andrew ng machine learning course

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Stanford ng Machine Learning Lecture Notes-Referral system (Recommender systems)

Recommended systems (Recommender system) problem formulation:Recommendersystems: Why it has two reasons: first it is a very important machine learning application direction, in many companies occupy an important role, such as Amazon and other sites are very good to establish a recommendation system to promote the sale of goods. Secondly, the system has some big idea in

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,

NG Machine Learning Video notes (11) Theory of--k-mean value algorithm

NG Machine Learning Video notes (11)--k - means algorithm theory(Reproduced please attach this article link--linhxx)I. OverviewK-Means (K-means) algorithm, is a unsupervised learning (unsupervised learning) algorithm, its core is clustering (clustering), that is, a set of in

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 lea

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-Overview of common matlab programming commands (NG-ml-class octave/MATLAB tutorial)

Machine Learning-Overview of common matlab programming commands -- Summary from ng-ml-class octave/MATLAB tutorial CourseraA. basic operations and moving data around1 in command line mode, you can use Shift + press enter to append the next line to output 2 length command to apply to the matrix, and return a higher one-dimensional dimension3 help + command is the

Coursera open course notes: "Advice for applying machine learning", 10 class of machine learning at Stanford University )"

Stanford University machine Learning lesson 10 "Neural Networks: Learning" study notes. This course consists of seven parts: 1) Deciding what to try next (decide what to do next) 2) Evaluating a hypothesis (Evaluation hypothesis) 3) Model selection and training/validation/test sets (Model selection and training/verific

NG Machine Learning Video Notes (ii)--gradient descent algorithm interpretation and solving θ

NG Machine Learning Video notes (ii)--Gradient descent algorithm interpretation and solving θ (Reproduced please attach this article link--linhxx) First, the interpretation gradient algorithmA gradient algorithm formula and a simplified cost function diagram, as shown in.1) Partial derivativeBy the know, at point A, its partial derivative is less than 0, so θ min

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

To draw a full stop to the first four sessions of the course, here are two of the models that were mentioned in the first four lectures by Andrew the Great God.The Perceptron Learning Algorithm Sensing machineModel:From the model, the Perceptron is very similar to the logistic regression, except that the G function of logistic regression is a logical function (al

Machine Learning Professional Advanced Course _ Machine learning

At present, the application of machine learning business is more in communication and finance. Large data, machine learning these concepts have been popularized in recent years, but many researchers have worked in this field more than 10 years earlier. Now finally ushered in their own tuyere. I will use the professiona

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 the

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 more important concepts and ideas in foundati

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

A probe into machine learning1. What is machine learningLearning refers to the skill that a person refines in the course of observing things, rather than learning, machine learning refers to the ability of a computer to gain some

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

The last three weeks of Andrew Ng's machine learning were recently followed by the linear regression (Linear Regression) and logistic regression (logistic Regression) models in machines learning. Make a note here.Also recommended a statistical study of the book, "Statistical Learni

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 MATLAB changed to Python to be more fit to the

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

Coursera Course "Machine learning" study notes (WEEK1)

This is a machine learning course that coursera on fire, and the instructor is Andrew Ng. In the process of looking at the neural network, I did find that I had a problem with a weak foundation and some basic concepts, so I wanted to take this

coursera-Wunda-Machine learning-(programming exercise 7) K mean and PCA (corresponds to the 8th week course)

This series is a personal learning note for Andrew Ng Machine Learning course for Coursera website (for reference only)Course URL: https://www.coursera.org/learn/

Stanford CS229 Machine Learning course NOTE I: Linear regression and gradient descent algorithm

afternoon, just finish the first four lessons, listen to Andrew Ng to finish the related content of GLM generalized linear model. It's really a feeling brief encounter. I would like to recommend this course to all the students who see this article (although it's 07).Three elements of machine learningThe three elements

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

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