Processing 36 hours

    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.

  11. Recommender Systems 18 hours

    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.

  12. Generative Deep Learning 18 hours

    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.

  13. Advanced Computer Vision 18 hours

    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.

  14. Capstone Project 36 hours

    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.

Dates & Prices

  • Period
    23 April – 30 August
    720 hours (480 in-class hours)
    18:00 - 22:00
    3500€ if paid upfront, or 100€/mo. if paid in instalments. A possibility of State financing.