machine learning certification coursera

Hello everyone, I am mac Jiang, today and everyone to share Coursera-stanford university-machine Learning-week 10:large scale machine learning after the class exercise solution. Although my answer passed the system test, but my analysis is not necessarily correct, if you bo

values of each eigenvalue have the same scale range, so that the influence of each eigenvalue is the same.How do I set the value of λ? By selecting a different λ to repeat the test process, a λ that minimizes the prediction error is obtained. The best value can be obtained by cross-validation-the sum of squared errors is minimized on the test data.Ridge regression was first used to deal with more than a sample number of features, and is now used to add human bias to the estimate, thus obtaining

training set for training and get different model; 4, the model on the CV set on the performance of a score, choose a better performance models; There is a need to note that we will eventually choose to perform the best model on the CV set, but the final evaluation of this model is to be in a new data d_test (similar to the Netflix Prize competition, The official eventually gives your model a rating of data) on the test. Andrew NG recommends dividing the data as follows: k-fold Cross validtio

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

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

Machine learning Notes (iii) multivariable linear regression Note: This content resource is from Andrew Ng's machine learning course on Coursera, which pays tribute to Andrew Ng. One, multiple characteristics (multiple Features)The housing price problem discus

Recently is a period of idle, do not want to waste, remember before there is a collection of machine learning link Andrew ng NetEase public class, of which the overfiting part of the group will report involved, these days have time to decide to learn this course, at least a superficial understanding.Originally wanted to go online to check machine

calculates the accuracy of the entire system at this time: As shown in, text recognition consists of four parts. Now we can find the system accuracy after optimization for each part. The question is, how can we improve the accuracy of the entire system? We can see from the table that, if we have optimized the text moderation part, the accuracy will be72%Add89%If we optimize the character segmentation, the accuracy is only from89%To90%If character recognition is optimized90%To100%In contr

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

Machine learning notes (b) univariate linear regression Note: This content resource is from Andrew Ng's machine learning course on Coursera, which pays tribute to Andrew Ng. Model representationHow to solve the problem of house price in note (a), this will be

This content resource comes from Andrew Ng's Machine Learning course on Coursera, where he pays tribute to Andrew Ng. The "Logic regression" study notes for the sixth course of machine learning at Stanford University, this course consists of 7 main parts:1) Classification (c

Find a good article on the internet, paste it directly, add some supplements and your own understanding, and count as this article. My education in the fundamentals of machine learning has mainly come from Andrew Ng's excellent Coursera course on the topic. one thing that wasn't covered in that course, though, was the topic of "Boosting" which I 've come into SS

Hello everyone, I am mac Jiang, today and everyone to share the coursera-ntu-machine learning Cornerstone (Machines learning Foundations)-Job 2 q16-18 C + + implementation. Although there are many great gods in many blogs have given the implementation of Phython, but given the C + + implementation of the article is sig

reduced after removing the label, (2) using the data of the reduced dimension to train the model, (3) for the new data points, the PCA reduced dimension to obtain the dimensionality reduction data, and the model to obtain the predicted value. Note : You should only use the training set data for PCA dimensionality reduction get Map $x^{(i)}\rightarrow z^{(i)}$, and then apply the mapping (PCA-selected principal matrix $u_reduce$) to the validation set and test set do not use PCA to block ove

After being confused by Hot Spot's messy and changing parameters, I decided to change things for fun. Then we found the machine learning video on Coursera. Reading a few paragraphs is quite simple, so I recorded them in itouch and checked them out from time to time. The day before yesterday, I finally finished eating it. The content is really easy to understand.

I browsed some of the machine learning blogs of Daniel and summarized the typical contents as follows: 1. Book Reading Notes 2. Paper Reading Notes and classification survey summary 3. Technical Note and tutorial Reading Notes 4. Summary of typical and difficult problems 5. Study Plan and study records (updated daily) 6. Monthly summary and semester Summary 7. Co