Machine Learning Theory

Learn about classification, regression, clustering, and dimensionality reduction. Learn more

Techniques for cleaning, transforming, and preparing data for ML models. Learn more

Creating and selecting meaningful features for better model performance. Learn more

Techniques for training, validation, and test set creation. Learn more

Understanding optimization algorithms and their role in training models. Learn more

Different types of loss functions and their applications in ML. Learn more

Advanced optimization techniques like Adam, RMSprop, and momentum. Learn more

Essential linear algebra concepts for ML: vectors, matrices, transformations, and decompositions. Learn more

Core statistical concepts and probability theory for ML applications. Learn more

Understanding and implementing regression models for both continuous and categorical outcomes. Learn more

Tree-based models for classification and regression. Learn more

Powerful classification algorithm using maximum margin separation. Learn more

Deep learning architectures and their applications.

Architectures

• Deep Neural Networks (DNN)
  Multi-layer perceptrons and feedforward networks.
• Convolutional Neural Networks (CNN)
  Specialized for image processing and computer vision.
• Recurrent Neural Networks (RNN)
  LSTM, GRU, and sequence modeling.
• Transformers
  Attention mechanisms and modern NLP architectures.
• Graph Neural Networks (GNN)
  Processing graph-structured data.
• Generative Adversarial Networks (GAN)
  Generative models and adversarial training.
• Autoencoders
  Dimensionality reduction and feature learning.

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Methods for grouping similar data points together. Learn more

Techniques for reducing the number of features while preserving important information.

Key Methods

• Principal Component Analysis (PCA)
  Linear dimensionality reduction using singular value decomposition.
• t-SNE
  t-Distributed Stochastic Neighbor Embedding for visualization.
• UMAP
  Uniform Manifold Approximation and Projection for dimensionality reduction.
• Independent Component Analysis (ICA)
  Finding statistically independent components in data.

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Identifying unusual patterns or outliers in data. Learn more

Discovering interesting relationships between variables in large datasets. Learn more

Learning through interaction with an environment. Learn more

Neural networks and their applications. Learn more

Understanding and processing human language. Learn more