Introduction to Machine Learning
Draft of Incomplete Notes by Nils J. nilsson@cs.stanford.edu Description (as of ): From this page you can download a draft of notes I used for a Stanford course on Machine Learning. The notes survey many of the important topics in machine learning circa the late 1990s. There have been many important developments in machine learning since these notes were written. Download the notes: Introduction to Machine Learning (2.1 MB) Although this draft says that these notes were planned to be a textbook, they will remain just notes. Nils J. nilsson@cs.stanford.edu Copyright © 2014 Nils J.
Machine Learning Repository
Main Page - Wiki Course Notes
Summary of course Machine Learning by Andrew Ng on Coursera – luckycallor
This is my summary of course Machine Learning by Andrew Ng on Coursera. You can have a reference here after finishing the course. I'm glad to communicate with you and learn from each other. If you find any mistakes in the article, I would appreciate it if you pointed them out. a pdf edition 1. For m examples with n features, we can use a matrix X (with m rows and n columns) to describe the data, where row vector xi (with n+1 dimension including x0 ) represents an example, while column vector xj (with m dimension) represents a feature; or in matrix X, every element xij (row i, column j) represent the jth feature of ith example. For parameters, we use vector θ with n+1 elements to describe, where θj is correspond to xj . For labels, we use a vector y (with m elements) to represent, where element yi represent the label of ith example. And for every 1≤i≤m,xi0=0 . Hypothesis: Vector version: hθ=X∗θ Element version: hθ(xi)=n∑j=0xijθj Cost function: J(θ)=12mm∑i=1(hθ(xi)−yi)2 Gradient descent: θ=(XTX)−1XTy
Machine Learning - complete course notes
The following notes represent a complete, stand alone interpretation of Stanford's machine learning course presented by Professor Andrew Ng and originally posted on the ml-class.org website during the fall 2011 semester. The topics covered are shown below, although for a more detailed summary see lecture 19. The only content not covered here is the Octave/MATLAB programming. All diagrams are my own or are directly taken from the lectures, full credit to Professor Ng for a truly exceptional lecture course. What are these notes? Originally written as a way for me personally to help solidify and document the concepts, these notes have grown into a reasonably complete block of reference material spanning the course in its entirety in just over 40 000 words and a lot of diagrams! The notes were written in Evernote, and then exported to HTML automatically. How can you help!? If you notice errors or typos, inconsistencies or things that are unclear please tell me and I'll update them. Content
Google Launches Free Course on Deep Learning: The Science of Teaching Computers How to Teach Themselves
Last Friday, we mentioned how Google's artificial intelligence software DeepMind has the ability to teach itself many things. It can teach itself how to walk, jump and run. Even take professional pictures. The free course takes about 3 months to complete. Follow Open Culture on Facebook and Twitter and share intelligent media with your friends. If you'd like to support Open Culture and our mission, please consider making a donation to our site. Related Content: Google’s DeepMind AI Teaches Itself to Walk, and the Results Are Kooky, No Wait, Chilling Learn Python: A Free Online Course from Google Take a Free Course on Digital Photography from Stanford Prof Marc Levoy
1.4. Support Vector Machines — scikit-learn 0.17 documentation
The support vector machines in scikit-learn support both dense (numpy.ndarray and convertible to that by numpy.asarray) and sparse (any scipy.sparse) sample vectors as input. However, to use an SVM to make predictions for sparse data, it must have been fit on such data. For optimal performance, use C-ordered numpy.ndarray (dense) or scipy.sparse.csr_matrix (sparse) with dtype=float64. 1.4.1. SVC, NuSVC and LinearSVC are classes capable of performing multi-class classification on a dataset. SVC and NuSVC are similar methods, but accept slightly different sets of parameters and have different mathematical formulations (see section Mathematical formulation). As other classifiers, SVC, NuSVC and LinearSVC take as input two arrays: an array X of size [n_samples,n_features] holding the training samples, and an array y of class labels (strings or integers), size [n_samples]: After being fitted, the model can then be used to predict new values: >>> clf.predict([[2., 2.]])array([1]) 1.4.1.1. and . by
Introduction to Artificial Intelligence (AI)
Lo que aprenderás In this course, you will learn how to: Build simple machine learning models with Azure Machine Learning; Use Python and Microsoft cognitive services to work with text, speech, images, and video; Use the Microsoft Bot Framework to implement conversational bots. Ver el programa del curso Ocultar el programa del curso Programa del curso Skip Syllabus DescriptionIntroductionMachine Learning – The Foundation of AIText and Speech – Understanding LanguageComputer Vision – Seeing the World Through AIBots – Conversation as a PlatformNext Steps Do I need an Azure subscription to complete the course?
