Machine Learning Specialization: Learn the Math, Theory, and Python Implementations
A rigorous online program designed to teach you the fundamentals of machine learning using Python, NumPy, and scikit-learn.
Enrolment
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Machine learning is the core technology powering the modern artificial intelligence revolution. To build a career in this field, you must understand both the mathematical theory and the practical software implementations that make models work.
The "Machine Learning Specialization," created by Andrew Ng and Stanford Online in collaboration with DeepLearning.AI, provides a comprehensive, hands-on introduction to these foundational concepts.
Supervised Machine Learning: Regression and Classification
The first course in the specialization focuses on supervised learning, where algorithms learn from labeled training data.
Linear Regression
Logistic Regression
Unsupervised Learning, Recommenders, and Reinforcement Learning
The second half of the specialization covers algorithms that learn from unlabeled data or interact with dynamic environments.
Clustering Algorithms
Recommender Systems
Reinforcement Learning
Hands-On Python Implementations
The specialization emphasizes practical coding exercises using industry-standard libraries.
Essential Data Libraries
Building Neural Networks
Visit coursera.org to enroll.