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Foundations Vision Sequences Transformers Agentic AI System Design Practice Interview

Foundations

Foundations of Regression0/10

Designing a Learning System & the Types of Machine Learning
Linear regression: lines, SSR, and gradient descent
Why do we need logistic regression?
The sigmoid: the secret sauce of logistic regression
Logistic regression and decision boundaries
Intuition behind logistic regression
Log likelihood instead of squared error
Performance Metrics: Grading Classification & Regression Models
Interview Readiness, Foundations of Regression

Tree-Based Algorithms0/16

Decision Trees: impurity, splitting, and the CART and ID3 algorithms
Overfitting, Bias-Variance & Cross-Validation
Random Forests & Bagging
Boosting: AdaBoost & Gradient Boosting
Bayes' Theorem and Concept Learning
The Naive Bayes Classifier
The Bayes-Optimal Classifier and the Gibbs Algorithm
Bayesian Belief Networks
The EM Algorithm
Support Vector Machines: Hyperplanes, Margins, and Kernels
Non-Parametric Regression: Locally Weighted Regression and K-Nearest Neighbours
Unsupervised Learning & Clustering: Distances, k-Means, and Hierarchies
DBSCAN & Cluster Quality: Density Clustering, Silhouette & the Rand Index
Dimensionality Reduction: PCA, SVD, and t-SNE
Population-Based Optimization & Reinforcement Learning

Deep Neural Networks0/12

From linear regression to the perceptron
Layers in a deep neural network
Activation functions
Loss functions
How neural networks make predictions (forward pass)
How neural networks learn (backward pass)
Trainable parameters and hyperparameters
Overfitting in neural networks
Neural network architecture (binary classification recap)
Neural networks from scratch (NumPy)
Interview Readiness, Deep Neural Networks

Courses/AI Research

AI Research

Reading papers, reproducing results, and frontier topics, from autonomous math agents to how research workflows are changing.

Updated 24 Apr

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