Machine Learning

Understand vectors, matrices, matrix operations, eigenvalues, and decompositions that underpin nearly every ML algorithm.

Learn probability distributions, Bayes' theorem, expectation, variance, and hypothesis testing — the language ML uses to reason about uncertainty.

Understand derivatives, gradients, the chain rule, and how optimization algorithms find the best parameters for a model.

Survey the three main paradigms — supervised, unsupervised, and reinforcement learning — and understand when each applies.

Learn how to clean, normalize, encode, and transform raw data into features that ML models can learn from effectively.

Build your first predictive model: understand the cost function, closed-form solution, and gradient descent for fitting a line to data.

Extend regression to classification problems using the sigmoid function, cross-entropy loss, and decision boundaries.

Learn accuracy, precision, recall, F1, ROC-AUC, and cross-validation to measure how well your model truly performs.

Understand why models underfit or overfit, and learn regularization techniques (L1, L2) to find the sweet spot.

Learn how tree-based models split data to make predictions, and how ensembling many trees into a forest reduces overfitting.

Understand maximum-margin classifiers, the kernel trick, and how SVMs handle non-linearly separable data.

Learn instance-based learning where predictions come from the closest training examples, and understand the curse of dimensionality.

Apply Bayes' theorem with a strong independence assumption for fast, surprisingly effective text and spam classification.

Understand how sequentially adding weak learners that correct previous errors creates state-of-the-art models for tabular data.

Discover natural groups in data without labels using distance-based and density-based clustering algorithms.

Learn to compress high-dimensional data into fewer dimensions for visualization and noise removal while preserving structure.

Identify unusual patterns in data using statistical methods, isolation forests, and autoencoders for fraud detection and monitoring.

Understand how layers of neurons learn representations through forward passes and gradient-based weight updates.

Learn how convolution, pooling, and hierarchical feature extraction enable machines to understand images and spatial data.

Process sequential data like text and time series using networks with memory, and understand vanishing gradients and gating mechanisms.

Understand self-attention, positional encoding, and why the Transformer architecture revolutionized NLP and beyond.

Leverage pre-trained models to solve new tasks with less data by adapting learned representations to your specific domain.

Structure reproducible workflows using tools like scikit-learn pipelines, MLflow, and Weights & Biases to manage experiments systematically.

Explore grid search, random search, and Bayesian optimization to find the model configuration that maximizes performance.

Move models from notebooks to production using REST APIs, batch inference, and model serving frameworks.

Recognize how bias enters datasets and models, and learn techniques to measure and mitigate unfair outcomes.