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.
Artificial Intelligence – Advanced
Artificial intelligence involves the development of systems capable of performing tasks that normally require human intelligence. These systems can now mimic routine, non-creative behaviors and automate certain processes. Knowledge of artificial intelligence, which is called the technology of the future, gives a specialist a huge advantage. Artificial intelligence technology with a long enough history is still constantly growing and changing. There are great opportunities in the field of artificial intelligence – after all, it can expand human possibilities in a way that is still difficult to imagine today.
2760€The average salary of an Artificial Intelligence specialist in Lithuania
97%Artificial Intelligence specialists in Lithuania are satisfied with their work
81%Students successfully complete the Artificial Intelligence course
CodeAcademy pasirinkau nes jie aiškiai papasakojo apie kursų krypčių pasirinkimus ir galimybes. Įsiminė kantrūs dėstytojai, kurie visada atsakydavo į kilusius klausimus ir informuodavo, kad kiekvieną problemą galima spręsti keliais būdais.
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Python Crash Course
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.
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.
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.
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.
Mūsų dėstytojų komanda – skirtingų IT specialistų mišinys. Vieni, kaip kokie superdidvyriai 🦸, dienomis
užima Top pozicijas savo įmonėse ir vakarais atsiliepia į studentų pagalbos šauksmus, kiti – dirba kaip freelancer’iai, kasdien žongliruodami tarp klientų bei studentų. Tačiau visi jie 100% pasiruošę žiniomis ir patirtimi padėti tau! 🧑🎓
Gustav von Zitzewitz
Senior Machine Learning Engineer @DataRobot
Machine Learning Engineer @ZF Group
8 May - 14 June
18:00 - 22:00 EET
Price from 4200 € or from 50 € / month by instalments.Skaičiuoklė
Mes siūlome tiek skirtingų išsimokėjimo galimybių ir lengvatų, kad sukūrėme skaičiuoklę savo galimybes pasiskaičiuoti pačiam – visai kaip banke. 💸
- Nuo 50 €/mėn.
100% Užimtumo Tarnybos finansavimas
- Užimtumo Tarnybos finansavimas leidžia persikvalifikuoti dirbantiems ir įgyti naujas kompetencijas nedirbantiems!
- Besimokantiems studentams gali būti skiriamos UžT stipendijos, Daugiau informacijos čia.
Mokėk po sėkmingo įsidarbinimo!
- Mėnesinis mokestis – 10% nuo Neto pajamų, su galimybe turėti mokėjimo atostogas iki 5 mėnesių.