examples of machine learning projects

Source: paperweekly This article a total of 900 characters, recommended to read 6 minutes.This article lists the top ten interesting machine learning open source projects for you recently GitHub.-01- Face recognition #世界上最简单的人脸识别库 This project is known as the simplest face recognition library in the world and can be invoked using Python and the command line.

See Professor Max Welling on the home page there are a lot of learning notes, a collection of it, its recently published a book it has not yet seen.Http://www.ics.uci.edu/~welling/classnotes/classnotes.htmlStatistical Estimation [PS]-Bayesian estimation-Maximum a posteriori (MAP) estimation-Maximum likelihood (ML) estimation-Bias/variance Tradeoff Minimum description length (MDL)expectation maximization (EM) algorithm [PS]- Detailed derivation plus

sets.3. CodeThe following code from the "Machine Learning Combat" a book, the original book all the code examples can be in the site: here. To download.def classify0 (InX, DataSet, labels, k): datasetsize = dataset.shape[0] Diffmat = Tile (InX, (datasetsize,1))-Dat ASet Sqdiffmat = diffmat**2 sqdistances = sqdiffmat.sum (Axis=1) distances = sqdist

" respectively have 3, 2, 2 kinds of possible value, also consider the attribute can take any value, such as: Regardless of color, root pedicle = curl up and knock sound = cloudy sound melon are good melon. So each attribute has a value of * (any value) in addition to the value of a single meaning. Therefore, the above problems will produce 4*3*3=36 hypothesis altogether. This set of all assumptions is called the hypothetical space. The hypothetical space for the watermelon problem is shown in t

little use.####################### #小 ********** Knot ###############################1, here is simply a hmm model to analyze the stock data examples, although the practical value is not small, but can give other complex algorithms to provide a little thought.2, or that sentence, away from the stock market, away from harm.#################################################################Note: This section of the code has been uploaded to ( my GitHub),

ArticleDirectory Welcome to Deep Learning SVM Series Explore python, machine learning, and nltk Libraries 8. http://deeplearning.net/Welcome to Deep Learning 7. http://blog.csdn.net/zshtang/article/category/870505 SVD and LSI tutorial 6. http://blog.csdn.net/shikai1030/article/details/7182312 Gaussian di

For the practical application of machine learning, the light stays in the understanding of the level is not enough, we need to find some problems in the actual in-depth mining understanding. I'm going to make a tidy up of some trivial knowledge points.1 Data imbalance issuesThis problem is often encountered. Take a supervised study of the two classification problem, we need a positive example and a negative

1. What are decision Trees (decision tree) Decision tree is a tree structure similar to a flowchart, where each tree node represents a test on an attribute, Each branch represents the output of a property, and each leaf node represents the distribution of a class or class, and the topmost layer of the tree is the root node of the Tree. cite an example. Xiao Ming students want to enjoy swimming according to the weather: There are 6 properties, a sample is an example, the concept of

1. Alternating Least SquareALS (Alternating Least Square), alternating least squares. In machine learning, a collaborative recommendation algorithm using least squares method is specified. As shown, u represents the user, v denotes the product, the user scores the item, but not every user will rate each item. For example, user U6 did not give the product V3 scoring, we need to infer that this is the task of

One problem: Beautiful StringThis is the 2014 Microsoft School Recruit programming problem, test instructions roughly as follows:If a string consists of three or more groups of consecutive ascending letters, each set of equal lengths, then we call this string beautiful Examples of compliant beautiful string: ABC, CDE, AABBCC, AAABBBCCC Inconsistent Beautiful string Example: Abd,cba,aabbc,zabEnter a string containing only lowercase letters

model can converge to the real model faster; When there are hidden variables, throw can be used to learn the method of generation, when the discriminant method can not be used Discriminant method and discriminant modelDiscriminant Model: Finite sample = = "discriminant function = Predictive model = =" PredictionThe discriminant method is directly studied by the data decision function f (X) or conditional probability distribution P (y| X) as a predictive model, i.e. a discriminant

For the practical use of machine learning. It is not enough to know the level of light, and we need to dig deeper into the problems encountered in the practical. I'm going to make a tidy up of some trivial knowledge points.1 Data imbalance issuesThis problem is often encountered.Take a supervised study of the two classification problem. We need the annotations of both positive and negative

In the process of learning machine learning algorithms, we often need data to validate algorithms and debug parameters. But it's not that easy to find a set of data samples that are perfectly suited to a particular type of algorithm. Fortunately NumPy, Scikit-learn all provide the function of random data generation, we can generate data for a certain model oursel

This series is a personal learning note for Andrew Ng Machine Learning course for Coursera website (for reference only)Course URL: https://www.coursera.org/learn/machine-learning Exercise 7--k-means and PCA Download coursera-Wunda-Machin