Artificial Intelligence (AI) continues to evolve at a rapid pace, influencing various industries and reshaping the way we interact with technology. As we step into 2024, the demand for AI skills is higher than ever. Whether you’re a beginner or a seasoned professional looking to expand your knowledge, having a structured roadmap can make the learning journey more effective and manageable. This guide outlines a comprehensive roadmap to learn AI in 2024, covering essential skills, tools, and resources.
Top-down approach
This curriculum follows a top-down approach (code first, theory then).
I like to learn by necessity. So, when I need to find something, solve a problem or create a prototype, I search extensively for the information I need, study it, understand it and act accordingly.
For example, I want to become an AI engineer who can understand LLMs at a basic level. That requires the ability to program a transformer from scratch or optimize an LLM on a GPU. I can’t do that right now because there are gaps in my knowledge and I want to fill those gaps. I also focus on NLP. If you are looking for other AI specializations like Computer Vision or Reinforcement Learning, leave a comment below or DM me on Twitter or Linkedin. Here are some recommendations:
Before I give you a bunch of links, I wish someone had told me two important things before you start learning anything:
Learning Machine Learning (ML)
Machine Learning is the backbone of AI. To become proficient, follow these steps:
Study Key ML Algorithms:
Supervised Learning: Learn about regression, decision trees, and support vector machines.
Unsupervised Learning: Study clustering algorithms like K-means and dimensionality reduction techniques like PCA.
Reinforcement Learning: Understand how agents learn from interaction with environments.
That means getting into the habit of creating. This means: Writing blogs and tutorials Attending hackathons and collaborating with others Asking and answering questions in the Discord community Working on a side project that excites you Tweeting about something interesting you’ve recently discovered And speaking of Twitter.
Table of contents
Mathematics
Tools
∘ Python
∘ PyTorch
Machine Learning
∘ Write from Scratch
∘ Compete
∘ Do side projects
∘ Deploy them
∘ Supplementary
Deep Learning
∘ Fast.ai
∘ Do more competitions
∘ Implement papers
∘ Computer Vision
∘ NLP
Large Language Models
∘ Watch Neural Networks: Zero to Hero
∘ Free LLM boot camp
∘ Build with LLMs
∘ Participate in hackathons
∘ Read papers
∘ Write Transformers from scratch.
∘ Some good blogs
∘ Watch Umar Jamil
∘ Learn how to run open-source models.
∘ Prompt Engineering
∘ Fine-tuning LLMs
∘ RAG
How to stay updated
Other curriculums/listicles you may find useful
Practice and Experimentation:
Constantly challenge yourself with new projects and experiment with different techniques.
Stay curious and be open to learning new concepts and tools as the field evolves.
The roadmap to learning AI in 2024 is a blend of foundational knowledge, hands-on practice, and continuous exploration of new advancements. By following this guide, you can build a robust skill set that prepares you for the diverse opportunities in the AI landscape. Whether you aim to contribute to cutting-edge research, develop innovative AI applications, or simply stay ahead in your career, this roadmap will serve as a valuable reference on your journey to mastering AI.