Build and train a neural network with TensorFlow to perform multi-class classification. Build and train supervised machine learning models for prediction and binary classification tasks, including linear regression and logistic regression. Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn. Unsupervised Learning, Recommenders, Reinforcement Learningīy the end of this Specialization, students will be ready to: Supervised Machine Learning: Regression and Classification There are three courses in this specialization: It provides a broad introduction to modern machine learning, including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), and some of the best practices used in Silicon Valley for artificial intelligence and machine learning innovation (evaluating and tuning models, taking a data-centric approach to improving performance, and more.)īy the end of this specialization, students will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems. This 3-course specialization is an updated version of Andrew’s pioneering Machine Learning course, rated 4.9 out of 5 and taken by over 4.8 million learners since it launched in 2012. This specialization is 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. This beginner-friendly program will teach you the fundamentals of machine learning and how to use these techniques to build real-world AI applications. 
The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. Link to the specialization Coursera - Link Machine Learning Specialization DeepLearning.AI
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