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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
2 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 β 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
20 STUDYING NOW
course
Introduction to NLP with Python
Explore the fundamentals of Natural Language Processing (NLP) by learning essential text preprocessing techniques and methods for representing text data. Gain practical experience with the tools used to clean, analyze, and interpret textual information. Develop the skills needed to transform raw language into structured insights, laying a strong foundation for advanced applications in artificial intelligence and machine learning.
5 hours 29 chaptersAdvanced
1 STUDYING NOW
course
Classification with Python
Master the core classification algorithms that power modern machine learning. Explore how models like k-NN, logistic regression, decision trees, and random forests make predictions, evaluate their accuracy, and understand when to use each. Build the skills to compare models and choose the best one for your data.
3 hours 24 chaptersIntermediate
2 STUDYING NOW
course
Cluster Analysis with Python
Gain a solid understanding of cluster analysis, a key unsupervised learning technique for uncovering patterns in unlabeled data. Explore the essentials of K-Means, Hierarchical Clustering, DBSCAN, and GMMs, and get hands-on experience with real datasets to build confidence in applying clustering to real-world problems.
4 hours 34 chaptersIntermediate
5 STUDYING NOW
course
Recurrent Neural Networks with Python
Master Recurrent neural networks and their advanced variants like LSTMs and GRUs using PyTorch. Gain hands-on experience processing sequential data for practical applications. Apply these powerful models to tackle real-world challenges in time series forecasting and various Natural language processing tasks.
3 hours 22 chaptersIntermediate
1 STUDYING NOW
course
Data Preprocessing and Feature Engineering with Python
Learn practical techniques to clean, transform, and engineer data for machine learning using Python. This course covers essential preprocessing steps, feature creation, and hands-on challenges to prepare data for modeling.
1 hour 12 chaptersBeginner
10 STUDYING NOW
course
PyTorch Essentials
Learn the fundamental and advanced concepts needed to work with PyTorch efficiently. Gain a solid understanding tensors, including creation, operations, and reshaping. Explore the essentials of gradients, backpropagation, and linear regression before moving on to handling datasets. Master the skill needed build, train, and evaluate neural networks.
3 hours 20 chaptersAdvanced
4 STUDYING NOW
course
Computer Vision Essentials with Python
Comprehensive introduction to Computer Vision, focusing on machine perception and interpretation of visual data. Covers image preprocessing, feature extraction, object detection, and deep learning techniques used in modern vision systems.
4 hours 29 chaptersIntermediate
3 STUDYING NOW
course
Loss Functions in Machine Learning
A comprehensive theoretical exploration of loss functions in machine learning, covering mathematical foundations, geometric intuition, and practical implications for model optimization and evaluation.
2 hours 15 chaptersIntermediate
course
Bio-Inspired Algorithms with Python
Explore the foundations and practical applications of bio-inspired algorithms in Python. This course covers evolutionary computation, swarm intelligence, and other nature-inspired optimization techniques, using only standard Python and permitted scientific libraries.
1 hour 16 chaptersBeginner
course
Explainable AI (XAI) Basics
Gain a foundational understanding of Explainable AI (XAI): what it is, why it matters, key concepts, main techniques, and ethical considerations. This course is theory-focused, using clear explanations and quizzes to build your intuition about making AI systems more transparent and trustworthy.
1 hour 15 chaptersBeginner
course
Feature Encoding Methods in Python
Master advanced categorical feature encoding methods in Python, including Weight-of-Evidence, Leave-one-out, Helmert, and high-cardinality encodings. Learn to avoid encoding leakage and apply robust techniques for real-world data science projects.
1 hour 9 chaptersIntermediate
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
Sorry... We can't find
what you're looking for
Career tracks
Courses & Projects
Technologies
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
2 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 β 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
20 STUDYING NOW
course
Introduction to NLP with Python
Explore the fundamentals of Natural Language Processing (NLP) by learning essential text preprocessing techniques and methods for representing text data. Gain practical experience with the tools used to clean, analyze, and interpret textual information. Develop the skills needed to transform raw language into structured insights, laying a strong foundation for advanced applications in artificial intelligence and machine learning.
5 hours 29 chaptersAdvanced
1 STUDYING NOW
course
Classification with Python
Master the core classification algorithms that power modern machine learning. Explore how models like k-NN, logistic regression, decision trees, and random forests make predictions, evaluate their accuracy, and understand when to use each. Build the skills to compare models and choose the best one for your data.
3 hours 24 chaptersIntermediate
2 STUDYING NOW
course
Cluster Analysis with Python
Gain a solid understanding of cluster analysis, a key unsupervised learning technique for uncovering patterns in unlabeled data. Explore the essentials of K-Means, Hierarchical Clustering, DBSCAN, and GMMs, and get hands-on experience with real datasets to build confidence in applying clustering to real-world problems.
4 hours 34 chaptersIntermediate
5 STUDYING NOW
course
Recurrent Neural Networks with Python
Master Recurrent neural networks and their advanced variants like LSTMs and GRUs using PyTorch. Gain hands-on experience processing sequential data for practical applications. Apply these powerful models to tackle real-world challenges in time series forecasting and various Natural language processing tasks.
3 hours 22 chaptersIntermediate
1 STUDYING NOW
course
Data Preprocessing and Feature Engineering with Python
Learn practical techniques to clean, transform, and engineer data for machine learning using Python. This course covers essential preprocessing steps, feature creation, and hands-on challenges to prepare data for modeling.
1 hour 12 chaptersBeginner
10 STUDYING NOW
course
PyTorch Essentials
Learn the fundamental and advanced concepts needed to work with PyTorch efficiently. Gain a solid understanding tensors, including creation, operations, and reshaping. Explore the essentials of gradients, backpropagation, and linear regression before moving on to handling datasets. Master the skill needed build, train, and evaluate neural networks.
3 hours 20 chaptersAdvanced
4 STUDYING NOW
course
Computer Vision Essentials with Python
Comprehensive introduction to Computer Vision, focusing on machine perception and interpretation of visual data. Covers image preprocessing, feature extraction, object detection, and deep learning techniques used in modern vision systems.
4 hours 29 chaptersIntermediate
3 STUDYING NOW
course
Loss Functions in Machine Learning
A comprehensive theoretical exploration of loss functions in machine learning, covering mathematical foundations, geometric intuition, and practical implications for model optimization and evaluation.
2 hours 15 chaptersIntermediate
course
Bio-Inspired Algorithms with Python
Explore the foundations and practical applications of bio-inspired algorithms in Python. This course covers evolutionary computation, swarm intelligence, and other nature-inspired optimization techniques, using only standard Python and permitted scientific libraries.
1 hour 16 chaptersBeginner
course
Explainable AI (XAI) Basics
Gain a foundational understanding of Explainable AI (XAI): what it is, why it matters, key concepts, main techniques, and ethical considerations. This course is theory-focused, using clear explanations and quizzes to build your intuition about making AI systems more transparent and trustworthy.
1 hour 15 chaptersBeginner
course
Feature Encoding Methods in Python
Master advanced categorical feature encoding methods in Python, including Weight-of-Evidence, Leave-one-out, Helmert, and high-cardinality encodings. Learn to avoid encoding leakage and apply robust techniques for real-world data science projects.
1 hour 9 chaptersIntermediate
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
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
2 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 β 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
20 STUDYING NOW
course
Introduction to NLP with Python
Explore the fundamentals of Natural Language Processing (NLP) by learning essential text preprocessing techniques and methods for representing text data. Gain practical experience with the tools used to clean, analyze, and interpret textual information. Develop the skills needed to transform raw language into structured insights, laying a strong foundation for advanced applications in artificial intelligence and machine learning.
5 hours 29 chaptersAdvanced
1 STUDYING NOW
course
Classification with Python
Master the core classification algorithms that power modern machine learning. Explore how models like k-NN, logistic regression, decision trees, and random forests make predictions, evaluate their accuracy, and understand when to use each. Build the skills to compare models and choose the best one for your data.
3 hours 24 chaptersIntermediate
2 STUDYING NOW
course
Cluster Analysis with Python
Gain a solid understanding of cluster analysis, a key unsupervised learning technique for uncovering patterns in unlabeled data. Explore the essentials of K-Means, Hierarchical Clustering, DBSCAN, and GMMs, and get hands-on experience with real datasets to build confidence in applying clustering to real-world problems.
4 hours 34 chaptersIntermediate
5 STUDYING NOW
course
Recurrent Neural Networks with Python
Master Recurrent neural networks and their advanced variants like LSTMs and GRUs using PyTorch. Gain hands-on experience processing sequential data for practical applications. Apply these powerful models to tackle real-world challenges in time series forecasting and various Natural language processing tasks.
3 hours 22 chaptersIntermediate
1 STUDYING NOW
course
Data Preprocessing and Feature Engineering with Python
Learn practical techniques to clean, transform, and engineer data for machine learning using Python. This course covers essential preprocessing steps, feature creation, and hands-on challenges to prepare data for modeling.
1 hour 12 chaptersBeginner
10 STUDYING NOW
course
PyTorch Essentials
Learn the fundamental and advanced concepts needed to work with PyTorch efficiently. Gain a solid understanding tensors, including creation, operations, and reshaping. Explore the essentials of gradients, backpropagation, and linear regression before moving on to handling datasets. Master the skill needed build, train, and evaluate neural networks.
3 hours 20 chaptersAdvanced
4 STUDYING NOW
course
Computer Vision Essentials with Python
Comprehensive introduction to Computer Vision, focusing on machine perception and interpretation of visual data. Covers image preprocessing, feature extraction, object detection, and deep learning techniques used in modern vision systems.
4 hours 29 chaptersIntermediate
3 STUDYING NOW
course
Loss Functions in Machine Learning
A comprehensive theoretical exploration of loss functions in machine learning, covering mathematical foundations, geometric intuition, and practical implications for model optimization and evaluation.
2 hours 15 chaptersIntermediate
course
Bio-Inspired Algorithms with Python
Explore the foundations and practical applications of bio-inspired algorithms in Python. This course covers evolutionary computation, swarm intelligence, and other nature-inspired optimization techniques, using only standard Python and permitted scientific libraries.
1 hour 16 chaptersBeginner
course
Explainable AI (XAI) Basics
Gain a foundational understanding of Explainable AI (XAI): what it is, why it matters, key concepts, main techniques, and ethical considerations. This course is theory-focused, using clear explanations and quizzes to build your intuition about making AI systems more transparent and trustworthy.
1 hour 15 chaptersBeginner
course
Feature Encoding Methods in Python
Master advanced categorical feature encoding methods in Python, including Weight-of-Evidence, Leave-one-out, Helmert, and high-cardinality encodings. Learn to avoid encoding leakage and apply robust techniques for real-world data science projects.
1 hour 9 chaptersIntermediate
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
Sorry... We can't find
what you're looking for
