examples of machine learning projects

. Xtas Our team of colleagues previously released Xtas, also Python-based text mining toolkit, welcome, Link: Http://t.cn/RPbEZOW. Look good, look back and try it. 3. Python Scientific Computing ToolkitNumpy, Scipy, Matplotlib, IPython4. Python machine learning and Data Mining toolkit Scikit-learn The famous Scikit-learn,scikit-learn is an open-source

2019 Machine Learning: Tracking the path of AI developmentHttps://mp.weixin.qq.com/s/HvAlEohfSEJMzRkH3zZtlwThe time has come to "guide" the "Smart assistant". Machine learning has become one of the key elements of the global digital transformation, and in the enterprise domain, the growth of

11.1 What to do first11.2 Error AnalysisError measurement for class 11.3 skew11.4 The tradeoff between recall and precision11.5 Machine-Learning data11.1 what to do firstThe next video will talk about the design of the machine learning system. These videos will talk about the major problems you will encounter when desi

computer, and each instruction represents one or more operations.Give a simple example, and you can use it in your life. Now make a small game, a on the paper randomly wrote a 1 to 100 integer, b to guess, guess the game is over, guess the wrong word a will tell B guess small or big. So what will b do, the first time you must guess 50, guess the middle number. Why is it? Because of this worst case scenario (log2100">Log2log2100) Six or seven times can be guessed.This is a binary search, which m

Here are some general basics, but it's still very useful to actually do machine learning. As the key to the application of machine learning on current projects such as recommender systems and DSPs, I think data processing is very important because in many cases,

engineering: A computer program was said to learn from experience E with respect to some task T and some performance measure p, if it p Erformance on T, as measured by P, improves with experience E. So if you want your program to be able to predict things like traffic patterns at a busy intersection, you can use a machine learning algorithm that loads data about past traffic patterns and then

using HMMs to answer one of the most fundamental questions in Biology–how are DNA, The Code of Life, translated to physical or behavioral attributes of an organism? The course is comprised of videos and runs a total time of 4 hours. It currently averages a 4.7/5 user rating. However, the course isn't free, it costs $.9. DATA Science and machine learning with Python–hands on! ($)Frank KaneIf you ' ve

algorithm, decision tree, Naive Bayes, logistic regression, support vector machine, etc.Unsupervised learning (unsupervised learning): Contrary to supervised learning, the data set is completely untagged, the main basis is that similar samples in the data space of the general distance is similar, so that the distance

translation table (it's just another conditional probability table, after all). A lot of interesting work is being done. The most ambitious and exciting is to completely abandon the current machine translation pipeline method, directly from the depth model to learn a translation model from beginning to end. The interesting thing here is that the output is structured (a joint distribution of a word sequence), and not simply a point prediction (because

Part I: ClassificationThe first two parts of the book focus on supervised Learning (supervisedieaming). In the process of supervising learning, we only need to give the input sample set , and the machine can push the possible results of the specified target variable from it. Supervised learning is relatively simple, an

This article is a translation of the article, but I did not translate the word by word, but some limitations, and added some of their own additions.Machine Learning (machines learning, ML) is what, as a mler, is often difficult to explain to everyone what is ML. Over time, it is found to understand or explain what machine lea