Why Nebius Academy
Educational programs
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Become an AI-Powered Engineer
A series of comprehensive mini-courses 
on AI-assisted programming
LLM Engineering Essentials
Designed for professionals seeking skills to launch their own LLM-powered projects
Machine Learning Essentials for Coders
Students learn the ML basics at their own pace
Practical Generative AI
Participants develop practical GenAI expertise and elevate their ML careers
In-person
Y-DATA
An intensive data course held at the Tel Aviv University campus
Intro to ML from an LLM standpoint
Ideal for those based in London and eager to explore applications of LLMs
Applied Deep Learning: From Neural Networks to GenAI
A comprehensive exploration of generative models and DL theory
Why Nebius Academy
Educational programs
Online
Become an AI-Powered EngineerLLM Engineering EssentialsMachine Learning Essentials for CodersPractical Generative AI
In-person
Y-DATAIntro to ML from an LLM standpointApplied Deep Learning: From Neural Networks to GenAI
For Business
Contact us
Start Learning
Free Online Course
Machine Learning Essentials for Coders
Immerse yourself in Machine Learning and develop a solid grasp of its basics. We’ll guide you through five clear and straightforward notebooks that introduce you to cutting edge concepts.

No prior machine learning experience is necessary — just a solid foundation in Python programming. Embark on your Machine Learning journey with confidence!
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Best Suited For
Software professionals
from junior to middle levels, who are looking to build a foundational understanding of AI/ML
Anyone with a STEM background
who has little to no understanding of Machine Learning and is looking to build a strong foundation in the field
Recent college grads
or final-year undergraduates eager to start their journey in AI/ML

Why Choose This Course?

1
Step-by-Step Learning
We’ll begin with simple examples and provide clear, step-by-step explanations on training machine learning models. Perfect for beginners!
2
Transformers & LLMs
Discover transformers & LLMs that power recent AI breakthroughs. Gain the knowledge to apply them in practice.
3
Solid foundation
By the end of the course, you’ll have practical skills and a solid foundation in machine learning, ready to take on new projects and challenges.
Key Topics
Basic machine learning principles
Linear classifier
Transformers for texts and images
ChatGPT architecture
CLIP model architecture
OpenAI API, LangChain
HuggingFace libraries: datasets, transformers
Learning process
You will work through Jupyter notebooks on Google Colab that contain both theory and code.
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AI FastTrack: From Basics to GPT

Explore the key topics of our course, organized into modules, each of which covers in detail important aspects of machine learning and artificial intelligence.
Lesson 1
Introduction to ML
  • General principles of machine learning
  • Overview of different areas within machine learning
  • Introduction to text classification using a linear classifier
Lesson 2
Hands-on Linear Models
  • Using Hugging Face datasets
  • Bag-of-words model and text vectorization with scikit-learn
  • Math behind a multiclass linear classifier
  • Gradient descent for parameter optimization
  • Training a linear model with scikit-learn
Lesson 3
Transformers
  • Basics of neural networks
  • Texts tokenization and embeddings
  • Attention mechanism in neural networks
  • Transformers architecture
  • Stochastic gradient descent for neural networks training
  • Training and fine-tuning models with Hugging Face transformers
Lesson 4
ChatGPT
  • Base GPT model is pre-training using self-supervised learning
  • ChatGPT fine-tuning and alignment training
  • In-context learning with ChatGPT
  • Using the OpenAI API for text classification
  • Building applications with large language models using LangChain
Lesson 5
Computer Vision
  • Basics of image classification
  • Vision transformer architecture
  • Principles of transfer learning
  • Training and fine-tuning vision transformers
  • CLIP model architecture
  • Zero-shot image classification with CLIP
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with the course
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