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 critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded 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. 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.) By the end of this Specialization, you will have mastered key concepts and gained the practical know-how to quickly and powerfully apply machine learning to challenging real-world problems.

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 critical research at Stanford University and groundbreaking work at Google Brain, Baidu, and Landing.AI to advance the AI field. This 3-course Specialization is an updated and expanded 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. 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.)

When applying for a programming or data science job, machine learning certifications and certificates have the potential to help you stand out from the crowded pool of candidates. Whether you've just completed a course of study or passed an exam offered by a respected institution, obtaining a certificate or certification is a real accomplishment that indicates your knowledge, experience, and expertise in the field of machine learning. But, what certificates and certifications are right for you? In this article, you'll learn more about the difference between certificates and certifications and explore five of the most popular ones for machine learning available today. Though they are often confused, certificates and certifications are not the same.

Machine Learning is very powerful and many people are shifting their careers into the Machine learning field. The reason behind machine learning popularity is its power to make useless data into more meaningful data. Coursera has a wide range of Machine Learning courses. That's why I have listed the 10 Best Courses for Machine Learning on Coursera. So give your few minutes and find out Best Courses for Machine Learning on Coursera for you.

This course is Free to Audit and good for understanding more about the ethics behind data science. In this course, you will get to know about the framework to analyze ethical considerations regarding the privacy and control of consumer information and big data. This course will cover the following questions- Who owns data, How do we value privacy, How to receive informed consent, and What it means to be fair.