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Andrew Ng’s Machine Learning Collection. Courses and specializations from leading organizations and universities, curated by Andrew Ng. Andrew Ng is founder of DeepLearning.AI, general partner at AI Fund, chairman and cofounder of Coursera, and an adjunct professor at Stanford University.
The Machine Learning Specialization is a foundational online program taught by Andrew Ng, an AI visionary who has led critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field.
Machine Learning Engineering for Production (MLOps) Specialization. Machine learning engineering for production refers to the tools, techniques, and practical experiences that transform theoretical ML knowledge into a production-ready skillset.
Build machine learning models in Python using popular machine learning libraries NumPy & scikit-learn. Build & train supervised machine learning models for prediction & binary classification tasks, including linear regression & logistic regression.
New Machine Learning Specialization, an updated foundational program for beginners created by Andrew Ng | Start Your AI Career Today 🌟 New Course! Enroll in Carbon Aware Computing for GenAI Developers
Andrew Ng Machine Learning Courses: Study machine learning with Andrew Ng for foundational knowledge. Learn about supervised learning, neural networks, and model evaluation.
Led by Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (gen...
150 courses. Andrew Ng is Founder of DeepLearning.AI, General Partner at AI Fund, Chairman and Co-Founder of Coursera, and an Adjunct Professor at Stanford University. As a pioneer both in machine learning and online education, Dr. Ng has changed countless ...
This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory ...
This course provides a broad introduction to machine learning and statistical pattern recognition. You will learn about both supervised and unsupervised learning as well as learning theory, reinforcement learning and control.
