Today we're adding MIT's course on Artificial Intelligence to our ever-growing collection, 1200 Free Online Courses from Top Universities. Featuring 30 lectures, MIT's course "introduces students to the basic knowledge representation, problem solving, and learning methods of artificial intelligence." It includes interactive demonstrations designed to "help students gain intuition about how artificial intelligence methods work under a variety of circumstances." And, by the end of the course, students should be able "to develop intelligent systems by assembling solutions to concrete computational problems; understand the role of knowledge representation, problem solving, and learning in intelligent-system engineering; and appreciate the role of problem solving, vision, and language in understanding human intelligence from a computational perspective."
IN JULY 2011 Sebastian Thrun, who among other things is a professor at Stanford, posted a short video on YouTube, announcing that he and a colleague, Peter Norvig, were making their "Introduction to Artificial Intelligence" course available free online. In 2012 Mr Thrun founded an online-education startup called Udacity, and Mr Ng co-founded another, called Coursera. Even outside the AI community, there is a broad consensus that technological progress, and artificial intelligence in particular, will require big changes in the way education is delivered, just as the Industrial Revolution did in the 19th century. Community colleges are setting up all kinds of schemes that combine education with learning on the job, says Mr Bessen.
Learners will implement and apply predictive, classification, clustering, and information retrieval machine learning algorithms to real data sets throughout each course in the specialization. You will apply basic data science tools, including data management and visualization, modeling, and machine learning using your choice of either SAS or Python, including pandas and Scikit-learn. Description: Learn scalable data management, evaluate big data technologies, and design effective visualizations This Specialization covers intermediate topics in data science. Building on Course 3, which introduces students to integral supervised machine learning concepts, this course will provide an overview of many additional concepts, techniques, and algorithms in machine learning, from basic classification to decision trees and clustering.
Deep Learning For Coders is a new online course that, for the first time, promises to teach coders how to create state of the art deep learning models. Jeremy says that this is First deep learning course to show end-to-end how to get state of the art results (including how to get a top place in a Kaggle competition) First code-centric full deep learning course (18 hours of lessons) First time that nearly every part of a convolutional neural net has been implemented as a spreadsheet! First deep learning course to show end-to-end how to get state of the art results (including how to get a top place in a Kaggle competition) First code-centric full deep learning course (18 hours of lessons) First time that nearly every part of a convolutional neural net has been implemented as a spreadsheet! First time that nearly every part of a convolutional neural net has been implemented as a spreadsheet!
In this introductory course, the "Backyard Data Scientist" will guide you through wilderness of Machine Learning for Data Science. Accessible to everyone, this introductory course not only explains Machine Learning, but where it fits in the "techno sphere around us", why it's important now, and how it will dramatically change our world today and for days to come. We'll then explore the past and the future while touching on the importance, impacts and examples of Machine Learning for Data Science: To make sense of the Machine part of Machine Learning, we'll explore the Machine Learning process: Our final section of the course will prepare you to begin your future journey into Machine Learning for Data Science after the course is complete. So I invite you to join me, the Backyard Data Scientist on an exquisite journey into unlocking the secrets of Machine Learning for Data Science.... for you know - everyday people... like you!
We've already learned some classic machine learning models like k-nearest neighbor and decision tree. In this course you'll study ways to combine models like decision trees and logistic regression to build models that can reach much higher accuracies than the base models they are made of. In particular, we will study the Random Forest and AdaBoost algorithms in detail. Since deep learning is so popular these days, we will study some interesting commonalities between random forests, AdaBoost, and deep learning neural networks.
The course covers supervised learning concepts, which require labeled training data. The supervised techniques include various types of linear regression, decision trees, k-nearest neighbors, Naive Bayes, support vector machines and ensemble methods. Unsupervised machine learning including clustering techniques and other advanced topics will be covered in separate follow-up courses. Students will complete a data mining project using the supervised algorithms learned in class.
About this course: Case Study - Predicting Housing Prices In our first case study, predicting house prices, you will create models that predict a continuous value (price) from input features (square footage, number of bedrooms and bathrooms,...). In this course, you will explore regularized linear regression models for the task of prediction and feature selection. You will be able to handle very large sets of features and select between models of various complexity. You will also analyze the impact of aspects of your data -- such as outliers -- on your selected models and predictions.
Right now, Machine Learning and Data Science are two hot topics, the subject of many courses being offered at universities today. Above, you can watch a playlist of 18 lectures from a course called Learning From Data: A Machine Learning Course, taught by Caltech's Feynman Prize-winning professor Yaser Abu-Mostafa. This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. Learning From Data will be permanently added to our list of Free Online Computer Science Courses, part of our ever-growing collection, 1200 Free Online Courses from Top Universities.
This tutorial will introduce the use of Python for statistical data analysis, using data stored as Pandas DataFrame objects. Finally, participants will be introduced to methods for statistical data modeling using some of the advanced functions in Numpy, Scipy and Pandas. For students familiar with Git, you may simply clone this repository to obtain all the materials (iPython notebooks and data) for the tutorial. He specializes in computational statistics, Bayesian methods, meta-analysis, and applied decision analysis.