Machine Learning Techniques

Machine learning is a subset of artificial intelligence. Machine learning algorithms take a particular set of parameters as input and predict an output. A technique is a method of solving a specific problem. Let us look at all the different machine learning techniques in python.

Machine Learning Techniques

1. Supervised machine learning

Supervised learning is a type of machine learning which learns from labelled data. Labelled data is the type of data which has particular values assigned to it.

Supervised learning is used for structured datasets. It contains several data points or samples described with the help of an input variable. The output variable is known as the label. It learns the training dataset and creates a function the algorithm will use on another input.

This type of learning makes use of tagged data to train predictive models. Training models automatically categorise tagged data for data points whose values are unknown.

In supervised learning, the machine will already know the algorithm’s output before it performs operations because it was already taught to it.

The algorithm needs to figure out the steps to go from input to output for any unknown data point given to it. We teach the algorithm with the help of a training dataset. If, in any case, the algorithm provides a wrong output, we go back and train the model again by making slight changes.
An algorithm connects the input variable(x) to the output variable (y).

There are three broad ways to categorise a machine learning problem:

a. Classification

Suppose two people have decided to buy a product online. The experience of customer A with the product was good, and the experience of customer B with the product was bad. We need a model that distinguishes between good and bad experiences with a product for different customers. It is precisely what a classification model does.

Classification problems are further divided into binary classification and multi-class classification.

We draw a decision boundary on the plot graph separating positive and negative reviews. The model is trained with vast amounts of data along with the answers or names of the classes. It allows the model to draw a plot graph and a decision boundary. Based on this, it can predict the class of any unknown data point.

b. Regression

Regression problems are similar to classification problems, except the output is continuous real numbers.

2. Unsupervised machine learning

Unsupervised machine learning is used on raw datasets to find structures and patterns from an unstructured dataset. It tries to find already existing patterns in the data when no underlying truth is known to the model.

The most common unsupervised learning model is clustering, where we group natural clusters from an existing dataset. The concept of unsupervised learning is limited and needs to be in more use.

The learning process in unsupervised learning is complex since the system needs proper input and output. Nevertheless, unsupervised learning has the power to find patterns from vast amounts of data.

a. Clustering

The clustering algorithm aims to detect common patterns and similarities between data points in the given dataset.

Examples include spam filtering and book recommendations. The most common clustering algorithm is the K-means clustering algorithm.

Reinforcement Learning

This learning type gives the software lots of freedom to decide what the ideal behaviour should be within any context. It allows the algorithm to maximise its performance in a manner that allows it to grow. Simple feedback about the performance of a machine allows it to perform better every next time.

In reinforcement learning, the agent decides the next step based on the current state. It helps machines to learn the outcome of what exactly they are doing.

This kind of learning allows machines to distinguish between good and bad behaviour.

Anomaly Detection

Anomaly detection is the process of identifying different or unexpected events or patterns in the dataset. These patterns or events differ from the regular flow of the database.

Anomaly is any abnormal activity that takes place in a data set. It is recognised so that it does not impact the end-user or the customer. The anomaly may give wrong results and thus should be identified.

In a large organisation with vast amounts of data, all the transactions must be matched and checked for mistakes to be recognised.

Working:

• The model is trained by giving lots of data containing all the faulty information.
• The machine learning model develops a kind of intelligence that identifies faulty behaviour.
• It helps us analyse the anomalies that occur frequently.
• These can then be corrected.

Dimensionality Reduction

Dimensionality reduction is how we reduce high-dimensional data into some meaningful representation with reduced dimensions. The reduced dimensions have a dimensionality similar to that of the data.

The minimum number of parameters needed to account for the observed properties of the data is known as ‘Intrinsic Dimensionality.’

Dimensionality reduction removes the curse of dimensionality. It must ensure that it projects the same information concisely. We use it to perform feature selection and feature extraction. It helps us find the essential features with which we can make a prediction.

N dimensions of the dataset can be reduced to k dimensions. These k dimensions are easily identified from the dataset or can be a combination of different dimensions.

Ensemble Methods in Machine Learning

Ensemble methods mix a lot of different machine learning algorithms to produce one meta-algorithm. It is done to:

1. Decrease variance(bagging)
2. Bias(boosting)
3. Improve predictions(stacking)

It combines many decision trees to increase predictive performances rather than combining one decision tree. The main idea behind ensemble methods is that many weak learners can come together to form one strong learner.

These are ideally divided into two groups:

1. Sequential Ensemble Methods: We sequentially generate the base learners and this model works on the independence between base learners.

2. Parallel Ensemble Methods: We generate base learners in a parallel manner.

Homogeneous ensembles are made of learners of the same type to make a single base learning algorithm. Some methods make use of heterogeneous learners leading to heterogeneous ensembles.
The three most common ensemble methods are boosting, bagging and stacking.

Decision Trees

A decision tree is in the form of a tree, where each branch node is a choice among many alternatives, and each leaf node is a decision. It works on both discrete and continuous values.
Decision trees train themselves from any given dataset and can predict unforeseen circumstances. As a result, they are helpful in medical analysis and disease detection.

The trees are made of three types of nodes. They are as follows:

Root Node: It does not have any incoming edges and is made of one or more outgoing edges.

Internal Nodes: It has one incoming edge along with two or more outgoing edges.

Leaf or Terminal Nodes: It has only one incoming edge and no outgoing edges.
Each leaf node is given a class label. In addition, the non-terminal nodes, including the root node, contain test conditions that help make predictions. Also, the attributes in the decision trees can be binary or ordinal.

Neural Networks and Deep Learning

A neural network is made of a large group of nodes called neurons. These nodes are interconnected.
A neural network works when some input is fed or given to it. We process this data by passing it through several perceptron layers to get the output. These processing layers are also known as hidden layers.

Output from one layer is given to another layer for processing. We train neural networks with the help of stochastic gradient descent (SGD) and backpropagation algorithm.

Deep Learning

Deep learning is a subset of machine learning aiming to model high-level abstractions in the data. There are two sets of neurons. The first set of neurons receives the input and the second of neurons predicts the output. In between these two sets are processing layers made of many linear and non-linear transformations.

The most common deep learning architectures are:

• Deep feedforward networks
• Convolution networks
• Recurrent networks.

Transfer Learning

Transfer learning is a machine learning method. In this learning method, the main idea is to train to perform one task and then use the same knowledge to perform another similar task.

For example, we want to train a convolutional neural network to identify an image of the cheetah. We only have 1000 images of the cheetahs to give as the training dataset. However, this amount is not enough.

We can use existing CNN models that are trained with millions of animals. In this dataset, we can add these 1000 cheetah images so the model can identify cheetahs very well.

Here the knowledge of identifying all animals is used to identify cheetahs by providing specific training. It is an example of transfer learning.

Natural Language Processing

Natural language processing is the method in artificial intelligence to deal with human languages. It gives the machine the power to read, understand and derive conclusions from human languages.

NLP combines human languages and computer science to decipher language structure. These models can comprehend, break down and separate details from text and speech.

Data analysis and machine learning experts take data from human interactions to give machines the power to mimic human linguistic behaviour.

Word Embedding

Word embedding or word vector is a numeric vector input that represents a word. For example, in this method, instead of using the characters for the word “the” as input, we would use a length 500 numeric vector to represent “the”.

Every word in our vocabulary has a unique vector associated with it. The way they are placed is on the basis that words similar to each other will be used more often. For example, cats and dogs are placed together with the word veterinarian.

Conclusion

In this article, we briefly looked at the various machine-learning techniques. We hope our explanation was easy to understand.