Phase 1
Completed
Math & Python Foundations
Linear algebra, calculus, probability, and numpy/pandas fluency.
Linear Algebra
Calculus
Probability
NumPy
Pandas
Matplotlib
Phase 2
In Progress
Classical Machine Learning
Core supervised & unsupervised algorithms, feature engineering, model evaluation.
Linear Regression
Logistic Regression
Decision Trees
SVM
K-Means
PCA
Cross-Validation
Phase 3
Upcoming
Deep Learning
Neural networks, CNNs, RNNs, backpropagation, and optimisation techniques.
Perceptrons
Backprop
CNNs
RNNs
LSTMs
Dropout
Batch Norm
Adam
Phase 4
Upcoming
Advanced AI Topics
Transformers, diffusion models, reinforcement learning, and LLM fine-tuning.
Attention
Transformers
BERT/GPT
Diffusion
RL Basics
Policy Gradient
Fine-Tuning
Attention Is All You Need
Introduces the Transformer, replacing recurrence with self-attention to achieve state-of-the-art on translation tasks.
Generative Adversarial Networks
A framework where two networks compete — a generator and discriminator — producing realistic synthetic data.
Deep Residual Learning for Image Recognition
Residual connections allow training of 150+ layer networks, winning ImageNet 2015 by a large margin.
Language Models are Few-Shot Learners (GPT-3)
Shows that scaling LLMs dramatically improves few-shot task performance across diverse NLP benchmarks.
Playing Atari with Deep Reinforcement Learning
Combines Q-learning with CNNs to achieve human-level Atari performance from raw pixel inputs.
Training Language Models to Follow Instructions
Introduces RLHF to align LLM outputs with human intent, forming the basis of ChatGPT-style models.
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What is backpropagation?
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