Discover forrester wave machine learning data catalogs, include the articles, news, trends, analysis and practical advice about forrester wave machine learning data catalogs on alibabacloud.com
"Stove-refining AI" machine learning 045-Modeling of stock data by hidden Markov model(Python libraries and version numbers used in this article: Python 3.6, Numpy 1.14, Scikit-learn 0.19, matplotlib 2.2)Stock data is very very typical timing data, the
algorithm)Feature Selection (Feature selection algorithm):Mutual information (Mutual information), Documentfrequence (document frequency), information Gain (information gain), chi-squared test (Chi-square test), Gini (Gini coefficient).Outlier Detection (anomaly detection algorithm):Statistic-based (based on statistics), distance-based (distance based), density-based (based on density), clustering-based (based on clustering).Learning to Rank (based o
)Feature Selection (Feature selection algorithm):Mutual information (Mutual information), Documentfrequence (document frequency), information Gain (information gain), chi-squared test (Chi-square test), Gini (Gini coefficient).Outlier Detection (anomaly detection algorithm):Statistic-based (based on statistics), distance-based (distance based), density-based (based on density), clustering-based (based on clustering).Learning to Rank (based on
are wrong):The focus of machine learning is to predict new cases with some known answers.The dots in blue indicate one case, and the red dots indicate a different case. So give a new point, how to tell if it belongs to the blue category or the red one?The answer is to ask for distance. (in the classic case seems to be looking for new points ah, to determine the new case ah, a variety of complex balabala)Th
Data imbalance in Machine Learning Recently, I encountered a problem where the positive data is much less than the negative data. Such a dataset will make the learned model more biased towards negative prediction results during machine
This article is a series of tutorials in the first part of the tutorial on using the machine learning capability workflow from scratch in Python, covering algorithmic programming and other related tools from the start of the group. Will eventually become a set of hand-crafted machine language work packages. This time the content will begin with
Here is the note for lecture three.
The Linear ModelLinear Model is a basic and important model in machine learning.1. Input RepresentationThe data we get usually needs some changes, most of them is the input data.In linear model,Input = (x1, x2, X3, X4, x5... XN)Then the model will beModel = (W1, W2, W3, W4, w5... wn)That means we shoshould use our
This is a creation in
Article, where the information may have evolved or changed.
Catalogue [−]
Iris Data Set
KNN k Nearest Neighbor algorithm
Training data and Forecasts
Evaluation
Python Code implementation
This series of articles describes how to use the Go language for data analysis and machine
What is http://www.quora.com/What-is-data-science data science?Http://www.quora.com/How-do-I-become-a-data-scientist how can I become a data scientist?Http://www.quora.com/Data-Science/How-does-data-science-differ-from-traditional
At present, machine learning is one of the hottest technologies in the industry.With the rapid development of computer and network, machine learning plays a more and more important role in our life and work, and it is changing our life and work. From the daily use of the camera, daily use of the search engine, online e
hypothesis could not being built up,Generlly the number of hypothesisThat can is built is less than a^b.Let's come back to the inequlity, we can prove it mathematically thatif M can be replaced by a polynomial, which means the number of hypothesis in a set are not infinite and then we can declar E that learning was feasible using this hypothesis set.There is a new statement this wil be proved next lecture, if the maxnum of hypothesis are less than it
Algorithms have become an important part of our daily lives, and they almost appear in any area of business. Gartner, the research firm, says the phenomenon is "algorithmic commerce", where algorithmic commerce is changing the way we operate and manage companies. Now you can buy these various algorithms for each business area on the "algorithmic market". The algorithmic market provides developers with more than 800 algorithms, including sound and visual processing,
Spark Machine Learning Mllib Series 1 (for Python)--data type, vector, distributed matrix, API
Key words: Local vector,labeled point,local matrix,distributed Matrix,rowmatrix,indexedrowmatrix,coordinatematrix, Blockmatrix.Mllib supports local vectors and matrices stored on single computers, and of course supports distributed matrices stored as RDD. An example of
With the growth of application data, statistical analysis and machine learning are becoming a big challenge in large datasets. Currently, there are many languages/libraries for statistical analysis/machine learning, such as the R language designed for
are two issues to note:1, if the data is linearly non-divided.When the data is linearly non-divided, we can also use the above method, but will come to an unacceptable solution, at this time we can detect whether the solution is valid to determine whether our data can be divided.2. What happens if W0 exists in Z?In our previous assumptions, W0 represents a const
Netfei is a DVD leasing company. by increasing its sales by 10%, it can earn 1 million RMB in revenue, which is very impressive.
How to: predict consumers' ratings for movies? (Increase the predicted value by 10 percentage points through their own systems) if the recommendations you provide to consumers are very accurate, the consumers will be very satisfied.
The essence of machine learning: 1. An existin
This lesson mainly describes the processing of linear models.
Including:
1. Input Representation)
2. Linear Classification)
3. Linear Regression)
4. nonlinear transformation)
The author believes that to test the availability of a model, it is to use real data to do a good job.
To explain how to apply linear models, the author uses linear models to solve the problem of post office data identification:
Becau
Original: Http://www.infoq.com/cn/news/2014/03/baidu-salon48-summaryMarch 15, 2014, in the 48th phase of Baidu Technology salon, sponsored by @ Baidu, @InfoQ responsible for organizing and implementing, from Baidu Alliance Big Data Machine Learning technology responsible for summer powder, and Sogou precision Advertising Research and development Department of tec
The content source of this page is from Internet, which doesn't represent Alibaba Cloud's opinion;
products and services mentioned on that page don't have any relationship with Alibaba Cloud. If the
content of the page makes you feel confusing, please write us an email, we will handle the problem
within 5 days after receiving your email.
If you find any instances of plagiarism from the community, please send an email to:
info-contact@alibabacloud.com
and provide relevant evidence. A staff member will contact you within 5 working days.