Master the essential Python programming concepts and tools needed for machine learning. This course covers data structures, functions, libraries, and best practices for ML development.
Essential Python concepts and tools for machine learning development.
Start your journey into machine learning with fundamental concepts, mathematical foundations, and practical implementations. This course provides a solid foundation for understanding modern ML techniques.
Statistical validation, cross-validation techniques, and performance metrics.
Understanding different matrix structures and their applications in ML.
Exploring regression methods, from linear regression solutions to logistic regression and optimization.
Understanding tree-based methods, from basic decision trees to ensemble techniques.
Exploring instance-based learning and probabilistic classification methods.
Deep dive into SVD and its applications in recommendation systems.
Understanding how different ML approaches transform data into higher dimensions.
Bridging traditional ML approaches with neural networks and modern frameworks.
Examining bias introduction, detection, and mitigation in deep learning systems.
Dive deep into advanced ML concepts, neural architectures, and state-of-the-art techniques. This course builds on ML1 to explore cutting-edge applications, with a special focus on Large Language Models and modern architectures.
History, evolution, neural network concepts, and framework comparisons.
Detailed backpropagation, gradient descent variations, and training challenges.
Practical implementation of neural networks for housing price prediction using the California Housing Dataset.
Explore vector representations and similarity measures through practical examples, including food preference vectors, sparse representation challenges, and learned representations.
Autoencoder architectures, embedding spaces, and dimensionality reduction.
Explore the evolution from autoencoders to modern embedding techniques, focusing on Word2Vec, GloVe, and practical applications in recommendation systems.
Understanding sequence models, attention mechanisms, and their applications.
Deep dive into CNNs, their architectures, and applications.
Exploring the transition from supervised learning to generative models.
Understanding the fundamentals of LLMs and their capabilities.
Learn to integrate and develop with LLM APIs effectively.
Deep dive into RAG architecture and implementation.
Learn comprehensive approaches to evaluating LLM performance.
Explore how LLMs can be used for decision-making and autonomous tasks.
Looking ahead at emerging developments in LLM technology.
Explore specialized topics and cutting-edge developments in machine learning, featuring guest lectures and focused deep dives into specific areas of ML.
A co-developed libary with Andrew Ng, that allows for rapid swaping of LLM providers.
Explore different approaches to ranking and evaluation, from traditional methods to modern ML-based systems.
Deep dive into Singular Value Decomposition, its mathematical foundations, and applications in machine learning.
Explore the fundamentals and advanced concepts of reinforcement learning, from Q-learning to deep RL architectures.
A comprehensive exploration of PCA, from intuitive understanding to mathematical foundations and practical applications.
Deep dive into Markov chains, Hidden Markov Models, and their applications in machine learning and sequential data analysis.
Leveraging pre-trained models for new tasks and domains, with practical implementations and best practices.
Enhancing LLMs with external knowledge retrieval, focusing on architecture, implementation, and best practices.
Explore probabilistic programming frameworks, Bayesian inference, and MCMC methods for modern machine learning.
Hands-on examples using real-world datasets, featuring detailed notebooks with data analysis, model development, and evaluation techniques.
Deep dive into logistic regression, covering theory, implementation, and practical applications in classification tasks.