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some time ago on the Internet to see the Coursera Open Classroom Big Machine learning Cornerstone Course, more comprehensive and clear machine learning needs of the basic knowledge, theoretical basis to explain. There are several more important concepts and ideas in foundation, first review, and then open the follow-up
July online April machine learning algorithm class notes--no.1
Objective
Machine learning is a multidisciplinary interdisciplinary, including probability theory, statistics, convex analysis, feature engineering and so on. Recently followed the July algorithm to learn the kno
1.1 machine learning basics-python deep machine learning, 1.1-python
Refer to instructor Peng Liang's video tutorial: reprinted, please indicate the source and original instructor Peng Liang
Video tutorial: http://pan.baidu.com/s/1kVNe5EJ
1. course Introduction
2. Machine
In machine learning, often need to calculate the distance between each sample, used for classification, according to distance, different samples grouped into a class; But in the current machine learning algorithm, the distance calculation mode is endless, then this blog is mainly to comb the current
Task View on Cran-r Language Machines Learning Package list, grouped by algorithm type.
Caret-r language 150 a unified interface for machine learning algorithms
Superlearner and subsemble-This package sets up a variety of machine learning algorithms
Introduction
This is already the third algorithm of machine learning. Speaking of the simple Bayes, perhaps everyone is not very clear what. But if you have studied probability theory and mathematical statistics, you may have some idea of Bayesian theorem, but you can't remember where it is. Yes, so important a theorem, in probability theory and mathematical
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Discriminant analysis is mainly in the statistics over there, so I am not very familiar with the temporary find statistics Department of the Boudoir Honey made up a missed lesson. Here we are now learning to sell.
A typical example of discriminant analysis is linear discriminant analysis (Linear discriminant analyses), referred to as LDA.
(notice here not to be
language is the same, but the syntax and API are slightly different.
R Project for statistical Computing: This is a development environment that employs a scripting language similar to Lisp. In this library, all the statistics-related features you want are available in the R language, including some complex icons. The code in the Machine learning direct
A bunch of online searches, and finally the links and differences between these concepts are summarized as follows:
1. Data mining: Mining is a very broad concept. It literally means digging up useful information from tons of data. This work bi (business intelligence) can be done, data analysis can be done, even market operations can be done. Using Excel to analyze the data and discover some useful information, the process of guiding your business through this information is also the process of
operator string • zoo performs regular and irregular time series operations
• Ggvis, lattice, and ggplot2 for data visualization
• Caret machine learning
How to use Python?
If your data analysis tasks require Web applications or code statistics to be integrated into the production database, you can use python as a fully sophisticated programming language, it i
data from the webpage, whether through the website API or the webpage capture module beauul ul Soap. Data can be collected through web crawling and applied to machine learning algorithms.
4. In the last step, you must Learn machine learning tools, such as Scikit-Learn, or execute the
", a book written by Chinese scientists, is quite understandable.6. "Managing gigabytes", a good book of information retrieval.7. "Information theory:inference and Learning Algorithms", reference books, relatively deep.Relevant mathematical basis (reference books, not suitable to read through):1. Linear algebra: This reference book is not listed, many.2. Matrix Mathematics:"Matrix Analysis", Roger Horn. The undisputed classic of matrix analysis.3. Pro
: http://blog.echen.me
A post/month, a more practical topic
Machine learning, http://www.machinedlearnings.com
A post/month, more practical topic, usually around big data learning
Flowingdata, http://flowingdata.com
A thread/day, mainly to solve some statistical problems
linear kernel)The neural network works well in all kinds of n, m cases, and the defect is that the training speed is slow.Reference documents[1] Andrew Ng Coursera public class seventh week[2] Kernel Functions for machine learning applications. http://crsouza.com/2010/03/kernel-functions-for-machine-learning-applicati
-Node.js.
Support Vector Machine for Node-SVM-Node.js
Neural Networks implemented by brain-Javascript
The implementation of the Bayesian-bandit-Bayesian bandit algorithm is used by node. js and browsers.
Julia General Machine Learning
The probability graph model framework implemented by PGM-Julia.
The normalized discriminant analysis package implemented by
: http://blog.echen.me
A post/month, a more practical topic
Machine learning, http://www.machinedlearnings.com
A post/month, more practical topic, usually around big data learning
Flowingdata, http://flowingdata.com
A thread/day, mainly to solve some statistical problems
browsers.
Julia General Machine Learning
The probability graph model framework implemented by PGM-Julia.
The normalized discriminant analysis package implemented by Da-Julia.
Regression-regression analysis algorithm package (such as linear regression and logistic regression ).
Local regression-local regression, very smooth!
Simple Julia Implementation of Naive Bayes-Naive Bayes
Mixed models-(
Learning: Large file statistics and sortingThis is the main record of learning Aboutspeaker classmate of the following problem of the algorithm thinking and code.The topic is this:
There are 10 files, each file 1G, each file is stored in each line of the user's query (please randomly generated), each file can be repeated query. Ask you to sort by the fre
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