edureka machine learning course

On Github, Afshinea contributed a memo to the classic Stanford CS229 Course, which included supervised learning, unsupervised learning, and knowledge of probability and statistics, linear algebra, and calculus for further studies. Project Address: https://github.com/afshinea/stanford-cs-229-machine-learningAccordi

in the first section, the meta-algorithm briefly describesIn the case of rare cases, the hospital organizes a group of experts to conduct clinical consultations to analyze the case to determine the outcome. As with the panel's clinical consultations, it is often better to summarize a large number of individual opinions than a person's decision. Machine learning also absorbed the ' Three Stooges top Zhuge Li

randomly groups the data to the extent that training intensive accounts for 70% of the original data (this ratio can vary depending on the situation), and the test error is used as the criterion when selecting the model. The question comes from the Stanford University Machine Learning course on Coursera, which is described as follows: the size and price of the

this regard, the first Scikit-learn python system, ease of use not to say, the document is also moving to No. Alternative to spark in the Mllib and Mahout, the pros and cons do not repeat, a check will be clear. If it is a statistically cheese, using R or MATLAB is of course also very good.4. MathematicalStatistics/probability/optimization (convex optimization), in which all the knowledge deep like the sea, and really short. But the human energy is l

thorough search. Many greedy algorithms are like this, as will be mentioned later. Decision Tree Algorithm. The previous inductive bias is called Limited offsetThe latter is called Preferred offset. When studying other inductive inference methods, it is necessary to keep in mind the existence and strength of such inductive bias. If an algorithm is more biased, the more inductive it can be, and more instances are not found. Of course, the correctness

prediction Naturual Language Processing Coursera Course Book on NLP NLTK NLP W/python Foundations of statistical Language processing Probability Statistics Thinking Stats-book + Python Code From algorithms to Z-scores-book The Art of R Programming-book (not finished) All of Statistics Introduction to statistical thought Basic probability theory Introduction to probability Principle of u

training, but as a punishment or reward for the environment. Typical problems are system and robot control. Example of an algorithm packageQ-Learning and sequential differential learning (temporal difference learning).Algorithmic similarityAccording to the function and form similarity of the algorithm, we can classify the algorithm, for example, tree-based algor

-means division, until the user-specified number of clusters. There are two ways to continue dividing clusters according to the SSE selection:(1) Choosing which cluster to divide depends on whether the value of SSE can be minimized by its partitioning. This requires dividing each cluster into two divisions, then calculating the sum of the cluster SSE after the cluster and calculating the difference between its and the binary SSE (of course SSE must fa

This blog is reproduced from a blog post, introduced Gan (generative adversarial Networks) that is the principle of generative warfare network and Gan's advantages and disadvantages of analysis and the development of GAN Network research. Here is the content. 1. Build Model 1.1 Overview Machine learning methods can be divided into generation methods (generative approach) and discriminant methods (discrimin

recurrent neural Network (RNN). It memorizes any commonalities on the network and serves like a memory later. Formally, the argument states that;Let us assume, the persistence or repetition of a reverberatory activity (or "trace") tends to induce lasting cellular Changes that add-to-its stability .... When an axon of cell a was near enough to excite a cell B and repeatedly or persistently takes part I n firing it, some growth process or metabolic change takes place in one or both cells such tha

Learning notes for "Machine Learning Practice": Draw a tree chart use a decision tree to predict the contact lens type, The decision tree is implemented in the previous section, but it is only implemented using a nested dictionary containing tree structure information. Its representation is difficult to understand. Obviously, it is necessary to draw an intuitiv

data in fr.readlines ()] Lenseslabel = [ ' age ' , ' prescript ' , ' astigmatic ' , ' tearrate ' ]lensestree = Tree.buildtree ( Lensesdata, Lenseslabel) #print lensesdata print lensestreeprint plottree.createplot (lensestree) It can be seen that the early implementation of the decision tree construction and drawing, using different data sets can be very intuitive results, you can see, along the different branches of the decision tree, you can get different patients need to wear the ty

Reprinted article: Norm Rule in machine learning (i) L0, L1 and L2 norm[Email protected]Http://blog.csdn.net/zouxy09Today we talk about the very frequent problems in machine learning: overfitting and regulation. Let's begin by simply understanding the L0, L1, L2, and kernel norm rules that are commonly used. Finally, w

Machine Learning Algorithms and Python practices (7) Logistic Regression) Zouxy09@qq.com Http://blog.csdn.net/zouxy09 This series of machine learning algorithms and Python practices mainly refer to "machine learning practices. B