machine learning apis by example

can get the y I want, if not so strictly, all this method of data analysis can be counted as machine learning category. So the basic elements that a machine learning should normally include are: training data, model with parameters, loss function, training algorithm training The data function is needless to say; the m

, through experience e, to improve the performance of the task T performed p. (Tom mitchell,1998) Machine learning can be divided into four main parts: Supervised learningProvides a set of standard answers to the algorithm, to supervise the algorithm for the specific input output, is not the answer we give.The problem of regression and classification can be attributed to supervised

prediction example of the house price, suppose we have implemented a regular linear regression method to predict the price:However, when you find that this prediction is applied to a new training data with great error (Error), some solutions should be taken:Get more training Examplestry smaller sets of featurestry getting additional featurestry adding polynomial features (e.g. X1^2, x2^2, x1x2 ...) Try Decreasingλtry increasingλDiagnosis of

continuously updating theta. Map Reduce and Data Parallelism: Many learning algorithms can be expressed as computing sums of functions over the training set. We can divide up batch gradient descent and dispatch the cost function for a subset of the data to many different machines So, we can train our algorithm in parallel. Week 11:Photo OCR: Pipeline: Text detection Character segmentation Ch

7 machine learning System Design Content 7 Machine Learning System Design 7.1 Prioritizing 7.2 Error Analysis 7.3 Error Metrics for skewed classed 7.3.1 Precision/recall 7.3.2 Trading off precision and RECALL:F1 score 7.4 Data for machine

FrameSimilar to the Spark Dataframe, but the engine is unknowable (for example, in the future it will run on the engine rather than the spark). This includes the interface between Cross-validation and the external machine learning Library.Interface to other machine learning

Original: http://blog.csdn.net/abcjennifer/article/details/7834256This column (machine learning) includes linear regression with single parameters, linear regression with multiple parameters, Octave Tutorial, Logistic Regression, regularization, neural network, design of the computer learning system, SVM (Support vector machines), clustering, dimensionality reduc

Simple examples are used to understand what machine learning is, and examples are used to understand machine learning. 1. What is machine learning? What is machine

model and new input to make predictions. Arthur Samuel (1959): a discipline that enables computers to learn independently without being instructed by external programs Langley (1996): "machine learning is an artificial intelligence science. The main research object in this field is artificial intelligence, especially how to improve the performance of specific algorithms in Experience

ProfileThis article is the first of a small experiment in machine learning using the Python programming language. The main contents are as follows: Read data and clean data Explore the characteristics of the input data Analyze how data is presented for learning algorithms Choosing the right model and

Drawing a learning curve is useful, for example, if you want to check your learning algorithm and run normally. Or you want to improve the performance or effect of the algorithm. Then the learning curve is a good tool. The learning curve can judge a

ability of machine learning. Because machine learning is hypothesis to be processed on the out of sample, not on the in sample. So, a means to evaluate whether machine learning is in place is from validation. The general practice

Application Recommendations for machine learningFor a long time, the machine learning notes have not been updated, the last part of the updated neural network. This time we'll talk about the application of machine learning recommendations.Decide what to do nextSuppose we nee

I. BACKGROUND In machine learning, there are 2 great ideas for supervised learning (supervised learning) and unsupervised learning (unsupervised learning) Supervised learning, in layman

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: It is not who has the best algorithm that w

Use Python to master machine learning in four steps and python to master machines in four steps To understand and apply machine learning technology, you need to learn Python or R. Both are programming languages similar to C, Java, and PHP. However, since Python and R are both relatively young and "Far Away" from the CP

Machine Learning Summary (1), machine learning SummaryIntelligence:The word "intelligence" can be defined in many ways. Here we define it as being able to make the right decision based on certain situations. Knowledge is required to make a good decision, and this knowledge must be operable, for