Artificial Intelligence (AI)

The more advanced part covers language syntax, iterators, generators, object-oriented programming principles, algorithms, and data structures

Digital Information Processing using Python with the NumPy library. Concepts of data science including vectorization and broadcasting of code, as well as NumPy array methods and operations

Pandas library when working with tabular data. Creating, writing, reading, and indexing Pandas DataFrame. Methods of data frames, how to use them for analyzing and visualizing tabular data

Covers the basics needed to start using machine learning models, such as handling missing values, validation, colleration, picking the right data for the right tasks. And the most basic of models, such as k-NN

Covers basic machine learning models such as decision trees, random forests, support vector machines, and other simple machine learning models, this part helps students to grasp how machine learning works, and prepares them for more advanced topics such as neural networks

Machine learning algorithms. Data exploration, model validation, handling missing values, and other aspects of machine learning. Fundamentals of deep learning. Types of neural networks, activation functions, loss functions, and optimizers. Current applied applications of deep learning in the field of artificial intelligence

Structured data, which is extremely important in bussines but often overlooked in many deep learning courses. Completion of a portfolio project classifying a binary variable

Students learn how to work with convolutional neural networks, grasping how are images made, and how to transform them into numerical data and what kind of other transformations (such as converting from RGB to gray scale) are needed in order for the convolutional networks to understand and process the information correctly

Computer vision, convolutional neural networks. The main focus of this section is on portfolio projects: you will create image classifiers with different architectures, formats, and numbers of classes. Working on projects, you will learn advanced architectures and practice the latest training methods

Computer vision and creating a reverse image search model that can find similar elements to what the user provides. This project will help understand the fundamental importance of embeddings in deep learning models and prepare for the sections on natural language processing and recommendation systems

Stock market movements using recurrent neural networks. Difference in recurrent neural networks, short-term memory networks, persistent recurrent blocks

Dive into how neural networks learn representations of natural language. While natural language processing (NLP) is an entirely new domain, you will leverage familiar recurrent neural networks to tackle this challenge. You will learn key NLP concepts and apply them by creating two NLP portfolio projects

Building recommendation systems. Working with recommendation systems, you will learn about embeddings and collaborative filtering

You will learn about generative deep learning models and create a convolutional neural network capable of generating images, commonly referred to as deep dreams

Advanced computer vision topics: object detection and segmentation. You will learn how to create 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 have the opportunity to apply everything you have learned throughout the course in creating your own AI project. While working on the project, we will review your Github portfolio, LinkedIN profile, and conduct mock interviews to prepare you for deep learning/machine learning/artificial intelligence engineer roles

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

Information search with AI; Answers generation with AI