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course
Introduction to Machine Learning with Python
Machine learning is now used everywhere. Want to learn it yourself? This course is an introduction to the world of Machine learning for you to learn basic concepts, work with Scikit-learn – the most popular library for ML and build your first Machine Learning project.
This course is intended for students with a basic knowledge of Python, Pandas, and Numpy.
4 hours
32 chaptersIntermediate
22 STUDYING NOW
course
Meta-Learning Fundamentals
A theory-first exploration of meta-learning, focusing on mathematical intuition, optimization dynamics, and learning theory. Understand how models learn to learn, the foundations of MAML, and the conceptual landscape of meta-learning methods.
2 hours 9 chaptersAdvanced
course
Introduction to Reinforcement Learning with Python
Reinforcement Learning (RL) is a powerful branch of machine learning focused on training intelligent agents through interaction with their environment. In this course, you'll learn how agents gradually discover effective behaviors through trial and error. Beginning with core concepts like Markov decision processes and multi-armed bandits, you'll work your way through dynamic programming, Monte Carlo methods, and temporal difference learning.
6 hours 37 chaptersAdvanced
1 STUDYING NOW
course
Transfer Learning Essentials with Python
Master the core concepts and hands-on techniques of transfer learning. Learn how to leverage pre-trained models for image classification and sentiment analysis, and gain practical experience with CNNs and transformers.
1 hour 11 chaptersBeginner
course
Mastering scikit-learn API and Workflows
Master the scikit-learn library by learning its API, core abstractions, and engineering patterns. Focus on syntax, structure, and workflow to confidently build, compose, and inspect machine learning pipelines using scikit-learn.
2 hours 19 chaptersIntermediate
1 STUDYING NOW
course
Active Learning with Python
Explore the principles and practical techniques of Active Learning to maximize label efficiency in machine learning workflows. Learn the core concepts, sampling strategies, and hands-on iterative simulations using Python and scikit-learn.
1 hour 10 chaptersIntermediate
1 STUDYING NOW
course
Ensemble Learning Techniques with Python
A comprehensive intermediate course on ensemble learning in machine learning, covering foundational concepts, mathematical intuition, and practical implementation of bagging, boosting, and advanced integration methods using Python and scikit-learn.
1 hour 14 chaptersBeginner
course
Autoencoders and Representation Learning
A comprehensive, theory-driven exploration of autoencoders and representation learning, covering foundational concepts, key autoencoder variants, mathematical underpinnings, and practical interpretability insights. This course is designed for learners seeking a deep conceptual understanding of autoencoders, their architectures, and their role in modern machine learning.
3 hours 17 chaptersAdvanced
course
Functional Analysis for Machine Learning
A rigorous exploration of the functional-analytic foundations of machine learning, focusing on normed spaces, operators, compactness, and the mathematical structure underlying generalization and stability.
2 hours 9 chaptersAdvanced
course
Machine Learning for Time Series Forecasting
A hands-on course teaching practical machine learning techniques for time series forecasting using tree-based and boosting models. Learn windowing, feature engineering, temporal validation, and multi-step forecasting strategies with Python and scikit-learn.
2 hours 12 chaptersIntermediate
1 STUDYING NOW
course
Spectral Methods in Machine Learning
Explore the mathematical foundations of spectral methods in machine learning. Understand how eigenvalues, eigenvectors, and spectral decompositions underpin dimensionality reduction, graph learning, and kernel methods, with a focus on theory and structure.
2 hours 9 chaptersAdvanced
course
Graph Theory for Machine Learning with Python
Master advanced machine learning techniques tailored for graph-structured data. Explore graph theory, graph representation, node embeddings, and practical graph ML tasks using Python and essential libraries.
1 hour 12 chaptersBeginner
course
Continual Learning and Catastrophic Forgetting
A research-oriented, advanced theoretical course exploring the structural causes of catastrophic forgetting in neural networks, the optimization and representational challenges it poses, and the core theoretical strategies for continual learning without data rehearsal. Emphasis is placed on the geometry of parameter space, the stability–plasticity dilemma, and the fundamental trade-offs and open problems in the field.
2 hours 9 chaptersAdvanced
course
Sampling Methods for Machine Learning
Explore the mathematical intuition and practical foundations of sampling methods in machine learning, from Monte Carlo basics to MCMC and their roles in modern generative models.
2 hours 9 chaptersAdvanced
course
Introduction to Neural Networks with Python
Neural networks are powerful algorithms inspired by the structure of the human brain that are used to solve complex machine learning problems. You will build your own Neural Network from scratch to understand how it works. After this course, you will be able to create neural networks for solving classification and regression problems using the scikit-learn library.
4 hours 25 chaptersAdvanced
7 STUDYING NOW
Sorry... We can't find
what you're looking for
Career tracks
Courses & Projects
Technologies
course
Introduction to Machine Learning with Python
Machine learning is now used everywhere. Want to learn it yourself? This course is an introduction to the world of Machine learning for you to learn basic concepts, work with Scikit-learn – the most popular library for ML and build your first Machine Learning project.
This course is intended for students with a basic knowledge of Python, Pandas, and Numpy.
4 hours 32 chaptersIntermediate
22 STUDYING NOW
course
Meta-Learning Fundamentals
A theory-first exploration of meta-learning, focusing on mathematical intuition, optimization dynamics, and learning theory. Understand how models learn to learn, the foundations of MAML, and the conceptual landscape of meta-learning methods.
2 hours 9 chaptersAdvanced
course
Introduction to Reinforcement Learning with Python
Reinforcement Learning (RL) is a powerful branch of machine learning focused on training intelligent agents through interaction with their environment. In this course, you'll learn how agents gradually discover effective behaviors through trial and error. Beginning with core concepts like Markov decision processes and multi-armed bandits, you'll work your way through dynamic programming, Monte Carlo methods, and temporal difference learning.
6 hours 37 chaptersAdvanced
1 STUDYING NOW
course
Transfer Learning Essentials with Python
Master the core concepts and hands-on techniques of transfer learning. Learn how to leverage pre-trained models for image classification and sentiment analysis, and gain practical experience with CNNs and transformers.
1 hour 11 chaptersBeginner
course
Mastering scikit-learn API and Workflows
Master the scikit-learn library by learning its API, core abstractions, and engineering patterns. Focus on syntax, structure, and workflow to confidently build, compose, and inspect machine learning pipelines using scikit-learn.
2 hours 19 chaptersIntermediate
1 STUDYING NOW
course
Active Learning with Python
Explore the principles and practical techniques of Active Learning to maximize label efficiency in machine learning workflows. Learn the core concepts, sampling strategies, and hands-on iterative simulations using Python and scikit-learn.
1 hour 10 chaptersIntermediate
1 STUDYING NOW
course
Ensemble Learning Techniques with Python
A comprehensive intermediate course on ensemble learning in machine learning, covering foundational concepts, mathematical intuition, and practical implementation of bagging, boosting, and advanced integration methods using Python and scikit-learn.
1 hour 14 chaptersBeginner
course
Autoencoders and Representation Learning
A comprehensive, theory-driven exploration of autoencoders and representation learning, covering foundational concepts, key autoencoder variants, mathematical underpinnings, and practical interpretability insights. This course is designed for learners seeking a deep conceptual understanding of autoencoders, their architectures, and their role in modern machine learning.
3 hours 17 chaptersAdvanced
course
Functional Analysis for Machine Learning
A rigorous exploration of the functional-analytic foundations of machine learning, focusing on normed spaces, operators, compactness, and the mathematical structure underlying generalization and stability.
2 hours 9 chaptersAdvanced
course
Machine Learning for Time Series Forecasting
A hands-on course teaching practical machine learning techniques for time series forecasting using tree-based and boosting models. Learn windowing, feature engineering, temporal validation, and multi-step forecasting strategies with Python and scikit-learn.
2 hours 12 chaptersIntermediate
1 STUDYING NOW
course
Spectral Methods in Machine Learning
Explore the mathematical foundations of spectral methods in machine learning. Understand how eigenvalues, eigenvectors, and spectral decompositions underpin dimensionality reduction, graph learning, and kernel methods, with a focus on theory and structure.
2 hours 9 chaptersAdvanced
course
Graph Theory for Machine Learning with Python
Master advanced machine learning techniques tailored for graph-structured data. Explore graph theory, graph representation, node embeddings, and practical graph ML tasks using Python and essential libraries.
1 hour 12 chaptersBeginner
course
Continual Learning and Catastrophic Forgetting
A research-oriented, advanced theoretical course exploring the structural causes of catastrophic forgetting in neural networks, the optimization and representational challenges it poses, and the core theoretical strategies for continual learning without data rehearsal. Emphasis is placed on the geometry of parameter space, the stability–plasticity dilemma, and the fundamental trade-offs and open problems in the field.
2 hours 9 chaptersAdvanced
course
Sampling Methods for Machine Learning
Explore the mathematical intuition and practical foundations of sampling methods in machine learning, from Monte Carlo basics to MCMC and their roles in modern generative models.
2 hours 9 chaptersAdvanced
course
Introduction to Neural Networks with Python
Neural networks are powerful algorithms inspired by the structure of the human brain that are used to solve complex machine learning problems. You will build your own Neural Network from scratch to understand how it works. After this course, you will be able to create neural networks for solving classification and regression problems using the scikit-learn library.
4 hours 25 chaptersAdvanced
7 STUDYING NOW
course
Introduction to Machine Learning with Python
Machine learning is now used everywhere. Want to learn it yourself? This course is an introduction to the world of Machine learning for you to learn basic concepts, work with Scikit-learn – the most popular library for ML and build your first Machine Learning project.
This course is intended for students with a basic knowledge of Python, Pandas, and Numpy.
4 hours 32 chaptersIntermediate
22 STUDYING NOW
course
Meta-Learning Fundamentals
A theory-first exploration of meta-learning, focusing on mathematical intuition, optimization dynamics, and learning theory. Understand how models learn to learn, the foundations of MAML, and the conceptual landscape of meta-learning methods.
2 hours 9 chaptersAdvanced
course
Introduction to Reinforcement Learning with Python
Reinforcement Learning (RL) is a powerful branch of machine learning focused on training intelligent agents through interaction with their environment. In this course, you'll learn how agents gradually discover effective behaviors through trial and error. Beginning with core concepts like Markov decision processes and multi-armed bandits, you'll work your way through dynamic programming, Monte Carlo methods, and temporal difference learning.
6 hours 37 chaptersAdvanced
1 STUDYING NOW
course
Transfer Learning Essentials with Python
Master the core concepts and hands-on techniques of transfer learning. Learn how to leverage pre-trained models for image classification and sentiment analysis, and gain practical experience with CNNs and transformers.
1 hour 11 chaptersBeginner
course
Mastering scikit-learn API and Workflows
Master the scikit-learn library by learning its API, core abstractions, and engineering patterns. Focus on syntax, structure, and workflow to confidently build, compose, and inspect machine learning pipelines using scikit-learn.
2 hours 19 chaptersIntermediate
1 STUDYING NOW
course
Active Learning with Python
Explore the principles and practical techniques of Active Learning to maximize label efficiency in machine learning workflows. Learn the core concepts, sampling strategies, and hands-on iterative simulations using Python and scikit-learn.
1 hour 10 chaptersIntermediate
1 STUDYING NOW
course
Ensemble Learning Techniques with Python
A comprehensive intermediate course on ensemble learning in machine learning, covering foundational concepts, mathematical intuition, and practical implementation of bagging, boosting, and advanced integration methods using Python and scikit-learn.
1 hour 14 chaptersBeginner
course
Autoencoders and Representation Learning
A comprehensive, theory-driven exploration of autoencoders and representation learning, covering foundational concepts, key autoencoder variants, mathematical underpinnings, and practical interpretability insights. This course is designed for learners seeking a deep conceptual understanding of autoencoders, their architectures, and their role in modern machine learning.
3 hours 17 chaptersAdvanced
course
Functional Analysis for Machine Learning
A rigorous exploration of the functional-analytic foundations of machine learning, focusing on normed spaces, operators, compactness, and the mathematical structure underlying generalization and stability.
2 hours 9 chaptersAdvanced
course
Machine Learning for Time Series Forecasting
A hands-on course teaching practical machine learning techniques for time series forecasting using tree-based and boosting models. Learn windowing, feature engineering, temporal validation, and multi-step forecasting strategies with Python and scikit-learn.
2 hours 12 chaptersIntermediate
1 STUDYING NOW
course
Spectral Methods in Machine Learning
Explore the mathematical foundations of spectral methods in machine learning. Understand how eigenvalues, eigenvectors, and spectral decompositions underpin dimensionality reduction, graph learning, and kernel methods, with a focus on theory and structure.
2 hours 9 chaptersAdvanced
course
Graph Theory for Machine Learning with Python
Master advanced machine learning techniques tailored for graph-structured data. Explore graph theory, graph representation, node embeddings, and practical graph ML tasks using Python and essential libraries.
1 hour 12 chaptersBeginner
course
Continual Learning and Catastrophic Forgetting
A research-oriented, advanced theoretical course exploring the structural causes of catastrophic forgetting in neural networks, the optimization and representational challenges it poses, and the core theoretical strategies for continual learning without data rehearsal. Emphasis is placed on the geometry of parameter space, the stability–plasticity dilemma, and the fundamental trade-offs and open problems in the field.
2 hours 9 chaptersAdvanced
course
Sampling Methods for Machine Learning
Explore the mathematical intuition and practical foundations of sampling methods in machine learning, from Monte Carlo basics to MCMC and their roles in modern generative models.
2 hours 9 chaptersAdvanced
course
Introduction to Neural Networks with Python
Neural networks are powerful algorithms inspired by the structure of the human brain that are used to solve complex machine learning problems. You will build your own Neural Network from scratch to understand how it works. After this course, you will be able to create neural networks for solving classification and regression problems using the scikit-learn library.
4 hours 25 chaptersAdvanced
7 STUDYING NOW
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what you're looking for
