machine learning course on coursera

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Machine learning fundamentals and concepts for the foundation course of machine learning in Tai-Tai

some time ago on the Internet to see the Coursera Open Classroom Big Machine learning Cornerstone Course, more comprehensive and clear machine learning needs of the basic knowledge, theoretical basis to explain. There are several

Tai Lin Xuan Tian Machine learning course note----machine learning and PLA algorithm

vectors or the longer the length of the vector, the following to deal with the length of the vector.Using the nature of the PLA's "Fault only Update", in the case of making mistakes, through the above deduction, the final conclusion is that the square of WT length increases the square of xn longest length after each update.Using the conclusion of the first proof, the derivation process is as follows:The above is known as three conditions, there are two points to be explained:1) Because the valu

"Machine Learning Basics" machine learning Cornerstone Course Learning Introduction

wide range of user personalization services, such as marketing strategies for consumers The key to machine learning (pattern) There is a potential pattern or rule that can be learned (Definition) is not easy to implement programmatically (data) has information about a pattern The actual definition of machine learningM

Andrew Ng's Machine Learning course learning (WEEK5) Neural Network Learning

This semester has been to follow up on the Coursera Machina learning public class, the teacher Andrew Ng is one of the founders of Coursera, machine learning aspects of Daniel. This course is a choice for those who want to underst

Machine Learning 001 Deeplearning.ai Depth Learning course neural Networks and deep learning first week summary

Deep Learning SpecializationWunda recently launched a series of courses on deep learning in Coursera with Deeplearning.ai, which is more practical compared to the previous machine learning course. The operating language also has M

Stanford Machine Learning Open Course Notes (14th)-large-scale machine learning

Public Course address:Https://class.coursera.org/ml-003/class/index INSTRUCTOR:Andrew Ng 1. Learning with large datasets ( Big Data Learning ) The importance of data volume has been mentioned in the previous lecture on machine learning design. Remember this sentence:

Stanford Machine Learning Open Course Notes (8)-Machine Learning System Design

findF1scoreThe algorithm with the largest value. 5. Data for Machine Learning ( Machine Learning data ) In machine learning, many methods can be used to predict the problem. Generally, when the data size increases, the accura

Machine Learning Public Course notes (10): Large-scale machine learning

increase or reduce the number of example (change 100 to 1000 or 10, etc.), reduce or increase the learning rate.elearning (Online learning)The previous algorithm has a fixed training set to train the model, when the model is well trained to classify and return the future example. Online learning is different, it updates the model parameters for each new example,

Stanford Machine Learning Open Course Notes (7)-some suggestions on machine learning applications

Public Course address:Https://class.coursera.org/ml-003/class/index INSTRUCTOR:Andrew Ng 1. deciding what to try next ( Determine what to do next ) I have already introduced some machine learning methods. It is obviously not enough to know the specific process of these methods. The key is to learn how to use them. The so-called best way to master knowledge

Andrew Ng's Machine Learning course Learning (WEEK4) Multi-Class classification and neural Networks

This semester has been to follow up on the Coursera Machina learning public class, the teacher Andrew Ng is one of the founders of Coursera, machine learning aspects of Daniel. This course is a choice for those who want to underst

Stanford Machine Learning Course Note (1) Supervised learning and unsupervised learning

is that only the input paradigm is provided for this network, and it automatically identifies its potential class rules from those examples. When the study is complete and tested, it can also be applied to new cases. A typical example of unsupervised learning is clustering. The purpose of clustering is to bring together things that are similar, and we do not care what this class is. Therefore, a clustering algorithm usually needs to know how to c

Stanford CS229 Machine Learning course Note III: Perceptual machine, Softmax regression

before, but you need to define T (Y) here:In addition, make:(t (y)) I represents the first element of the vector T (y), such as: (t (1)) 1=1 (T (1)) 2=01{.} is an indicator function, 1{true} = 1, 1{false} = 0(T (y)) i = 1{y = i}Thus, we can introduce the multivariate distribution of the exponential distribution family form:1.2 The goal is to predict the expectation of T (y), because T (y) is a vector, so the resulting output will also be a desired vector, where each element is:Corresponds to th

Stanford Machine Learning Open Course Notes (15th)-[application] photo OCR technology

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

Machine-learning Course Learning Summary (1-4)

First, Introduction1. Concept : The field of study that gives computers the ability to learn without being explicitly programmed. --an older, informal definition by Arthur Samuel (for tasks that cannot be programmed directly to enable the machine to learn) "A computer program was said to learn from experience E with respect to some class of tasks T and performance measure P, if Its performance on tasks in T, as measured by P, improves wit

Stanford CS229 Machine Learning course Note six: Learning theory, model selection and regularization

be trained and predicted immediately, which is called Online learning. each of the previously learned models can do online learning, but given the real-time nature, not every model can be updated in a short time and the next prediction, and the perceptron algorithm is well suited to do online learning:The parameter Update method is: if hθ (x) = y is accurate, the parameter is not updated otherwise, θ:=θ+ y

Machine Learning Public Course notes (8): K-means Clustering and PCA dimensionality reduction

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

Coursera Special Course--Introduction to program design and algorithm

1. What is a special course (specializations)?If you want to learn a major that you do not understand, you can study according to the special course arrangement. Coursera Special Course collects a field of curriculum, and according to the Order of teaching, it is very suitable for the new people who don't feel well.2.

What courses are worth learning about Python and data analysis on coursera?

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

After-school reading Supplement to the software Security course on Coursera

Took a course on software security at Coursera. Here is a list of readings from the professor:Week 1ReadingsRequired ReadingThe only required reading this week is the following: Common Vulnerabilities Guide for C programmers. Take note of the unsafe C library functions listed here, and how they is the source of the buffer overflow vulnerabilities. This list is relevant for the project and this week

Machine Learning Public Course notes (1)

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

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