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Machine learning is an AI discipline geared toward the technological development of human knowledge. Machine learning allows computers to handle new situations via analysis, self training, observation and experience.

ML facilitates the continuous advancement of computing through exposure to new scenarios, testing and adaptation, while employing pattern and trend detection for improved decisions in subsequent (though not identical) situations.

Systems that learn to recognize objects, patterns or events that are important; without someone explicitly programming what that object, pattern or event is ahead of time. This is achieved by iterating the model through trials with large quantities of training data

Deep learning is a collection of algorithms used in machine learning, used to model high-level abstractions in data through the use of model architectures, which are composed of multiple nonlinear transformations. It is part of a broad family of methods used for machine learning that are based on learning representations of data.

1. Input data is called training data and has a known label or result such as spam/notspam or a stock price at a time.

2. A model is prepared through a training process in which it is required to make predictions and is corrected when those predictions are wrong. The training process continues until the model achieves a desired level of accuracy on the training data.

3. Example problems are classification and regression.

4. Example algorithms include Logistic Regression and the Back Propagation Neural Network.

1. Input data is not labeled and does not have a known result.

2. A model is prepared by deducing structures present in the input data. This may be to extract general rules. It may be through a mathematical process to systematically reduce redundancy, or it may be to organize data by similarity.

3. Example problems are clustering, dimensionality reduction and association rule learning.

4. Example algorithms include: the Apriori algorithm and k-Means

1. Input data is a mixture of labeled and unlabelled examples.

2. There is a desired prediction problem but the model must learn the structures to organize the data as well as make predictions.

3. Example problems are classification and regression.

4. Example algorithms are extensions to other flexible methods that make assumptions about how to model the unlabeled data.

Regression is concerned with modeling the relationship between variables that is iteratively refined using a measure of error in the predictions made by the mode.

Regression methods are a workhorse of statistics and have been coopted into statistical machine learning. This may be confusing because we can use regression to refer to the class of problem and the class of algorithm. Really, regression is a process.

The most popular regression algorithms are:

• Ordinary Least Squares Regression (OLSR)

• Linear Regression • Logistic Regression

• Stepwise Regression

• Multivariate Adaptive Regression Splines (MARS)

• Locally Estimated Scatterplot Smoothing (LOESS)

Decision tree methods construct a model of decisions made based on actual values of attributes in the data.

Decisions fork in tree structures until a prediction decision is made for a given record. Decision trees are trained on data for classification and regression problems. Decision trees are often fast and accurate and a big favorite in machine learning

The most popular decision tree algorithms are:

• Classification and Regression Tree (CART)

• Iterative Dichotomiser 3 (ID3)

• C4.5 and C5.0 (different versions of a powerful approach)

Clustering, like regression, describes the class of problem and the class of methods.

Clustering methods are typically organized by the modeling approaches such as centroid-based and hierarchal. All methods are concerned with using the inherent structures in the data to best organize the data into groups of maximum commonality

The most popular clustering algorithms are:

• k-Means

• k-Medians

• Expectation Maximisation (EM)

• Hierarchical Clustering

Ensemble methods are models composed of multiple weaker models that are independently trained and whose predictions are combined in some way to make the overall prediction.

Much effort is put into what types of weak learners to combine and the ways in which to combine them. This is a very powerful class of techniques and as such is very popular.

• Boosting

• Bootstrapped Aggregation (Bagging)

• AdaBoost

• Stacked Generalization (blending)