All Courses & Projects

Career tracks

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TEST TRACK 12

Beginner

Full Stack Web Development

Beginner

Become a React Developer

Intermediate

Mastering Data Visualization

Intermediate

SQL from Zero to Hero

Beginner

С++ Essentials

Beginner

Python from Zero to Hero

Beginner

Machine Learning Mastery

Intermediate

Python Beyond Intermediate

Beginner

Java Essentials

Beginner

Game Development with Unity

Beginner

Become a Django Developer

Advanced

Flask for Dummies

Intermediate

Frontend Development Foundations

Beginner

Web Developer from Zero to Hero

Beginner

Preparation for Data Science

Intermediate

Deep Learning Odyssey

Advanced

Web Development with C#

Beginner

TEST E2E TRACK BEGINNER

Beginner

Test Track

Skilled Python BackEnd Developer

Advanced

Test Recalculate

Beginner

Excel Essentials

Beginner

Full-Stack .NET Developer Journey

Intermediate

Full-Stack .NET Developer Journey

Intermediate

Test track with rating

Advanced

TEST TEST TRACK

0 Task

Beginner

track

Only for Ultimate

Data Analyst Foundation

Beginner

C++ Mastery

Advanced

Java Web

Advanced

Become a QA Engineer

Beginner

Video Production with Adobe

Beginner

UI/UX Design Tools

Beginner

Test image upload track

Beginner

Essential Office Skills

Beginner

GitHub from Zero to Hero

Beginner

No-Code Website Development

Beginner

C Programming & Low-Level Development

Beginner

testes

Beginner

Test translation track

Intermediate

Test CMS Track

Beginner

Python for Data Analysis

Intermediate

Digital Marketing Essentials

Beginner

Complete Social Media Management

Beginner

Business AI Toolkit

Beginner

Courses & Projects

Recognizing Handwritten Digits

In this project, our primary objective will be to delve into the identification of handwritten digits through the application of machine learning algorithms. This endeavor aims to harness the power of machine learning to effectively interpret and understand handwritten digits, showcasing the potential of these algorithms in processing and analyzing complex visual information.

Introduction to Neural Networks

Neural networks are powerful algorithms inspired by the structure of the human brain that are used to solve complex machine learning problems. You will build your own Neural Network from scratch to understand how it works. After this course, you will be able to create neural networks for solving classification and regression problems using the scikit-learn library.

Identifying Fake News

In this project, we are going to classify news and identify the fake ones.

ML Introduction with scikit-learn

Machine learning is now used everywhere. Want to learn it yourself? This course is an introduction to the world of Machine learning for you to learn basic concepts, work with Scikit-learn – the most popular library for ML and build your first Machine Learning project. This course is intended for students with a basic knowledge of Python, Pandas, and Numpy.

Association Rule Mining

The Association Rule Mining course offers a comprehensive exploration of the principles and methodologies behind uncovering meaningful associations in large datasets. From understanding the fundamental measures like support, confidence, and lift to employing advanced algorithms such as Apriori and FP-Growth, you will develop the skills necessary to extract valuable insights from transactional data. Through practical applications in diverse domains like retail, healthcare, and finance, participants learn to drive data-driven decision-making, optimize business processes, and uncover hidden opportunities for growth and innovation.

Introduction to NLP

Explore the fundamentals of Natural Language Processing (NLP) by learning essential text preprocessing techniques and methods for representing text data. Gain practical experience with the tools used to clean, analyze, and interpret textual information. Develop the skills needed to transform raw language into structured insights, laying a strong foundation for advanced applications in artificial intelligence and machine learning.

Image Synthesis Through Generative Networks

Discover the artistry behind image generation using generative networks in our course. From theory to practice, delve into the world of generative networks, unlocking the secrets to crafting captivating images from data.

Classification with Python

In machine learning, classification is used in predictive modeling to assign input data with a class label. Sounds difficult? Don't worry! Let's cope with this! Welcome to the ML!

Cluster Analysis

Gain a solid understanding of cluster analysis, a key unsupervised learning technique for uncovering patterns in unlabeled data. Explore the essentials of K-Means, Hierarchical Clustering, DBSCAN, and GMMs, and get hands-on experience with real datasets to build confidence in applying clustering to real-world problems.

Data Anomaly Detection

Anomaly detection is integral to any data scientist's work: high-quality, cleaned, and well-prepared data is the key to success for almost any machine learning problem.

Ensemble Learning

Ensemble Learning is an advanced machine learning technique that combines multiple models to improve overall predictive performance and decision-making when solving real-life tasks.

Logistic Regression Mastering

Logistic regression is a statistical method for analyzing a dataset in which there are one or more independent variables that determine an outcome. In this project, we will leverage logistic regression to classify data into two distinct categories. The process will involve data preprocessing, model training, evaluation, and tuning to achieve optimal performance.

Logistic Regression Mastering

In this project, we are going to understand the career tracks of Data Scientists.

Data Science Interview Challenge

Ready to try your hand at data science? This course is designed to challenge your existing knowledge and hands-on skills, ensuring you are fully prepared for any twists and turns a data science interview might present. We'll push your understanding of critical topics to the limit, assessing your readiness for real-life scenarios.

Introduction to Big Data with Apache Spark in Python

This course will help those who want to get some of Big Data basics, including different types of distributed computings and such programming paradigm as MapReduce. Also, main part of the course will be devoted to such framework as Apache Spark and it's high-level API PySpark using Python programming language.

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Career tracks

track

Only for Ultimate

TEST TRACK 12

Beginner

Full Stack Web Development

Beginner

Become a React Developer

Intermediate

Mastering Data Visualization

Intermediate

SQL from Zero to Hero

Beginner

С++ Essentials

Beginner

Python from Zero to Hero

Beginner

Machine Learning Mastery

Intermediate

Python Beyond Intermediate

Beginner

Java Essentials

Beginner

Game Development with Unity

Beginner

Become a Django Developer

Advanced

Flask for Dummies

Intermediate

Frontend Development Foundations

Beginner

Web Developer from Zero to Hero

Beginner

Preparation for Data Science

Intermediate

Deep Learning Odyssey

Advanced

Web Development with C#

Beginner

TEST E2E TRACK BEGINNER

Beginner

Test Track

Skilled Python BackEnd Developer

Advanced

Test Recalculate

Beginner

Excel Essentials

Beginner

Full-Stack .NET Developer Journey

Intermediate

Full-Stack .NET Developer Journey

Intermediate

Test track with rating

Advanced

TEST TEST TRACK

0 Task

Beginner

track

Only for Ultimate

Data Analyst Foundation

Beginner

C++ Mastery

Advanced

Java Web

Advanced

Become a QA Engineer

Beginner

Video Production with Adobe

Beginner

UI/UX Design Tools

Beginner

Test image upload track

Beginner

Essential Office Skills

Beginner

GitHub from Zero to Hero

Beginner

No-Code Website Development

Beginner

C Programming & Low-Level Development

Beginner

testes

Beginner

Test translation track

Intermediate

Test CMS Track

Beginner

Python for Data Analysis

Intermediate

Digital Marketing Essentials

Beginner

Complete Social Media Management

Beginner

Business AI Toolkit

Beginner