Supervised Machine Learning: Regression and Classification

In the first year of the Machine Learning Specialization, you will: - Build machine learning models in Python using popular machine learning libraries NumPy and scikit-learn - Build and train supervised machine learning models for prediction and binary classification problems, including linear regression and logistic regression The Machine Learning Specialization is a foundational online program created in collaboration between DeepLearning.AI and Stanford Online. In this beginner-friendly program, you will learn the fundamentals of machine learning and how to use these techniques to build real-world AI applications. This specialization is taught by Andrew Ng, an AI visionary who has led landmark research at Stanford University and pioneering work at Google Brain, Baidu, and Landing.AI to advance the field of AI. This three-course specialization is an updated and expanded version of Andrew’s groundbreaking machine learning course, which has received a 4.9 out of 5 stars and has been taken by more than 4.8 million students since its introduction in 2012. The course provides a broad overview of 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 AI and machine learning innovation (model evaluation and tuning, a data-driven approach to improving performance, and more.) By the end of this specialization, you’ll master key concepts and gain the practical know-how to quickly and effectively apply machine learning to solve complex, real-world problems. If you’re looking to enter the AI ​​field or build a career in machine learning, the new Machine Learning specialization is the best place to start.