Master machine learning from foundational theory to deep learning and real-world applications.
← Back to HomepageLearn supervised vs unsupervised learning, models, and theory.
Predict continuous variables and interpret regression outputs.
Model binary outcomes and learn about classification probabilities.
Understand decision boundaries, overfitting, and ensemble methods.
Learn about hyperplanes, margins, and kernel tricks.
Simple distance-based classification and regression technique.
Probability-based classification for text and categorical data.
Clustering, dimensionality reduction, and anomaly detection.
Reduce dimensionality while preserving variance.
Group data into clusters using distance metrics.
Precision, recall, F1-score, confusion matrices, and AUC.
Cross-validation, hyperparameter tuning, and pipelines.
Build and train artificial neural networks from scratch.
Train deep models using TensorFlow or Keras APIs.
Build models for image recognition and spatial data.
Sequence modeling and time-series prediction.
Tokenization, embeddings, transformers, and BERT.
Real-world end-to-end ML project with data prep, modeling, and deployment.