machine learning course on coursera

algorithms include q-learning and time difference learning (temporal difference learning)In the case of enterprise Data application, the most commonly used is the model of supervised learning and unsupervised learning. In the field of image recognition, semi-supervised

to use this feedback data to help us improve our current application. If the system is based on machine learning, log data can be used to improve the performance of applications. Of course, user behavior data is often noisy. We need to consider how to remove noise and Improve the Quality of log data. In another example, you may also know Amazon Mechanical Turk.

previous article Python machine learning "Getting Started"Body:In the previous introductory article, we mainly introduced two algorithms for machine learning tasks: supervised learning and unsupervised learning. Among them, the t

. Application scenarios of Naive Bayes An important application of machine learning is automatic document classification, while Naive Bayes is a common algorithm for document classification. The basic step is to traverse and record the words that appear in the document, and use the appearance or absence of each word as a feature. In this way, there are as many features as the number of words in the document

is very complete, combined with the later exercise with the R language of their own contact, for understanding the basic methods of machine learning is very helpful, such as: Logistic,ridge regression. The book can also be downloaded directly to the electronic version on the author's website. http://statweb.stanford.edu/~tibs/ElemStatLearn/ With a theoretical basis, combined with a number of professors of

-plane in a high-dimensional space separates the data points, which involves the mapping of non-linear data to high-dimensional to achieve the purpose of linear divisible data.Support Vector Concepts:The above sample map is a special two-dimensional situation, of course, the real situation may be many dimensions. Start with a simple understanding of what a support vector is at a low latitude. Can see 3 lines, the middle of the red line to the other tw

Deep learning of wheat-machine learning Algorithm Advanced StepEssay background: In a lot of times, many of the early friends will ask me: I am from other languages transferred to the development of the program, there are some basic information to learn from us, your frame feel too big, I hope to have a gradual tutorial or video to learn just fine. For

input data directly feedback to the model, the model must be immediately adjusted. Common application scenarios include dynamic systems and robot control. Common algorithms include q-learning and time difference learning (temporal difference learning)In the case of enterprise Data application, the most commonly used is the model of supervised

similarityAccording to the function and form similarity of the algorithm, we can classify the algorithm, for example, tree-based algorithm, neural network based algorithm and so on. Of course, the scope of machine learning is very large, and some algorithms are difficult to classify into a certain category. For some classifications, the same classification algor

Course Description:This lesson focuses on the things you should be aware of in machine learning, including: Occam's Razor, sampling Bias, and Data snooping.Syllabus: 1, Occam ' s razor.2, sampling bias.3, Data snooping.1, Occam ' s Razor.Einstein once said a word: An explanation of the data should is made as simple as possible, but no simpler.There are similar s

The idea behind integrated learning is to combine different classifiers to get a meta-classifier, which has better generalization performance than a single classifier. For example, let's say we've got a forecast for an event from 10 experts, and integrated learning can combine these 10 predictions to get a more accurate forecast.We will learn later that there are different ways to create an integration mode

Machine learning Notes (i)Today formally began the study of machine learning, in order to motivate themselves to learn, but also to share ideas, decided to send their own experience of learning to the Internet to let everyone share.Bayesian learningLet's start with an exampl

SVM is a widely used classifier, the full name of support vector machines , that is, SVM, in the absence of learning, my understanding of this classifier Chinese character is support/vector machines, after learning, Only to know that the original name is the support vector/machine, I understand this classifier is: by the sparse nature of a series of support vecto

-party library, without organic integration, the corresponding learning costs will be higher. Python is faster than R. Python can directly deal with the data on the G, R No, r analysis data need to first through the database to transform big data into small data (through GroupBy) to the R for analysis, so R can not directly analyze the behavior of the list, can only analyze statistical results. Python's advantage lies in its glue language characterist

Course Address: Https://class.coursera.org/ntumltwo-002/lectureImportant! Important! Important!1. Shallow-layer neural networks and deep learning2. The significance of deep learning, reduce the burden of each layer of network, simplifying complex features. Very effective for complex raw feature learning tasks, such as machine