Lilly Tech Systems
ML Algorithm Implementation
The classic ML interview format: implement algorithms from scratch using only NumPy. No scikit-learn, no PyTorch — just you, NumPy, and the math. Build linear regression with gradient descent, logistic regression with softmax, decision trees with information gain, K-means clustering, neural networks with backpropagation, and PCA from eigendecomposition.
Your Learning Path
Follow these lessons in order to build every major ML algorithm from scratch, or jump to any topic you need to review.
1. Implementing ML From Scratch
Why interviewers ask you to implement algorithms from scratch, what they evaluate, and the NumPy-only approach to building ML systems.
2. Linear Regression
Full implementation: gradient descent, normal equation, Ridge and Lasso regularization, polynomial features, and convergence analysis.
3. Logistic Regression
Full implementation: sigmoid function, cross-entropy loss, gradient descent, multi-class classification with softmax, and decision boundaries.
4. Decision Trees
Full implementation: information gain, Gini impurity, recursive splitting, pre/post-pruning, and building a random forest ensemble.
5. K-Means & KNN
Full implementation: K-means with K-means++ initialization, K-nearest neighbors with multiple distance metrics, and DBSCAN clustering.
6. Neural Networks
Full MLP implementation: forward pass, backpropagation with chain rule, activation functions (ReLU, sigmoid, tanh), SGD and Adam optimizers.
7. PCA & SVD
Full implementation: PCA from eigendecomposition, SVD for dimensionality reduction, explained variance, and t-SNE basics.
8. Implementation Tips
Common implementation mistakes, numerical stability tricks, testing strategies, performance optimization, and FAQ for ML coding interviews.
What You'll Learn
By the end of this course, you will be able to:
Implement Any ML Algorithm
Build linear models, tree-based models, clustering algorithms, neural networks, and dimensionality reduction from scratch using only NumPy.
Understand the Math
Derive and implement gradient descent, backpropagation, information gain, eigendecomposition, and every other mathematical foundation behind ML.
Ace ML Interviews
Answer the most common ML coding interview question: "Implement X from scratch without using any ML library." Confidently and correctly.
Debug ML Code
Identify and fix common numerical issues: vanishing gradients, overflow in softmax, singular matrices, and convergence failures.