AI Learning Path

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

4 phases

🎯Real World Use Cases

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From the Blog

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

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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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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:

Fast.ai's Practical Deep Learning — Perfect for hands-on learners
Andrew Ng's ML Course — Great for fundamentals
DeepLearning.AI — Structured paths for different specializations

🎓

What about books and reading materials?

🤖

Here are some excellent reading resources:

Dive into Deep Learning — Interactive book with code examples
Microsoft's ML for Beginners — 12-week curriculum

🎓

How can I get hands-on practice?

🤖

Try these platforms for practical experience:

Kaggle — Competitions, datasets, and community
Hugging Face Courses — Learn to use state-of-the-art models

🎓

I want to understand GPU hardware better.

🤖

Here's what you need to know about GPUs:

NVIDIA CUDA Programming Guide — GPU architecture and CUDA programming
GPU Architecture Fundamentals — Stanford course on parallel computing

🎓

What about AI ethics and safety?

🤖

Essential resources for responsible AI:

Anthropic's AI Safety Guide — Core principles
Alignment Forum — Technical discussions
Fast.ai Ethics — Practical guidelines

Ongoing Learning

Stay current with newsletters, communities, and conferences.

Stay Updated

Communities

Major Conferences

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

Foundational Videos

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

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

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

Deep Learning Videos

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

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

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

Advanced Deep Learning Videos

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

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

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

Deployment & MLOps Videos

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

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

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:

Fast.ai's Practical Deep Learning — Perfect for hands-on learners
Andrew Ng's ML Course — Great for fundamentals
DeepLearning.AI — Structured paths for different specializations

🎓

What about books and reading materials?

🤖

Here are some excellent reading resources:

Dive into Deep Learning — Interactive book with code examples
Microsoft's ML for Beginners — 12-week curriculum

🎓

How can I get hands-on practice?

🤖

Try these platforms for practical experience:

Kaggle — Competitions, datasets, and community
Hugging Face Courses — Learn to use state-of-the-art models

🎓

I want to understand GPU hardware better.

🤖

Here's what you need to know about GPUs:

NVIDIA CUDA Programming Guide — GPU architecture and CUDA programming
GPU Architecture Fundamentals — Stanford course on parallel computing

🎓

What about AI ethics and safety?

🤖

Essential resources for responsible AI:

Anthropic's AI Safety Guide — Core principles
Alignment Forum — Technical discussions
Fast.ai Ethics — Practical guidelines

Ongoing Learning

Stay current with newsletters, communities, and conferences.

🎯Real World Use Cases

📊Business Analytics

🎨Generate AI Art

🤖Train AI Models

🔬Research & Learning

From the Blog

Foundations - Getting Your Feet Wet

Introduction to AI: What, Why, and How

Python for Data Science: The Essential Toolkit

Introduction to Machine Learning Algorithms: Your First Models

Foundational Videos

Deep Learning - Unleashing the Power of Neural Networks

Neural Networks Fundamentals: Building Blocks of Deep Learning

TensorFlow and Keras: Your Deep Learning Frameworks

Convolutional Neural Networks (CNNs): Mastering Image Data

Deep Learning Videos

Advanced Deep Learning - Specialized Architectures and Techniques

Recurrent Neural Networks (RNNs): Understanding Sequential Data

Generative AI: Creating New Data

Transformers: The State of the Art in NLP (and Beyond)

Optimizing for GPU Performance

Advanced Deep Learning Videos

Building and Deploying - From Model to Application

Model Deployment: Getting Your Model into the World

MLOps: The DevOps of Machine Learning

Project Portfolio: Showcasing Your Skills

Deployment & MLOps Videos

Advice from a robot

Ongoing Learning

Stay Updated

Communities

Major Conferences

🎯Real World Use Cases

📊Business Analytics

🎨Generate AI Art

🤖Train AI Models

🔬Research & Learning

From the Blog

Foundations - Getting Your Feet Wet

Introduction to AI: What, Why, and How

Python for Data Science: The Essential Toolkit

Introduction to Machine Learning Algorithms: Your First Models

Foundational Videos

Deep Learning - Unleashing the Power of Neural Networks

Neural Networks Fundamentals: Building Blocks of Deep Learning

TensorFlow and Keras: Your Deep Learning Frameworks

Convolutional Neural Networks (CNNs): Mastering Image Data

Deep Learning Videos

Advanced Deep Learning - Specialized Architectures and Techniques

Recurrent Neural Networks (RNNs): Understanding Sequential Data

Generative AI: Creating New Data

Transformers: The State of the Art in NLP (and Beyond)

Optimizing for GPU Performance

Advanced Deep Learning Videos

Building and Deploying - From Model to Application

Model Deployment: Getting Your Model into the World

MLOps: The DevOps of Machine Learning

Project Portfolio: Showcasing Your Skills

Deployment & MLOps Videos

Advice from a robot

Ongoing Learning

Stay Updated

Communities

Major Conferences