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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.
Software professionals
Anyone with a STEM background
Recent college grads
from junior to middle levels, who are looking to build a foundational understanding of AI/ML
who has little to no understanding of Machine Learning and is looking to build a strong foundation in the field
or final-year undergraduates eager to start their journey in AI/ML
Best Suited For
Free Online Course
No prior machine learning experience is necessary — just a solid foundation in Python programming. Embark on your Machine Learning journey with confidence!
Step-by-Step Learning
Transformers & LLMs
Solid foundation
We’ll begin with simple examples and provide clear, step-by-step explanations on training machine learning models. Perfect for beginners!
Discover transformers & LLMs that power recent AI breakthroughs. Gain the knowledge to apply them in practice.
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.
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Why Choose This Course?
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.
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
Lesson 2
Lesson 4
Lesson 5
Lesson 3
Introduction to ML
Hands-on Linear Models
Transformers
ChatGPT
Computer Vision
- General principles of machine learning
- Overview of different areas within machine learning
- Introduction to text classification using a linear classifier
- 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
- 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
- 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
- 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
Get started with the course
FAQ
We'll guide you through linear models, neural networks, and LLMs to help you understand how Machine Learning works inside
We are part of Nebius AI, a European AI-centric public cloud with global expertise. Recognizing the immense potential of ML and AI technologies, we actively encourage and support their study. Our mission is to provide education to anyone eager to start their journey into ML and AI. We’re passionate about sharing knowledge and believe that everyone should have access to the resources needed to explore these cutting-edge fields.
To start the course, fill out the form, and we’ll send you the links to all the lessons to the email you provided.
No prior machine learning knowledge is required.
You are expected to have a solid grasp of Python programming and a high school level of math.
You are expected to have a solid grasp of Python programming and a high school level of math.
The course is designed for fully self-paced learning.
The Machine Learning Essentials for Coders Program consists of Jupyter notebooks hosting both theory and code on Google Colab. In the assignments, you’ll need to fill in some missing code. You can check your answers and find help if you’re struggling in the solution notebooks.
The Machine Learning Essentials for Coders Program consists of Jupyter notebooks hosting both theory and code on Google Colab. In the assignments, you’ll need to fill in some missing code. You can check your answers and find help if you’re struggling in the solution notebooks.
Yes. You will be able to access it even after you complete the program.
Powered by Nebius
Nebius AI, established in late 2023, is a leading AI-centric public cloud platform designed to support the entire machine learning lifecycle. With a focus on empowering ML practitioners, Nebius offers comprehensive infrastructure and aims to become the preferred platform for generative AI developers.
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