My A.I. Learning Plan

owens.rustyon

So I asked an AI the best way to learn AI. I told it to ask me questions about my technical background. Once it quizzed me, it came up with a plan to get going on multiple fronts.

Foundations: Code, Math, Data

  1. Python + Numpy “mini‑library”
    • Build your own small numerical toolkit: vector/matrix class, basic stats (mean, variance), and simple gradient descent for linear regression.
    • Goal: Get comfortable with tensors, gradients, and debugging math bugs in code.​
  2. Data wrangling + EDA notebook
    • Take a public dataset (e.g., housing prices or Titanic) and do full exploratory data analysis: cleaning, feature engineering, visualization, and a baseline model.
    • Goal: Practice the full “data → model → evaluation” loop that underlies almost every AI project.

Classical Machine Learning

  1. Tabular prediction suite
    • Implement and compare: linear/logistic regression, decision trees, random forests, gradient boosting on a real dataset (churn, credit default, or housing prices).
    • Goal: Understand bias/variance, cross‑validation, feature importance, and when deep learning is overkill.
  2. Recommendation system
    • Build a simple recommender: user‑based or item‑based collaborative filtering, then a matrix‑factorization model; use movie or music data.
    • Goal: Learn similarity metrics, implicit feedback, and evaluation with ranking metrics.

Core Deep Learning

  1. Neural networks from scratch
    • Implement a fully connected neural net with backprop from scratch (Numpy only) and train on MNIST digits.
    • Goal: Internalize forward/backward passes, activations, losses, and optimization.​
  2. Image classifier with a framework
    • Use PyTorch or TensorFlow to train CNNs on CIFAR‑10 or a custom image dataset; add data augmentation and regularization.
    • Goal: Learn modern DL workflows, GPU usage, and metrics like confusion matrices and learning curves.

Natural Language Processing

  1. Text classification + sentiment analysis
    • Build a pipeline: cleaning, tokenization, classical models (logistic regression, SVM) and then a simple RNN/CNN or pretrained transformer for sentiment classification.
    • Goal: Compare classical NLP vs neural approaches, understand embeddings and sequence handling.
  2. Small retrieval‑augmented QA
    • Build a question answering tool over a set of documents: embed chunks in a vector DB, retrieve relevant passages, then have an LLM generate answers.
    • Goal: Learn RAG patterns, embeddings, and prompt engineering for LLMs.

Computer Vision

  1. Object detection or image segmentation app
    • Fine‑tune an off‑the‑shelf model (YOLO, Mask R‑CNN) on a small custom dataset (e.g., car parts, house components from Zillow images).
    • Goal: Touch more advanced CV tasks, annotation schemes, and the tradeoffs between accuracy and speed.
  2. Multimodal image captioning
  • Combine a CNN image encoder with an RNN/transformer text decoder (or fine‑tune an existing captioning model) to generate captions.
  • Goal: Work with multimodal architectures and understand how vision and language interact.​

Reinforcement Learning

  1. Classic control RL agent
  • Implement Deep Q‑Learning or policy gradients in a small environment (CartPole, LunarLander) using Gym‑style APIs.
  • Goal: Learn states, actions, rewards, exploration vs exploitation, and training instabilities specific to RL.
  1. Game‑playing or custom environment
  • Build a small grid‑world or simple 2D game and train an agent to learn strategies; log rewards and visualize policies.
  • Goal: Design reward functions and experiment with curriculum or shaping.​

MLOps and Production

  1. End‑to‑end ML pipeline
  • Take one of your earlier projects (e.g., churn prediction or image classification) and:
    • Package it as a REST API, containerize it, add logging, and basic monitoring.
  • Goal: Understand deployment, model versioning, and performance monitoring.
  1. Automated training + evaluation pipeline
  • Set up a script or workflow (e.g., with a job scheduler or CI) that: pulls data, trains, evaluates, saves metrics/artifacts, and optionally retrains on schedule.
  • Goal: Touch the basics of automated pipelines and continuous training.​

Generative AI and Agents

  1. Text generation fine‑tuning
  • Fine‑tune a small open LLM on a niche corpus (e.g., Bible commentary Q&A, automotive troubleshooting chats) and compare to base model behavior.
  • Goal: Learn dataset formatting, training loops for LLMs, and evaluation of generations.
  1. Small diffusion‑based image generator
  • Use a diffusion framework to fine‑tune an existing image model on a themed dataset (e.g., classic Western movie stills) and build a promptable UI.
  • Goal: Understand diffusion steps, conditioning, and safety/abuse considerations.
  1. Agentic AI “tool‑using assistant”
  • Build an agent that can call tools (e.g., web search, a calculator, your own APIs) to answer multi‑step questions, orchestrated via a framework like LangChain.
  • Goal: Learn reasoning‑and‑acting patterns, tool schemas, and how to chain multiple AI calls together.

Here is my plan in more detail. My plan is to document each step of the way.