In today’s time, Machine learning has become a common term with technology enthusiasts and with the given rate of its usage, soon it will become layman’s language and will gel into everyday life of a common man.
We have already understood Machine learning in previous blogs, in todays’ discussion we will focus on the machine learning models and how they are helping computer machines in recognising patterns and reducing the durations of processes significantly.
Let’s read further to understand.
A machine learning model is a computer software that has been taught to recognise particular patterns. You train a model on a set of data and give it an algorithm to use to reason about and learn from that data.
You can use the model to reason about data it hasn't seen before and make predictions about it once it's been trained.
Consider this scenario: you want to make an app that can recognise a user's emotions based on their facial expressions. You can train a model by feeding it images of faces with different emotions labelled on them, and then use that model in an app to determine any user's mood.
A model is a simplified representation of something or a process. The Earth, for example, is not shaped like a sphere, yet we may regard it as one if we're making a globe.
Similarly, assuming the world is deterministic, some natural process decides whether or not a buyer will purchase a product from a website.
We could build something that approximates that process, in which we provide some information about a consumer and it informs us whether or not that consumer is likely to purchase a product.
As a result, a "machine learning model" is a model created by a machine learning system.
(Related blog: Top machine learning tools)
Machine learning models
Different machine learning models are based on different types of machine learning. So, the models are categorised into the type of learning that they follow:
Classification is a predictive modelling task in machine learning where a class label is predicted for a given sample of input data.
In terms of modelling, classification necessitates a training dataset with a large number of instances of inputs and outputs from which to learn.
The training dataset will be used to find the optimum way to map samples of input data to specified class labels. As a result, the training dataset has to be sufficiently representative of the issue and contain a large number of samples of each class label.
It's used for spam filtering, language identification, document search, sentiment analysis, handwritten character recognition, and fraud detection.
The following are some examples of popular classification methods.
Logistic regression - It is a linear model that may be used to classify binary data.
The K-Nearest Neighbors algorithm- simple yet time-consuming, KNN classifies a new data point based on similarity.
Decision Tree — a classifier based on the ‘If Else' principle that is more resistant to outliers. Learn more about Decision trees here.
Support vector machines- It may be used to classify binary and multiclass data.
Naive Bayes- It is based on the Bayesian model.
Regression analysis is a statistical approach for modelling the connection between one or more independent variables and a dependent (target) variable.
Regression analysis, in particular, allows us to see how the value of the dependent variable changes in relation to an independent variable while the other independent variables are maintained constant. Temperature, age, salary, price, and other continuous/real data are predicted.
Regression is simply the "best guess" method for generating a forecast from a set of data. Fitting a set of points to a graph is what it is called.
Predicting the price of an aeroplane ticket, for example, is a common regression job. Let's take a look at some of the most common regression models in use today.
Linear Regression - The most basic regression model, it works best when data is linearly separable and there is little or no multicollinearity.
Lasso Regression - Linear regression with L2 regularization.
Ridge Regression - Linear regression with L1 regularization.
Support vector regression (SVR) - based on the same concepts as the Support Vector Machine (SVM) for classification, with a few small modifications.
Ensemble regression- It aims to increase prediction accuracy in learning situations with a numerical target variable by combining many models.
(Recommended blog: 7 types of regression techniques)
Neural networks are a set of algorithms that identify patterns and are roughly fashioned after the human brain. They use a sort of machine perception to understand sensory inputs, categorising or grouping raw data.
All real-world data, whether pictures, music, text, or time series, must be translated into the patterns they recognise, which are numerical and encoded in vectors.
A deep neural network examines data using learnt representations that are similar to how people think about problems. The algorithm is given a collection of relevant characteristics to examine in typical machine learning, but in deep learning, the algorithm is given raw data and derives the features itself.
(Must check: Deep Learning algorithms)
Deep learning is a subset of machine learning that employs multilayer neural networks. Deep learning has progressed in sync with the digital era, which has resulted in an avalanche of data in all formats and from all corners of the globe.
Big data is gathered from a variety of sources, including social media, internet search engines, e-commerce platforms, and online theatres.
However, since the data is typically unstructured, it might take decades for humans to analyse and extract useful information.
Companies are increasingly using AI systems for automated support as they see the enormous potential that can be realised by unlocking this wealth of data.
Let's look at some of the most important deep learning models based on neural network architecture:
Convolution Neural Networks (CNN)
Recurrent Neural Networks (RNN)
Clustering is the process of partitioning a population or set of data points into several groups so that data points in the same group are more similar to each other and different to data points in other groups. It is essentially a grouping of items based on their similarity and dissimilarity.
(Related blog: Clustering methods and applications)
Clustering is critical since it determines the inherent grouping among the unlabeled data. There are no requirements for a successful clustering. It is up to the user to determine what criteria they will employ to meet their needs.
For example, we could be interested in locating representations for homogenous groups (data reduction), locating “natural clusters” and describing their unknown qualities (“natural” data types), locating useful and appropriate groupings (“useful” data classes), or locating uncommon data items (outlier detection). This algorithm must make certain assumptions about point similarity, and each assumption results in a different yet equally acceptable cluster.
It's mostly used for consumer segmentation, data tagging, and abnormal behaviour detection, among other things. Some of the most often used clustering models are as follows:
K means – Simple but suffers due to high variance.
K means++ – Modified version of K means algorithm.
K medoids- A clustering algorithm that resembles K-means clustering technique.
Agglomerative clustering – A hierarchical clustering model(bottom-up approach).
DBSCAN – Density-based spatial clustering of applications with noise etc.
Association rule learning is an unsupervised learning approach that examines the interdependence of one data item on another and maps appropriately to make it more lucrative.
It tries to uncover some interesting relationships or links between the dataset's variables. It uses a set of rules to find interesting relationships between variables in a database.
One of the most significant topics in machine learning is association rule learning, which is used in Market Basket analysis, Web usage mining, continuous manufacturing, and other applications.
Market basket analysis is a methodology used by many large retailers to figure out how goods are related. We may explain it by using the example of a supermarket, where all things purchased at the same time are grouped together.
For example, if a consumer buys bread, he'll almost certainly also buy dairy products, therefore these items are kept on the same shelf or in close proximity.
The following are some examples of association rule models;
Apriori- This algorithm uses frequent datasets to generate association rules.
Eclat- Eclat algorithm stands for Equivalence Class Transformation.
F-P growth- The F-P growth algorithm stands for Frequent Pattern, and it is the improved version of the Apriori Algorithm.
(Also catch: EM algorithm in ML)
The dimensionality of a dataset refers to the number of input variables or characteristics. Techniques for reducing the number of input variables in a dataset are known as dimensionality reduction.
The curse of dimensionality refers to the fact that adding more input characteristics to a predictive modelling activity makes it more difficult to model.
For data visualisation, high-dimensionality statistics and dimensionality reduction methods are frequently utilised.
Nonetheless, in applied machine learning, similar strategies may be utilised to reduce a classification or regression dataset in order to better train a prediction model.
Let's look at some of the most prevalent dimensionality reduction models.
Principal component analysis(PCA) - It uses a high number of predictors to build a smaller number of new variables. The new variables are unrelated to one another, yet they are less interpretable.
t-distributed stochastic neighbour embedding(t-SNE) — Allows higher-dimensional data points to be embedded in a lower-dimensional space.
Singular value decomposition (SVD)- It is a technique for effectively decomposing a matrix into smaller components.
This problem is addressed by a variety of algorithms. In reality, a certain type of issue defines Reinforcement Learning, and all of its solutions are classified as Reinforcement Learning algorithms.
In this issue, an agent must determine the appropriate course of action depending on his present situation. The problem is known as a Markov Decision Process when this phase is repeated.
Markov decision process (MDP) models are widely used in engineering, economics, computer science, and the social sciences to describe sequential decision-making issues.
Many real-world issues addressed by MDPs have large state and/or action spaces, exposing them to the dimensionality curse and rendering practical solutions to the resulting models intractable.
(Must read: What is inverse reinforcement learning?)
Value-based machine learning algorithms include Q Learning. The goal is to find the best value function for a particular problem/environment. The letter ‘Q' stands for quality, and it aids in determining the next step that will result in the greatest quality condition.
This method is straightforward and intuitive. It's an excellent spot to begin your RL trip. A table called a Q Table is used to hold the data. The predicted future payoff of that action at that state is returned by Q(state, action).
This function may be approximated using Q-Learning, which uses the Bellman equation to iteratively update Q(s,a). The agent will begin to exploit the surroundings and take better actions once the Q-Table is available.
We can conclude that these models prove very decisive in some critical processes of our lives. These models and algorithms together constitute an ecosystem that strives to make our everyday simple and easy.
(Also read: Types of machine learning)
It is due to such machine models that we are able to carry out the gigantic processes in a matter of seconds and live our lives peacefully.
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