19000 courses for machine learning

Machine Learning

Andrew Ng
Stanford University via Coursera

best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine

Machine Learning Foundations: A Case Study Approach

Carlos Guestrin and Emily Fox
University of Washington via Coursera

understanding tasks of interest, matching these tasks to machine learning tools, and assessing the quality of the output. In subsequent courses, you will delve into the components of this black box by examining models and algorithms. Together, these pieces form the machine learning pipeline, which you will use in developing intelligent applications. Learning Outcomes: By the end of this course, you will be able to: -Identify potential

Advanced Machine Learning

Ryan Adams
Harvard University

This course is about learning to extract statistical structure from data, for making decisions and predictions, as well as for visualization. The course will cover many of the most important math- ematical and computational tools for probabilistic modeling, as well as examine specific models from the literature and examine how they can be used for particular types of data. There will be a heavy emphasis on implementation. You may use Matlab, Python or R. Each of the five assign- ments will involve some amount of coding, and the final project will almost certainly require the running of computer experiments.

Learning From Data (Introductory Machine Learning)

Yaser S. Abu-Mostafa
Caltech via edX

This introductory computer science course in machine learning will cover basic theory, algorithms, and applications. Machine learning is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to automatically learn how to perform a desired task based on information extracted from the data. Machine learning has become one of the hottest fields of study today and

Machine Learning Engineer Nanodegree

Arpan Chakraborty, David Joyner, Luis Serrano, Sebastian Thrun, Vincent Vanhoucke, and Katie Malone

Machine learning represents a key evolution in the fields of computer science, data analysis, software engineering, and artificial intelligence. This program will teach you how to become a machine learning engineer, and apply predictive models to massive data sets in fields like finance, healthcare, education, and more. Syllabus: 1. P0: Titanic Survival Exploration: In this optional project, you will create decision functions that attempt to

Intro to Machine Learning | ECE, Virginia Tech | Spring

Virginia Polytechnic Institute and State University (Virginia Tech)

Introduction to Machine Learning Virginia Tech, Electrical and Computer Engineering Spring 2015: ECE 5984 Course Information Have you ever wondered how Siri understands voice commands? How Netflix recommends movies to watch? How Kinnect recognizes full-body gestures? How Watson defeated Jeopardy champions? The answer is Machine Learning -- the study of algorithms that learn from large quantities of data, identify patterns

Prediction: Machine Learning and Statistics

Cynthia Rudin
MIT OpenCourseWare

Prediction is at the heart of almost every scientific discipline, and the study of generalization (that is, prediction) from data is the central topic of machine learning and statistics, and more generally, data mining. Machine learning and statistical methods are used throughout the scientific world for their use in handling the "information overload" that characterizes our current digital age. Machine learning developed from the artificial

Machine Learning A-Z™: Hands-On Python & R In Data Science

Kirill Eremenko, Hadelin de Ponteves, and SuperDataScience Team

Description Interested in the field of Machine Learning? Then this course is for you! This course has been designed by two professional Data Scientists so that we can share our knowledge and help you learn complex theory, algorithms and coding libraries in a simple way. We will walk you step-by-step into the World of Machine Learning. With every tutorial you will develop new skills and improve your understanding of this

Data Science: Supervised Machine Learning in Python

Lazy Programmer Inc.

03:54 Comparison to Deep Learning 04:39 Multiclass Classification 03:20 Sci-Kit Learn 09:02 Regression with Sci-Kit Learn is Easy 05:50 Building a Machine Learning Web Service 2 Lectures 10:22 Building a Machine Learning Web Service Concepts 04:10 Building a Machine Learning Web Service Code 06:12 Conclusion 1 Lecture 02:50 What’s Next? Support Vector Machines and Ensemble Methods (e.g. Random Forest) 02:50 Appendix 4 Lectures 45:09 How to

Machine Learning

Tom Mitchell
Carnegie Mellon University

Machine Learning is concerned with computer programs that automatically improve their performance through experience (e.g., programs that learn to recognize human faces, recommend music and movies, and drive autonomous robots). This course covers the theory and practical algorithms for machine learning from a variety of perspectives. We cover topics such as Bayesian networks, decision tree learning, Support Vector Machines, statistical learning