best machine learning coursera

a patient's tumour is malignant, depending on the size of the patient's tumour:Of course, sometimes we use more than one variable, such as the age of the patient, the size and shape of the tumour, and so on.In the picture, the circle represents benign and the fork is malignant, and the problem we want to learn becomes the division of benign tumors and malignant tumors.This problem is also called classification problem, the classification of the use of discrete values. We want to use this algori

This is what we have learned (except decision tree)Here is a typical decision tree algorithm, with four places to choose from:Then introduced a cart algorithm: By decision Stump divided into two categories, the criterion for measuring subtree is that the data are divided into two categories, the purity of these two types of data (purifying).The following is a measure of purity:Finally, when to stop:Decision tree may be overfitting, reducing the number of Ein and leaves (indicating the complexity

cost function least.The algorithm is:After derivation, get:Note: Although the resulting gradient descent algorithm appears to be the same as the gradient descent algorithm for linear regression, the hypothetical function here differs from the linear regression, so it is actually different. In addition, it is still necessary to perform feature scaling before applying the gradient descent algorithm.In addition, there are some alternatives to the gradient descent algorithm:In addition to the gradi

Welcome and Introductionoverviewreadinglog 9/9 videos and quiz completed; 10/29 Review; Note1.1 Welcome 1) What are machine learning? Machine learning are the science of getting compters to learn, without being explicitly programmed. 1.2 Introduction Linear reg

general, multiplication does not satisfy the exchange law: $ \ Matrix {A} \ times \ matrix {B} \ not = \ matrix {B} \ times \ matrix {A} $Special Matrix $ \ Matrix {I }=\ matrix {I _ {n \ times N }}=\ begin {bmatrix} 1 0 \ cdots 0 0 \ Cr0 1 \ cdots 0 0 \ Cr \ vdots \ vdots \ Cr0 0 \ cdots 1 0 \ Cr0 0 \ cdots 0 1 \ Cr \ end {bmatrix} $ For any matrix $ \ matrix {A} $: $ \ Matrix {A} \ times \ matrix {I }=\ matrix {I} \ times \ matrix {A }=\ matrix {A} $Inverse Matrix and inverte

In this section, a linear model is introduced, and several linear models are compared, and the linear regression and the logistic regression are used for classification by the conversion error function.More important is this diagram, which explains why you can use linear regression or a logistic regression to replace linear classificationThen the stochastic gradient descent method is introduced, which is an improvement to the gradient descent method, which greatly improves the efficiency.Finally

This section is about the nuclear svm,andrew Ng's handout, which is also well-spoken.The first is kernel trick, which uses nuclear techniques to simplify the calculation of low-dimensional features by mapping high-dimensional features. The handout also speaks of the determination of the kernel function, that is, what function K can use kernel trick.In addition, the kernel function can measure the similarity of two features, the greater the value, the more similar.Next is the polynomial Kernel, w

-Polynomial regressionSince linear regression does not apply to all data, sometimes we need to use curves to fit our data, for example, with two-times polynomial:Or three-time polynomial:Usually we need to look at the data before deciding what model to try to fit.After that, we can make:The two-time polynomial is then converted to a linear regression model.It is worth noting that if we adopt a polynomial regression model, feature scaling is necessary before the gradient descent algorithm is run.

the transpose of the Matrix.-Gradient descent for multiple variablesSimilar to univariate/feature linear regression, in multivariable/feature linear regression, we will also define a cost function, namely:Our goal is the same as the problem in univariate/characteristic linear regression, which is to find out the combination of parameters that make the cost function least.Therefore, the multivariable/linear regression gradient descent algorithm is:ThatAfter the derivative number can be obtained:

 II. Linear Regression with one Variable (Week 1)-Model representationIn the case of previous predictions of house prices, let's say that our training set of regression questions (Training set) looks like this:We use the following notation to describe the amount of regression problems:-M represents the number of instances in the training set-X represents the feature/input variable-Y represents the target variable/output variable-(x, y) represents an instance of a training set-Representing the

Programmers who have turned to AI have followed this number ☝☝☝ Author: Lisa Song Microsoft Headquarters Cloud Intelligence Advanced data scientist, now lives in Seattle. With years of experience in machine learning and deep learning, we are familiar with the requirements analysis, architecture design, algorithmic development and integrated deployment of

! I've been on this course 3 years ago, and it's been a long time ... Before going to bed to see this question, the day before yesterday wrote an article about learning Python in Coursera, just right question, so excerpt part, hope to be helpful:-) Let's talk about the process of learning Python in Coursera (and reco

friends leave a message saying they are already charged. Let's go to the official website and check it out! I have taken this course three years ago. It takes a long time ...... I saw this problem before I went to bed. I wrote an article about learning python in coursera the day before yesterday, which is just the right question. So I want to extract some of it and hope it will help me :-) Next, let's ta

Multivariate regressionReview simple linear regression: A feature, two correlation coefficients　　The actual application is much more complicated than this, such as1, house prices and housing area is not just a simple linear relationship.2, there are many factors affecting the price, not only the size of the house, but also many other factors.　　　　Now, in the first case, the price and the housing area are not simply linear, and may be two or polynomial:Two times function:　　Polynomial functions:　　P

(w ')Description W over fitting3 Sources of errorNoise, Bias, Variance1. Noise NoiseOf an inherent, irreducible, or reduced nature.　　　2, Bias Deviation　　　　　　The simpler the model, the greater the deviation　　The more complex the model, the smaller the deviation3. Variance Variance　　　　Simple model, small variance　　Complex model, large variance　　Deviations and variance tradeoffs, deviations and variances cannot be calculated　　　　Training error and the amount of test data, fixed model complexity, a

Operating system Learning notes----process/threading Model----Coursera Course note process/threading model 0. Overview 0.1 Process ModelMulti-Channel program designConcept of process, Process control blockProcess status and transitions, process queuesProcess Control----process creation, revocation, blocking, wake-up 、...0.2 threading ModelWhy threading is introducedThe composition of the threadImplementatio

full implementation of multi-layered neural network recognition picture of the cat Original Coursera Course homepage, in the NetEase cloud classroom also has the curriculum resources but no programming practice. This program uses the functions completed in the last job, fully implementing a multilayer neural network, and training to identify whether there is a cat in the picture. There is no comment in the Code and Training test data download Cod

Someting about Lists mutation1 ###################################2 #Mutation vs. Assignment3 4 5 ################6 #Look alike, but different7 8A = [4, 5, 6]9b = [4, 5, 6]Ten Print "Original A and B:", A, b One Print "is they same thing?"+ F isb A -A[1] = 20 - Print "New A and B:", A, b the Print - - ################ - #aliased + -c = [4, 5, 6] +D =C A Print "Original C and D:", C, D at Print "is they same thing?"+ D isD - -C[1] = 20 - Print "New C and D:", C, D - Print - in ##############

points of mini project are translated, Then translate the Mini project implementation steps, not a one-time full translation, take too long, the previous translation may forget, and the translation may not be accurate, and sometimes to see the original text. Complete a paragraph and translate the next paragraph, step by step. Do not translate all, some do not help to complete the task can not translate, save time. 4. Selective translation of code clinic,5. If you get stuck, search for keywords

[Introduction to machine learning] Li Hongyi Machine Learning notes-9 ("Hello World" of deep learning; exploring deep learning) PDF Video Keras Example application-handwriting Digit recognition Step 1