6-Month Generative AI Learning Plan

Month 1: Foundations
Week 1-2
Time Allocation: 20-25 hours/week
What to Learn
  • Basic syntax: Rules for writing code.
  • Data types: e.g., integers, strings, booleans.
  • Operators: Symbols like +, -, *.
  • Control flow: if/else statements, loops.
  • Data structures: Ways to organize data: lists, tuples, dictionaries, sets.
  • Functions: Reusable blocks of code.
  • Modules: Files containing Python code.
  • OOP basics: Object-Oriented Programming concepts.
  • Error handling: Using try/except blocks.
  • File handling: Reading and writing files.
Online Courses
Practice Platforms

HackerRank, LeetCode (start with easy problems).

Week 3-4
Time Allocation: 20-25 hours/week
What to Learn
  • NumPy arrays: Multi-dimensional data structures.
  • NumPy operations: Mathematical operations on arrays.
  • NumPy indexing: Accessing specific elements.
  • Pandas DataFrames and Series: Tabular data structure and one-dimensional labeled array.
  • Pandas data manipulation: Cleaning, transforming, merging data.
  • Matplotlib and Seaborn for basic data visualization.
Online Courses
Documentation

NumPy, Pandas, Matplotlib, Seaborn.

Practice

Work through exercises and small projects.

Month 2: Math & Basic ML
Week 5-6
Time Allocation: 15-20 hours/week
What to Learn
  • Linear Algebra: Vectors, matrices, matrix operations, eigenvalues, eigenvectors.
  • Calculus: Derivatives, integrals, gradient.
  • Probability and Statistics: Basic probability, distributions, descriptive statistics.
Online Courses
Books

"Deep Learning" book by Goodfellow, Bengio, and Courville (Mathematical notation chapters).

Week 7-8
Time Allocation: 15-20 hours/week
What to Learn
  • Supervised Learning:
    - Linear Regression: Predicting continuous values.
    - Logistic Regression: Predicting categories.
    - Decision Trees: Tree-like model for decisions.
    - Random Forests: Ensemble of decision trees.
    - Support Vector Machines (SVMs): Finding optimal separating hyperplane.
  • Unsupervised Learning:
    - Clustering (K-Means): Grouping similar data points.
    - Dimensionality Reduction (PCA): Reducing the number of features.
  • Model Evaluation: Metrics, Cross-validation, Bias-Variance Tradeoff.
  • Basic Feature Engineering techniques.
Online Courses
Books

"Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow" by Aurélien Géron.

Libraries

scikit-learn.

Month 3-4: Deep Learning & Intro to Gen AI
Week 9-12
Time Allocation: 20-25 hours/week
What to Learn
  • Introduction to Neural Networks: Perceptrons, activation functions.
  • Multi-Layer Perceptrons (MLPs).
  • Backpropagation algorithm.
  • Convolutional Neural Networks (CNNs): Architecture, applications.
  • Recurrent Neural Networks (RNNs): Architecture, applications.
  • Introduction to Deep Learning Frameworks: TensorFlow and/or PyTorch.
  • Training Deep Neural Networks: Optimization algorithms (Adam, SGD), regularization techniques (dropout, batch normalization), handling overfitting.
Online Courses
Books

"Deep Learning" book.

Documentation

TensorFlow or PyTorch.

Week 13-16
Time Allocation: 20-25 hours/week
What to Learn
  • Fundamentals of Generative Modeling.
  • Variational Autoencoders (VAEs): Architecture, training process, applications.
  • Generative Adversarial Networks (GANs): Architecture, training dynamics, common GAN architectures (DCGAN).
  • Introduction to Diffusion Models: Basic concepts and intuition.
  • Applications of Generative Models: Image generation, text generation, music generation, etc.
Online Courses
Research Papers

"Generative Adversarial Nets", Foundational papers on VAEs.

Tutorials and Blog Posts

Search for practical implementations of basic GANs and VAEs using TensorFlow or PyTorch.

Month 5-6: Projects & Job Prep
Week 17-20
Time Allocation: 25-30 hours/week
Project Ideas
  • Implement DCGAN on CIFAR-10
  • Implement VAE on MNIST
  • Experiment with text generation using pre-trained models from Hugging Face Transformers
  • Implement a basic diffusion model
Libraries

Hugging Face Transformers, Keras Examples, PyTorch Tutorials.

GitHub

Explore open-source implementations (understand the code, don't just copy).

Week 21-24
Time Allocation: 20-25 hours/week
Portfolio Building

Create a website (e.g., using GitHub Pages, Netlify) to showcase your projects and skills. Host code on GitHub with clear READMEs.

Resume and LinkedIn

Tailor to highlight your AI/ML and Generative AI skills and projects.

Interview Practice

Focus on Python, ML fundamentals, deep learning, and basic generative model concepts. Use platforms like LeetCode for coding practice.

Job Boards

Indeed, LinkedIn, Glassdoor, AngelList.

Continued Learning

Explore advanced GAN architectures, Stable Diffusion, Transformer models in more detail based on your interests.