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Free Nebius Academy course
Intro to ML from an LLM standpoint
In-person in London
14 weeks
3 hours a week
From industry experts
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With guidance from experienced professionals, learn applications of large language models, get a grasp on machine learning methodology, and find out what’s inside ML models and how to benefit from them.
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This course is best suited for
Software engineers and developers
Deepen your knowledge to get a chance to move into AI/ML Engineer roles.
Data scientists
Master ML and LLMs to explore the opportunities the field offers.
Study & exercise to apply new concepts
Up-to-date theory
Our instructors are here to answer all your questions. After each lecture, there will be a discussion to clarify and examine the course content in detail.
Practice
Home assignments won’t be graded, but our experts will talk them through in class.
In-person sessions
Join us at a convenient space in London (11 Cavendish Square, Chandos St, London W1G 9EB).
What will you learn?
This free course covers the basics of machine learning (ML), focusing on large language models (LLMs).
Skills you'll gain:
Creating, comparing, and using ML models in real-world situations
Leveraging a broad range of ML architectures: from linear models to transformers
Getting value from LLMs for your projects
Curriculum
Nov 5th — Feb 11th
1.
What is ML and how LLMs bring so much value
2.
Comparing models: LLM zoo and ML metrics
3.
What’s on top of an LLM: logits and linear models
4
How do we actually train ML models: gradient descent
5.
Geometry vs Semantics: from metric classification to Retrieval-Augmented Generation (RAG)
6.
Understanding neural networks
7.
Fine-tuning a pre-trained model
8.
Neural networks for sequences
9.
Attention and transformers
10.
Decoder-only transformers and how to train an LLM
11.
Intro to LLMOps: deploying an LLM, quantization, LoRA
12.
Architectural design choices that shape today’s LLM landscape
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Our instructors are industry experts
Stanislav Fedotov
AI and Education Expert, AI Content Lead at Nebius Academy
Stanislav started his career as a mathematician, but later switched to creating and managing educational projects in Data Science.
LinkedIn
Tatiana Gaintseva
Researcher, Methodologist, PhD Student at Queen Mary University of London
Tatiana is doing her PhD in AI in Computer Vision. Experienced in AI education, she is currently working on research related to the interpretability of multi-modal models.
LinkedIn
Karina Zainullina
Research Engineer (LLMs)
at Nebius
Machine Learning Engineer with deep passion and experience in NLP, specializing in generative LLMs pretraining and fine-tuning.
LinkedIn
Join our supportive community
Fellow learners and experts will help you with ML skills and concepts, share tips and best practices, and stay connected with you even after you graduate.

We use Slack to chat, network, and learn together: enroll to access the group and all the community benefits we offer.
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