Machine Learning Algorithm and How to Applications

Machine Learning teaches computers to do what comes naturally to humans: learn from experience. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model.

 

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The algorithms adaptively improve their performance as the number of samples available for learning increases.

Machine learning uses two types of techniques: Supervised Learning, which trains a model on known input and output data so that it can predict future outputs, and Unsupervised Learning, which finds hidden patterns or intrinsic structures in input data. 

The aim of supervised Machine Learning is to build a model that makes predictions based on evidence in the presence of uncertainty. A supervised learning algorithm takes a known set of input data and known responses to the data (output) and trains a model to generate reasonable predictions for the response to new data. 

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Supervised Learning uses classification and regression techniques to develop predictive models. Classification techniques predict categorical responses, for example, whether an email is genuine or spam, or whether a tumor is cancerous or begin.

Classification models classify input data into categories. Typical applications include medical imaging, image and speech recognition, and credit scoring. Regression techniques predict continuous responses, for example, changes in temperature or fluctuations in power demand. Typical applications include electricity load forecasting and algorithmic trading.

 

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Unsupervised Learning finds hidden patterns or intrinsic structures in data. It is used to draw inferences from datasets consisting of input data without labeled responses.

Clustering is the most common unsupervised learning technique. It is used for exploratory data analysis to find hidden patterns or groupings in data. Applications for clustering include gene sequence analysis, market research, and object recognition.

SELECTING THE RIGHT ALGORITHM

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Choosing the right algorithm can seem overwhelming—there are dozens of supervised and unsupervised machine learning algorithms, and each takes a different approach to learning. There is no best method or one size fits all.

Finding the right algorithm is partly based on trial and error even highly experienced data scientists cannot tell whether an algorithm will work without trying it out. Highly flexible models tend to overfit data by modeling minor variations that could be noise. Simple models are easier to interpret but might have lower accuracy.

 Therefore, choosing the right algorithm requires trading off one benefit against another, including model speed, accuracy, and complexity. Trial and error is at the core of machine learning—if one approach or algorithm does not work, you try another.

MATLAB provides tools to help you try out a variety of machine learning models and choose the best. To find MATLAB apps and functions to help you solve machine learning tasks, consult the following table. Some machine learning tasks are made easier by using apps, and others use command-line features.

MACHINE LEARNING APPLICATIONS THAT YOU MAY BE FAMILIAR

The iterative aspect of machine learning is important because as models are exposed to new data, they are able to independently adapt. They learn from previous computations to produce reliable, repeatable decisions and results. 

It’s a science that’s not new – but one that’s gaining fresh momentum. Because of new computing technologies, machine learning today is not like machine learning of the past. While many machine learning algorithms have been around for a long time, the ability to automatically apply complex mathematical calculations to big data – over and over, faster and faster – is a recent development.

Here are a few widely publicized examples of Machine Learning applications that you may be familiar with:

1. The heavily hyped, self-driving Google car? The essence of machine learning.

2. Online recommendation offers like those from Amazon and Netflix? Machine learning applications for everyday life.

3. Knowing what customers are saying about you on Twitter? Machine learning combined with linguistic rule creation.

4. Fraud detection? One of the more obvious, important uses in our world today.

WHY THE INCREASED INTEREST IN MACHINE LEARNING ?

Resurging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever.

Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage. All of these things mean it's possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results – even on a very large scale.

The result? High-value predictions that can guide better decisions and smart actions in real time without human intervention. One key to producing smart actions in real time is automated model building. Analytics thought leader Thomas H. Davenport wrote in The Wall Street Journal that with rapidly changing, growing volumes of data, "... you need fast-moving modeling streams to keep up." And you can do that with machine learning. He says, "Humans can typically create one or two good models a week; machine learning can create thousands of models a week."

More about machine learning, Read Machine Learning with SAS Enterprise Miner to find out how machine learning techniques were used to analyze the practical problem of customer churn. Learn how Viseca Card Services used machine learning to create a new customer loyalty program in this white paper: Statistics and Machine Learning at Scale.

MACHINE LEARNING INCLUDES

The Wolfram Language includes a wide range of state-of-the-art integrated machine learning capabilities, from highly automated functions like Predict and Classify to functions based on specific methods and diagnostics, including the latest neural net approaches.

The functions work on many types of data, including numerical, categorical, time series, textual, image and audio.

 

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1. Learning from Input and Output

Classify — classify data into categories using a built-in classifier or learning from examples

Predict — predict values from data using a built-in predictor or learning from examples

ClassifierFunction — symbolic representation of a classifier to be applied to data

PredictorFunction — symbolic representation of a predictor to be applied to data

ClassifierMeasurements, PredictorMeasurements — performance on test data

 

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2. Learning from Sequences

SequencePredict — predict subsequent elements from sequence examples

SequencePredictorFunction — symbolic representation of a sequence predictor

DimensionReduction — find how to project data onto lower-dimensional space

FeatureExtraction — find how to extract features from data

ClusterClassify — classify data into clusters

FindDistribution — find a simple symbolic distribution from data

FeatureSpacePlot — visualization of dimension-reduced feature space

Dendrogram — visualization of hierarchical clusters

BayesianMinimization — model-based minimization of arbitrary objective functions

ActiveClassification — learn a classifier by actively probing a system

ActivePrediction — learn a predictor by actively probing a system

NetGraph — represent an arbitrary neural network structure

NetTrain — train any neural network on CPUs, GPUs, etc.

NetModel — collection of trained and untrained models

FeatureExtractor — how to extract features to learn from

FeatureTypes — feature types to assume for input data

PerformanceGoal — whether to optimize for memory, quality, or speed

TimeGoal — how long to allocate for training, etc.

RandomSeeding — how to seed randomization

DeleteMissing — delete missing elements in data

Standardize — transform data to have zero mean and unit variance

MovingAverage — compute moving averages of lists, time series, etc.

ImageIdentify — recognize objects in images

ImageRestyle — restyle an image according to samples