fast_ml fast_ml-2013 fast_ml-2013-15 knowledge-graph by maker-knowledge-mining
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Introduction: How do you learn machine learning? A good way to begin is to take an online course. These courses started appearing towards the end of 2011, first from Stanford University, now from Coursera , Udacity , edX and other institutions. There are very many of them, including a few about machine learning. Here’s a list: Introduction to Artificial Intelligence by Sebastian Thrun and Peter Norvig. That was the first online class, and it contains two units on machine learning (units five and six). Both instructors work at Google. Sebastian Thrun is best known for building a self-driving car and Peter Norvig is a leading authority on AI, so they know what they are talking about. After the success of the class Sebastian Thrun quit Stanford to found Udacity, his online learning startup. Machine Learning by Andrew Ng. Again, one of the first classes, by Stanford professor who started Coursera, the best known online learning provider today. Andrew Ng is a world class authority on m
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1 There are very many of them, including a few about machine learning. [sent-4, score-0.21]
2 That was the first online class, and it contains two units on machine learning (units five and six). [sent-6, score-0.452]
3 After the success of the class Sebastian Thrun quit Stanford to found Udacity, his online learning startup. [sent-9, score-0.451]
4 Again, one of the first classes, by Stanford professor who started Coursera, the best known online learning provider today. [sent-11, score-0.474]
5 Andrew Ng is a world class authority on machine learning, and this course is a good place to start. [sent-12, score-0.643]
6 It features well chosen topics (notably missing are trees and ensembles) and programming assignments (Matlab/Octave). [sent-13, score-0.434]
7 The course seems very good - it’s engaging and well-presented. [sent-19, score-0.349]
8 Yet by watching it you can get a clue why the craze these days is data science, big data, deep learning etc. [sent-21, score-0.26]
9 This course has a strong emphasis on theory of learning. [sent-30, score-0.224]
10 Originally it was broadcasted live from Caltech site , so you could watch the lecture and ask your question afterwards by means of an online chat. [sent-34, score-0.453]
11 Part two about unsupervised learning and part three about reinforcement learning are coming. [sent-37, score-0.312]
12 This is not a machine learning course, but rather a linear algebra course. [sent-43, score-0.47]
13 Linear algebra, that is matrix and vector operations, is the foundation of machine learning. [sent-44, score-0.217]
14 So if you find yourself struggling with math a little bit this is the course to take. [sent-45, score-0.224]
15 The presentation is straightforward and application-oriented and the programming is done in Python. [sent-46, score-0.276]
16 An edX course from University of Texas at Austin, programming in Python. [sent-48, score-0.5]
17 One example is Computing for Data Analysis by Roger Peng, a very good introduction to R programming language. [sent-50, score-0.484]
18 Be sure to browse Udacity courses , they are generally easier to digest than Coursera’s. [sent-54, score-0.208]
19 Here are a few non-interactive resources, mainly course video lectures: Machine Learning by Pedro Domingos. [sent-59, score-0.362]
20 The course has an interesting and pretty comprehensive choice of topics and the content is good, even though video quality is not quite up to par with other courses. [sent-61, score-0.527]
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