You must have seen computer programs that can perform complex tasks and are designed in a way that they are able to grow and change on their own when new data is entered. Such programs use the kind of artificial intelligence, named machine learning, making them very dynamic and possessing them with an ability to learn without being programmed covetously.  It has very vest implications in real life. If you want to learn more about it, you can refer to the following resources:

  1. Learning from Data (Introductory Machine Learning course)
    A self-paced, 10-week long course that will teach you the fundamentals of machine learning with respect to algorithms, theory, and applications. You will also have take-home lessons to put your education to practical applications with its regards.
  2. Machine Learning – Stanford University
    You will get instructed by the Director of the Stanford Artificial Intelligence Lab, Andrew Ng, in this course. Therefore, you will learn form a professional, equipping you with this science, using video lectures, of which some may be integrated with quiz questions.
  3. Intro to Machine Learning
    In this course, you will get to learn the very basics of machine learning to its practical applications and all the ways you can make use of this artificial intelligence to design computers.
  4. Principles of Autonomy and Decision Making
    An MIT course that will teach you variety of paradigms for creating highly autonomous systems and decision support aids, machine learning being one of them. You will have access to lecture notes, assessments and exams to get through this course.
  5. Prediction: Machine Learning and Statistics
    Since prediction is at heart of machine learning and statistics, excelling at it would make you much more prone to effectively making use of machine learning. It is an MIT course that will take you through the course using lecture notes, projects and examples and instructor insights.
  6. Introduction to Machine Learning
    Those who have prior knowledge of R and statistics will find this course perfectly fitting for their need to learn machine learning. Nor will it only enable them to learn the very basics of machine learning, but also equip them with three of the most basic machine learning tasks: classification, regression and clustering.
  7. Learning from Data
    For beginners to this field of science, it is an introductory machine learning course with video lectures, taught by Caltech Professor Yaser Abu-Mostafa. You will be an expert at the fundamentals of machine learning by the end of it.
  8. Machine Learning
    An MIT course that will start off with the very basic algorithms, concepts and techniques implored in machine learning to much newer concepts such as boosting and Bayesian networks.
  9. Machine Learning for Data Science and analytics
    You will get a good grip of the basic concepts of machine learning from this course and also derive practical solutions using predictive analytics.
  10. Machine Learning: Supervised Learning
    It is the first part to the three-part Machine Learning series, concerned with supervised learning, the task that makes it possible for your phone to recognize your voice, your email to filter spam and other cool stuff that the computer does.

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