A rapidly developing field of technology, machine learning allows computers to automatically learn from previous data. For building mathematical models and making predictions based on historical data or information, machine learning employs a variety of algorithms. It is currently being used for a variety of tasks, including speech recognition, email filtering, auto-tagging on Facebook, a recommender system, and image recognition.
In the real world, we are surrounded by humans who can learn everything from their experiences with their learning capability, and we have computers or machines which work on our instructions. But can a machine also learn from experiences or past data like a human does? So here comes the role of Machine Learning.
A subset of artificial intelligence known as machine learning focuses primarily on the creation of algorithms that enable a computer to independently learn from data and previous experiences
Arthur Samuel first used the term “machine learning” in 1959. It could be summarized as follows:
Without being explicitly programmed, machine learning enables a machine to automatically learn from data, improve performance from experiences, and predict things.
Machine learning algorithms create a mathematical model that, without being explicitly programmed, aids in making predictions or decisions with the assistance of sample historical data, or training data. For the purpose of developing predictive models, machine learning brings together statistics and computer science. Algorithms that learn from historical data are either constructed or utilized in machine learning. The performance will rise in proportion to the quantity of information we provide.
A machine learning system builds prediction models, learns from previous data, and predicts the output of new data whenever it receives it. The amount of data helps to build a better model that accurately predicts the output, which in turn affects the accuracy of the predicted output. Let’s say we have a complex problem in which we need to make predictions. Instead of writing code, we just need to feed the data to generic algorithms, which build the logic based on the data and predict the output. Our perspective on the issue has changed as a result of machine learning.
Machine learning algorithms are essentially sets of instructions that allow computers to learn from data, make predictions, and improve their performance over time without being explicitly programmed. Machine learning algorithms are broadly categorized into three types:
1. Supervised Learning
Supervised algorithms as the name suggests are trained using labelled data, which means the input data is tagged with the correct output. The aim here is to learn mapping from inputs to outputs, making it possible to predict the output for new data. Common supervised learning algorithms include:
Linear Regression: Linear regression is one of the easiest and most popular Machine Learning algorithms. Used for predicting continuous outcomes. Linear Regression predicts a continuous value by finding the best-fit straight line between input (independent variable) and output (dependent variable). This reduces the difference between predicted and actual values.
Logistic Regression: Logistic Regression is used for predicting the categorical dependent variable using a given set of independent variables. In simple teams, its is used for binary classification tasks (e.g., predicting yes/no outcomes). It estimates probabilities using a logistic function. Logistic Regression is much similar to the Linear Regression except that how they are used. Linear Regression is used for solving Regression problems, whereas Logistic regression is used for solving the classification problems.
Decision Trees: These models predict the value of a target variable by learning simple decision rules inferred from the data features. Decision Tree is a Supervised learning technique that can be used for both classification as well as regression problems. It is a tree-structured classifier, where internal nodes represent the features of a dataset, branches represent the decision rules and each leaf node represents the outcome. In a Decision tree, there are two nodes, which are the Decision Node and Leaf Node. Decision nodes are used to make any decision and have multiple branches, whereas Leaf nodes are the output of those decisions and do not contain any further branches.
Random Forest: An ensemble of decision trees, typically used for classification and regression, improving model accuracy and overfitting control. Random Forest is a popular machine learning algorithm that belongs to the supervised learning technique. It is based on the concept of ensemble learning, which is a process of combining multiple classifiers to solve a complex problem and to improve the performance of the model.
Support Vector Machines (SVM): Effective in high-dimensional spaces, SVM is primarily used for classification but can also be used for regression. The goal of the SVM algorithm is to create the best line or decision boundary that can segregate n-dimensional space into classes so that we can easily put the new data point in the correct category in the future. This best decision boundary is called a hyperplane. SVM chooses the extreme points/vectors that help in creating the hyperplane. These extreme cases are called as support vectors, and hence algorithm is termed as Support Vector Machine.
Neural Networks: These are powerful models that can capture complex non-linear relationships. They are widely used in deep learning applications.
2. Unsupervised Learning
Unsupervised algorithms are used with data sets without labelled responses. The goal here is to infer the natural structure present within a set of data points. Common unsupervised learning techniques include:
Clustering: Algorithms like K-means, hierarchical clustering, and DBSCAN group a set of objects in such a way that objects in the same group are more similar to each other than to those in other groups. Clustering or cluster analysis is a machine learning technique, which groups the unlabelled dataset. It can be defined as “A way of grouping data points into different clusters, consisting of similar data points. The objects with the possible similarities remain in a group that has less or no similarities with another group.” Below are the main clustering methods used in Machine learning:
- Partitioning Clustering
- Density-Based Clustering
- Distribution Model-Based Clustering
- Hierarchical Clustering
- Fuzzy Clustering
Association: These algorithms find rules that describe large portions of your data, such as market basket analysis. Association rule learning is a type of unsupervised learning technique that checks for the dependency of one data item on another data item and maps accordingly so that it can be more profitable. It tries to find some interesting relations or associations among the variables of dataset. It is based on different rules to discover the interesting relations between variables in the database. Association rule learning can be divided into three types of algorithms:
- Apriori
- Eclat
- F-P Growth Algorithm
Principal Component Analysis (PCA): A statistical procedure that uses an orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables. Principal Component Analysis is an unsupervised learning algorithm that is used for the dimensionality reduction. It is a statistical process that converts the observations of correlated features into a set of linearly uncorrelated features with the help of orthogonal transformation. These new transformed features are called the Principal Components. PCA generally tries to find the lower-dimensional surface to project the high-dimensional data. The PCA algorithm is based on some mathematical concepts such as:
- Variance and Covariance
- Eigenvalues and Eigen factors
Autoencoders: Special type of neural network used to learn efficient coding of unlabelled data.
3. Reinforcement Learning
These algorithms learn to make a sequence of decisions. The algorithm learns to achieve a goal in an uncertain, potentially complex environment. In reinforcement learning, an agent makes decisions by following a policy based on which actions to take, and it learns from the consequences of these actions through rewards or penalties.
Q-learning: This is a model-free reinforcement learning algorithm that learns the value of an action in a particular state.
Deep Q-Networks (DQN): It combines Q-learning with deep neural networks, allowing the approach to learn successful policies directly from high-dimensional sensory inputs.
Policy Gradient Methods: These methods optimize the parameters of a policy directly as opposed to estimating the value of actions.
Monte Carlo Tree Search (MCTS): Used in decision processes for finding optimal decisions by playing out scenarios, notably used in games.
— -// We will learn of all the algorithms in details in next articles // — –
The demand for machine learning is steadily rising. Because it is able to perform tasks that are too complex for a person to directly implement, machine learning is required. Humans are constrained by our inability to manually access vast amounts of data; as a result, we require computer systems, which is where machine learning comes in to simplify our lives.
By providing them with a large amount of data and allowing them to automatically explore the data, build models, and predict the required output, we can train machine learning algorithms. The cost function can be used to determine the amount of data and the machine learning algorithm’s performance. We can save both time and money by using machine learning.
The significance of AI can be handily perceived by its utilization’s cases, Presently, AI is utilized in self-driving vehicles, digital misrepresentation identification, face acknowledgment, and companion idea by Facebook, and so on. Different top organizations, for example, Netflix and Amazon have constructed AI models that are utilizing an immense measure of information to examine the client interest and suggest item likewise.
Following are some key points which show the importance of Machine Learning:
- Rapid increment in the production of data
- Solving complex problems, which are difficult for a human
- Decision making in various sector including finance
- Finding hidden patterns and extracting useful information from data.
— -Happy Reading — –