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AI Learning Path

Practical machine learning — from fundamentals to deployment, designed around GPU computing.

4 phases

🎯Real World Use Cases

Jump to relevant sections

📊Business Analytics

🎨Generate AI Art

🤖Train AI Models

🔬Research & Learning

From the Blog

GPU and LLM pricing updates, provider comparisons, and infrastructure best practices.

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1

Foundations - Getting Your Feet Wet

Build a solid understanding of AI basics and set up your essential coding environment. This phase is crucial for anyone new to the field.

Foundational Videos

Machine Learning for Beginners

A comprehensive introduction that starts from the very basics of what machine learning is, why it's important, and how to get started. Ideal for absolute beginners.

Machine Learning for Beginners

freeCodeCamp

Intro to Machine Learning with Python

Provides a hands-on overview of essential Python libraries (NumPy, pandas, scikit-learn). You'll see how to install them and work through simple ML tasks.

Intro to Machine Learning with Python

sentdex

Neural Networks from Scratch - EXPLAINED!

Grant Sanderson provides an intuitive, visual explanation of neural networks that will give you a strong foundation in deep learning concepts.

Neural Networks from Scratch - EXPLAINED!

3Blue1Brown

2

Deep Learning - Unleashing the Power of Neural Networks

Enter the world of Deep Learning. Explore neural networks, learn to build them with TensorFlow/Keras, and discover their applications in image recognition.

Deep Learning Videos

Neural Networks from Scratch - P.1 Intro and Neuron Code

This series dives into building a neural network from scratch in Python, giving you a deep understanding of how everything works under the hood before moving to higher-level frameworks.

Neural Networks from Scratch - P.1 Intro and Neuron Code

sentdex

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners

A thorough, project-based course that starts with the basics of TensorFlow/Keras and progresses to building various deep learning models, including convolutional networks for image tasks.

TensorFlow 2.0 Complete Course - Python Neural Networks for Beginners

freeCodeCamp

Convolutional Neural Networks (CNN) - EXPLAINED

3Blue1Brown's visual explanations break down the math and intuition behind CNNs, making it an excellent primer before you start coding your own image recognition models.

Convolutional Neural Networks (CNN) - EXPLAINED

3Blue1Brown

3

Advanced Deep Learning - Specialized Architectures and Techniques

Take your Deep Learning skills to the next level. Explore RNNs, Generative AI, and the cutting-edge Transformer architecture.

Advanced Deep Learning Videos

Recurrent Neural Networks (RNN) - EXPLAINED

Visual, intuitive walkthrough of how RNNs handle sequential data (like text and time series). Great for foundational understanding before tackling LSTMs or GRUs.

Recurrent Neural Networks (RNN) - EXPLAINED

3Blue1Brown

Generative Adversarial Networks (GANs) Explained

Quickly grasp the core idea behind GANs—how two networks (generator and discriminator) compete and learn to produce realistic outputs.

Generative Adversarial Networks (GANs) Explained

Two Minute Papers

Transformers Explained - What They Are and How They Work

Provides a straightforward, step-by-step breakdown of the Transformer architecture, covering the attention mechanism and why Transformers outperform traditional RNNs for many tasks.

Transformers Explained - What They Are and How They Work

StatQuest with Josh Starmer

4

Building and Deploying - From Model to Application

Bridge the gap between training and deployment. Learn how to get your models working in the real world and discover best practices for managing the ML lifecycle.

Deployment & MLOps Videos

Best Practices for MLOps with MLFlow, with Zoltan C. Toth

A comprehensive guide to managing the machine learning lifecycle using MLflow, covering experiment tracking, metrics analysis, and model registry for effective lifecycle management.

Best Practices for MLOps with MLFlow, with Zoltan C. Toth

MLflow

Start the machine learning lifecycle with MLOps | CLL99

Learn best practices for creating and managing machine learning models using MLOps processes, covering the entire ML lifecycle from creation to monitoring and incident response.

Start the machine learning lifecycle with MLOps | CLL99

Microsoft Developer

MLOps for managing the end to end life cycle with Azure Machine Learning service

Explore MLOps capabilities in Azure Machine Learning service, with practical demonstrations of asset management and orchestration services for effective ML lifecycle management.

MLOps for managing the end to end life cycle with Azure Machine Learning service

Microsoft Azure

Advice from a robot

Quick-start resources for every stage of your AI journey.

🎓

I want to learn about AI. Where should I start?

🤖

Let's start with some excellent free courses:

🎓

What about books and reading materials?

🤖

Here are some excellent reading resources:

🎓

How can I get hands-on practice?

🤖

Try these platforms for practical experience:

🎓

I want to understand GPU hardware better.

🤖

Here's what you need to know about GPUs:

🎓

What about AI ethics and safety?

🤖

Essential resources for responsible AI:

Ongoing Learning

Stay current with newsletters, communities, and conferences.

Major Conferences

  • NeurIPS— Neural Information Processing Systems
  • ICML— International Conference on Machine Learning
  • ICLR— International Conference on Learning Representations