udemy machine learning course review

Logistic regression is used to classify, and linear regression is used to return.Linear regression is the addition of the properties of the sample to the front plus the coefficients. The cost function is the sum of squared errors. Therefore, in the minimization of the cost function, you can directly derivative, so that the derivative equals 0, as follows:Gradient descent can also be used to learn the same gradient as the logistic regression form.Advantages of linear regression: simple calculatio

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

Netease Open Course (Class 12 and 13)Notes, 7A, 7B, 8 This chapter introduces unsupervised AlgorithmsFor unsupervised users, K means is the most typical and simple. You need to read the 7A handouts directly. Mixtures of gaussians To understand mixtures of gaussians, go back and review gaussians Discriminant Analysis and Gaussian discriminant analysis. First, Gaussian discriminant analysis generates al

Naive Bayesian algorithm is to look for a great posteriori hypothesis (MAP), which is the maximum posteriori probability of the candidate hypothesis.As follows:In Naive Bayes classifiers, it is assumed that the sample features are independent from one another:Calculate the posterior probability of each hypothesis and choose the maximum probability, and the corresponding category is the result of the sample classification.Advantages and DisadvantagesVery good for small-scale data, suitable for mu

Netfei is a DVD leasing company. by increasing its sales by 10%, it can earn 1 million RMB in revenue, which is very impressive. How to: predict consumers' ratings for movies? (Increase the predicted value by 10 percentage points through their own systems) if the recommendations you provide to consumers are very accurate, the consumers will be very satisfied. The essence of machine learning: 1. An existin

23:55:01 | category: foreign university courses | Tag: machine learning | font size subscription INSTRUCTOR: Andrew Ng Http://see.stanford.edu/see/courseinfo.aspx? Coll = 348ca38a-3a6d-4052-937d-cb017338d7b1 Http://www.stanford.edu/class/cs229/materials.html Lecture Notes 1 (PS) (PDF) Supervised Learning, discriminative algorithms Lecture

slowly; conversely, if it is too large, the algorithm may miss the minimum value, or even not converge. Another thing to note is that, above $\theta_0, \theta_1$ 's update formula uses all the data in the dataset (called "Batch" Gradient descent), which means that for every update, we have to scan the entire data set, Causes the update to be too slow.Review of linear algebra Matrix and Vector definitions Matrix addition and multiplication Matrix-Vector Product Matrix-matrix

=[] For C in Cs: # Select Model CLS = Logisticregression (c=c) # submit data to Model training Cls.fit (X_train, Y_train) Scores.append (Cls.score (X_test, Y_test)) # # Drawing Fig=plt.figure () Ax=fig.add_subplot (1,1,1) ax.pl OT (cs,scores) ax.set_xlabel (r "C") Ax.set_ylabel (r "Score") Ax.set_xscale (' Log ') Ax.set_title ("Logisticregression") plt.show () If __name__== ' __main__ ': X_train,x_test,y_train,y_test=load_data () # Generates a dataset for regression problems Test_logist

(\theta) =-\frac{1}{m} \sum\limits_{i=1}^{m}\left[y^{(i)}\log (H_\theta (x^{(i)}) + (1-y^{(i)}) \log (1-h_\ Theta (x^{(i)})) \right] + \frac{\lambda}{2m}\sum\limits_{j=1}^{n}\theta_j^{2}$$Gradient Descent parameter update:$$\theta_0 = \theta_0-\alpha\frac{1}{m}\sum\limits_{i=1}^{m} (H_\theta (x^{(i)})-y^{(i)}) x_0^{(i)}; j = 0$$$$\theta_j = \theta_j-\alpha \left[\frac{1}{m}\sum\limits_{i=1}^{m} (H_\theta (x^{(i)})-y^{(i)}) x_j^{(i)} + \frac{\ Lambda}{m}\theta_j \right]; J > 1$$Reference documen

Public Course address:Https://class.coursera.org/ml-003/class/index INSTRUCTOR:Andrew Ng 1. Model Representation ( Model Creation ) Consider a question: what if we want to predict the price of a house in a given area based on the house price and area data? In fact, this is a linear regression problem. The given data is used as a training sample to train it to get a model that represents the relationship between price and area (actually a functi

Netease Open Course: 14th coursesNotes, 10 In the factor analysis mentioned earlier, the EM algorithm is used to find potential factor variables for dimensionality reduction. This article introduces another dimension reduction method, principal components analysis (PCA), which is more direct than factor analysis and easier to calculate. Principal component analysis is based on, In reality, for high-dimensional data, many dimensions are Disturb