ML Algorithms

ML Algorithms

The algorithms behind modern ML. Linear and logistic regression, decision trees, random forests, SVMs, k-means clustering, gradient descent, backpropagation, neural network architectures, and how to choose, implement, and evaluate models from scratch.

FundamentalsTopics 1–12
  • ·What is Machine Learning
  • ·Types of Learning
  • ·The ML Pipeline
  • ·Data Splitting
  • ·Feature Engineering
  • ·Bias-Variance Tradeoff
  • ·Overfitting & Underfitting
  • ·Evaluation Metrics
  • ·Cross-Validation
  • ·Scikit-learn Basics
  • ·Imbalanced Data
  • ·Optimization Foundations
Start Fundamentals →
IntermediateTopics 1–12
  • ·Linear Regression
  • ·Logistic Regression
  • ·Decision Trees
  • ·Random Forests
  • ·Gradient Boosting
  • ·Support Vector Machines
  • ·K-Nearest Neighbours
  • ·Naive Bayes
  • ·Ensemble Methods
  • ·Hyperparameter Tuning
  • ·Calibration & Thresholds
  • ·Interpretability
Start Intermediate →
IntermediateTopics 1–12
  • ·K-Means Clustering
  • ·Hierarchical Clustering
  • ·DBSCAN
  • ·Principal Component Analysis
  • ·t-SNE & UMAP
  • ·Autoencoders
  • ·Gaussian Mixture Models
  • ·Anomaly Detection
  • ·Association Rules
  • ·Dimensionality Reduction
  • ·Recommender Systems
  • ·Cluster Validity
Start Intermediate →
AdvancedTopics 1–12
  • ·Neural Network Building Blocks
  • ·Training Loop
  • ·Backpropagation Deep Dive
  • ·Regularisation
  • ·Batch Normalisation
  • ·Learning Rate Schedules
  • ·Optimisers
  • ·Architecture Patterns
  • ·Transfer Learning
  • ·Custom Loss Functions
  • ·Architecture Comparison
  • ·Production Pitfalls
Start Advanced →