It is overwhelming to start anything, but getting started in a large and diverse space like AI has been challenging for many. The AI technology space is advancing at an accelerated speed, so the time to get started is now. This article is a set of recommendations, suggestions, and ideas for an AI learning plan for beginners. I hope you find this article useful if you have been wondering when and how to start.
The article’s core focus is on providing a structured learning path for AI beginners, covering everything from programming fundamentals to advanced AI technologies.
[Photo generated by Microsoft Designer]
AI Learning Plan for Beginners TL;DR
Below is a suggested learning path for beginners that has helped me get started in AI. I have also noted some AI resources like blogs, newsletters, articles, books, and people to follow.
Here are the topics that we will cover. Let’s dive in.
Programming Fundamentals
- Master Python Programming
- Development Environment Setup
Machine Learning Foundations
- Machine Learning
- Key Learning Areas
- Supervised Learning
- Unsupervised Learning
- Reinforcement Learning
- Deep Learning
- Model Evaluation Metrics
- Overfitting and Underfitting Concepts
Emerging AI Technologies
- Generative AI
- Encoders & Decoders
- Transformer Models
- Large Language Models (LLMs)
- Vector Databases & Text Embeddings
- Prompt Engineering
- Retrieval Augmented Generation (RAG)
- MLOps and Deployment
- Ethical AI and Responsible Development
- AI Security
AI Resources
- Blogs I read
- Newsletters I subscribe to
- People I follow
- Articles I found interesting
- Books I recommend
Programming Fundamentals
Master Python programming
- Learn Python – Free Python Courses for Beginners (freeCodeCamp)
- Learn Python 3 (Codecademy)
- Learn Python libraries: NumPy, Pandas, Matplotlib, SpaCy, SciPy
- Learn scikit-learn library. Simple and efficient tools for predictive data analysis.
- Learn LangChain. LangChain is a composable framework to build with LLMs. LangGraph is the orchestration framework for controllable agentic workflows.
Development Environment Setup
- Python (latest version)
- Jupyter Notebook
- Visual Studio Code
- Git for version control - Git book
- Anaconda distribution
Machine Learning Foundations
Machine Learning
- Machine Learning Mastery
- Andrew Ng’s Machine Learning Course (Coursera)
- Fast.ai Practical Deep Learning
- Google’s Machine Learning Crash Course
Key Learning Areas
- Supervised Learning
- Learn classification algorithms, and regression techniques
- Supervised Learning (C3.ai)
- Unsupervised Learning
- Learn clustering algorithms, and dimensionality reduction
- Unsupervised Learning (C3.ai)
- Reinforcement Learning
- What is Reinforcement Learning? (MathWorks)
- Reinforcement Learning (C3.ai)
- An introduction to Reinforcement Learning (Youtube)
- Reinforcement Learning: Crash Course AI #9 (Youtube)
- Deep Learning
- What is deep learning? (IBM)
- Deep Learning (C3.ai)
- Deep Learning Course: Josh Starmerr: / @statquest
- Andrew Ng’s Deep Learning Specialization Course (Coursera)
- Deep Learning Book: HTML version | PDF version by Ian Goodfellow and Yoshua Bengio and Aaron Courville
- Model evaluation metrics
- Overfitting and underfitting concepts
AI Technologies Areas
Generative AI
- Introduction to Generative AI (Google)
- Generative AI on Google Cloud
- Generative AI Fundamentals (Databricks)
Encoders & Decoders
- Encoder-Decoder Architecture (Google)
Transformer Models
- Transformer Models and BERT Model (Google)
Large Language Models (LLMs)
- Introduction to AI: [1hr Talk] Intro to Large Language Models by Andrej Karpathy
- Introduction to Large Language Models (Google)
Vector Databases & Text Embeddings
- Vector Search and Embeddings (Google)
Prompt Engineering
- Google Prompting Essentials
- Prompt engineering course: Josh Starmer: / @statquest
- Prompt Design in Vertex AI (Google)
- ChatGPT Prompt Engineering for Developers (DeepLearning.ai)
Retrieval Augmented Generation (RAG)
- What is Retrieval-Augmented Generation (RAG)?
- Introduction to Retrieval Augmented Generation (RAG) (Coursera)
- Inspect Rich Documents with Gemini Multimodality and Multimodal RAG (Google)
- Mastering Retrieval Augmented Generation (RAG) IN LLMs (Udemy)
- Building and Evaluating Advanced RAG Applications (DeepLearning.ai)
- Creating AI Applications using Retrieval-Augmented Generation (RAG) (Codecademy)
MLOps and Deployment
- Explore Cloud AI platforms (AWS, Google Cloud)
- Learn Model deployment strategies, performance optimization, scalability considerations
- What Is MLOps? (MathWorks)
- MLOps: Continuous delivery and automation pipelines in machine learning (Google)
- MLOps Principles (MLOps)
- Machine Learning Operations (MLOps) for Generative AI (Google)
- Intro to MLOps: Build, Deploy, and Retrain a Model in Production (YouTube)
Ethical AI and Responsible Development
- Learn bias detection, fairness in machine learning, interpretability & transparency, privacy-preserving techniques etc.
- Introduction to Responsible AI (Google)
- Responsible AI: Applying AI Principles with Google Cloud
- Responsible AI for Developers: Fairness & Bias (Google)
- Responsible AI for Developers: Interpretability & Transparency (Google)
- Responsible AI for Developers: Privacy & Safety (Google)
AI Security
- Learn about AI Security and Hacking, AI Safety Research, and AI Regulations
- Defining AI Hacking: The Rise of AI Cyber Attacks (Sangfor)
- Stanford Center for AI Safety
- Blueprint for an AI Bill of Rights
- AI Watch: Global regulatory tracker - United States
- EU AI Act: first regulation on artificial intelligence
AI Resources
Blogs I read
- OpenAI blog
- Google DeepMind blog
- AWS Machine Learning blog
- MIT News – Artificial Intelligence
- Apple Machine Learning Research
- BAIR blog from UC Berkeley Artificial Intelligence Research Lab
- KDnuggets
- The Hugging Face blog
- Analytics Vidhya blog
Newsletters I subscribe to
- Last Week in AI
- Artificial Intelligence Made Simple
- AI Tidbits
- Deep (Learning) Focus
- State of AI
- AI Unlimited
- Ahead of AI
- The Generative Generation
- Latent Space
- Designing with AI
- AK’s Substack
- Educating AI
- Visually AI
- The AI Observer
- Creators’ AI
- The Era of Generative A.I.
- Unwind AI
- Gradient Ascent
- The AiEdge Newsletter
- Artificial Ignorance
- Understanding AI
People I follow
- Sam Altman (@sama)
- Sasha Luccioni (@SashaMTL)
- Adam Cheyer (@acheyer)
- Ashish Vaswani (@ashVaswani)
- Ilya Sutskever (@ilyasut)
- Fei-Fei Li (@drfeifei)
- Greg Brockman (@gdb)
- Josh Starmer (@statquest)
- Isa Fulford (@isafulf)
- Andrew Ng (@AndrewYNg)
Articles I found interesting
- How fast is AI improving?
- The Impact of AI in UX Design: The Complete Guide
- Comparing Chat GPT, Gemini, Copilot and Claude AI Chatbots
- 3 Steps to Prepare Your Culture for AI
- ChatGPT for UX Design: The Top 15 Prompts
- Building a web app using AI without the prompt engineering headaches
- LLM Powered Autonomous Agents
- Let’s build GPT: from scratch, in code, spelled out (YouTube)
Books I recommend
- Python book: Automate the Boring Stuff with Python (Free)
- Freecode camp for Python: Introduction to Programming and Computer Science
- Python Tips (Free online)
- OpenAI Cookbook by Isa Fulford
- A Brief History of Artificial Intelligence by Michael Wooldridge
- AI Literacy Fundamentals by Ben Jones
- Machine Learning by Ethem Alpaydin
- Hands-On Machine Learning with Scikit-Learn and TensorFlow by Aurélien Géron
- Deep Learning by John D. Kelleher
- Artificial Intelligence by Melanie Mitchell
- Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
- Pattern Recognition and Machine Learning by Christopher M. Bishop
- AI Ethics by Mark Coeckelbergh
- Life 3.0: Being Human in the Age of Artificial Intelligence by Max Tegmark
- Superintelligence: Paths, Dangers, Strategies by Nick Bostrom
- The Coming Wave by Mustafa Suleyman, Michael Bhaskar
- Human Compatible: Artificial Intelligence and the Problem of Control by Stuart Russell
Summary
In summary, the article covers the AI learning guide and provides a structured learning path for AI beginners, covering everything from programming fundamentals to advanced AI technologies.
Core Learning Path
- Start with Python programming fundamentals, including essential libraries like NumPy, Pandas, and scikit-learn
- Master machine learning foundations through supervised, unsupervised, and reinforcement learning concepts
- Progress to emerging AI technologies including generative AI, transformers, and large language models
Advanced Topics
- Learn practical skills in MLOps, including model deployment, performance optimization, and scalability
- Study ethical AI development, focusing on bias detection, fairness, and privacy-preserving techniques
- Understand AI security, safety research, and current regulatory frameworks
Learning Resources
- Access comprehensive learning through platforms like Coursera, Google Cloud Skills Boost, and DeepLearning.ai
- Stay updated through leading AI blogs & newsletters, and by following key industry experts & researchers
Hope this will help you get started on the path to AI learning. If you find any other helpful content or if you have questions or feedback, please let me know in the comments below.