Artificial Intelligence (AI)

This module builds upon the basics by introducing object oriented programming. Such as classes, methods, inheritance, and other various functionality related to objects

The module continues expanding on Object oriented programming with polymorphism abstraction and other object oriented principles

After learning Python Basics we move on towards better understanding of Python, with topics such as work with files and catalogues, allowing students to learn how to keep data persistently and teaching students how to handle exceptions. We also briefly introduce generators, iterators and decorators

To further expand the understanding of students we will introduce them to GitHub, so they are able to save their projects and work in teams, this module also involves a group assignment from previous topics while using GitHub. Also involves short introduction to pip and how to use it

We introduce the students to basic concept of what is a database, how to set it up and how to use SQL queries to manipulate data

This module expands on first one, by introducing more advanced SQL queries such as joins, aggregation and other concepts

In this module students learn how to implement databases to their existing Python projects and how to use ORM software such as SQL alchemy in order to reach their database and work with it directly. Also includes introduction to Alembic and migrations

Involves introduction to Flask and web development. This module contains crash course on HTML and CSS

Students continue working with flask, combining databases, objects, and everything they learned up to this point in order to complete their final project before starting with AI modules

Students start with learning the reasoning behind data analysis and preprocessing. They also are introduced to NumPy and pandas libraries

Students are introduced to libraries such as matplotlib and seaborn

Students are introduced to machine learning, and what it entails. And are thought the main keywords and concepts. This module involves learning what is SL, unsupervised learning, what is recursion, classification and various other topics

Students Dive deeper into SL, learning the most basic algorithms such as K-NN, SVM, Decision trees and various other simple algorithms

Students learn about Unsupervised learning methods such as PCA and K-means, and what is clusterization and why unsupervised learning is needed

Students are briefly introduced to Reinforcement learning it’s advantages and disadvantages, and how it can be applied to real life problems

Students are introduced to Neural Networks, such as FFN, CNN, RNN. And their subtypes and purpose of each of the type. Concepts such as backpropagation, gradient and various other must know knowledge for deep learning

Students deepen their knowledge of neural networks, while learning about ResNets, LSTM, GRU algorithms

Students have to apply everything that they learned on the final project involving databases basic python knowledge and deep Learning in order to make a new model, that would solve the problem of their own choosing

CV, LinkedIn, job interview workshops, individual activities and fees, IT specialist competencies