Data Science - Machine Learning

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Machine learning enables a machine to automatically learn from data, improve performance from experiences, and predict things without being explicitly programmed. Machine Learning is mainly concerned with the development of algorithms which allow a computer to learn from the data and past experiences on their own. The term machine learning was first introduced by Arthur Samuel in 1959.

Data Science is the science of gaining useful insights from data in order to get the most crucial and relevant information source. And given a dependable stream of data, generating predictions using machine learning.

Data Science and machine learning are subfields of computer science that focus on analyzing and making use of large amounts of data to improve the processes by which products, services, infrastructural systems, and more are developed and introduced to the market.

The two relate to each other in a similar manner that squares are rectangles, but rectangles are not squares. Data Science is the all-encompassing rectangle, while machine learning is a square that is its own entity. They are both commonly employed by data scientists in their job and are increasingly being accepted by practically every business.

What is Machine Learning?

Machine learning (ML) is a type of algorithm that lets software get more accurate at predicting what will happen in future without being specifically programmed to do so. The basic idea behind machine learning is to make algorithms that can take data as input and use statistical analysis to predict an output while also updating outputs as new data becomes available.

Machine learning is a part of artificial intelligence that uses algorithms to find patterns in data and then predict how those patterns will change in the future. This lets engineers use statistical analysis to look for patterns in the data.

Facebook, Twitter, Instagram, YouTube, and TikTok collect information about their users, based on what you've done in the past, it can guess your interests and requirements and suggest products, services, or articles that fit your needs.

Machine learning is a set of tools and concepts that are used in data science, but they also show up in other fields. Data scientists often use machine learning in their work to help them get more information faster or figure out trends.

Types of Machine Learning

Machine learning can be classified into three types of algorithms −

• Supervised learning

• Unsupervised learning

• Reinforcement learning

Supervised Learning

Supervised learning is a type of machine learning and artificial intelligence. It is also called "supervised machine learning." It is defined by the fact that it uses labelled datasets to train algorithms how to correctly classify data or predict outcomes. As data is put into the model, its weights are changed until the model fits correctly. This is part of the cross validation process. Supervised learning helps organisations find large-scale solutions to a wide range of real-world problems, like classifying spam in a separate folder from your inbox like in Gmail we have a spam folder.

Supervised Learning Algorithms

Some supervised learning algorithms are −

• Naive Bayes − Naive Bayes is a classification algoritm that is based on the Bayes Theorem's principle of class conditional independence. This means that the presence of one feature doesn't change the likelihood of another feature, and that each predictor has the same effect on the result/outcome.

• Linear Regression − Linear regression is used to find how a dependent variable is related to one or more independent variables and to make predictions about what will happen in the future. Simple linear regression is when there is only one independent variable and one dependent variable.

• Logistic Regression − When the dependent variables are continuous, linear regression is used. When the dependent variables are categorical, like "true" or "false" or "yes" or "no," logistic regression is used. Both linear and logistic regression seek to figure out the relationships between the data inputs. However, logistic regression is mostly used to solve binary classification problems, like figuring out if a particular mail is a spam or not.

• Support Vector Machines(SVM) − A support vector machine is a popular model for supervised learning developed by Vladimir Vapnik. It can be used to both classify and predict data. So, it is usually used to solve classification problems by making a hyperplane where the distance between two groups of data points is the greatest. This line is called the "decision boundary" because it divides the groups of data points (for example, oranges and apples) on either side of the plane.

• K-nearest Neighbour − The KNN algorithm, which is also called the "k-nearest neighbour" algorithm, groups data points based on how close they are to and related to other data points. This algorithm works on the idea that data points that are similar can be found close to each other. So, it tries to figure out how far apart the data points are, using Euclidean distance and then assigns a category based on the most common or average category. However, as the size of the test dataset grows, the processing time increases, making it less useful for classification tasks.

• Random Forest − Random forest is another supervised machine learning algorithm that is flexible and can be used for both classification and regression. The "forest" is a group of decision trees that are not correlated to each other. These trees are then combined to reduce variation and make more accurate data predictions.

Unsupervised Learning

Unsupervised learning, also called unsupervised machine learning, uses machine learning algorithms to look at unlabelled datasets and group them together. These programmes find hidden patterns or groups of data. Its ability to find similarities and differences in information makes it perfect for exploratory data analysis, cross-selling strategies, customer segmentation, and image recognition.

Common Unsupervised Learning Approaches

Unsupervised learning models are used for three main tasks: clustering, making connections, and reducing the number of dimensions. Below, we'll describe learning methods and common algorithms used −

Clustering − Clustering is a method for data mining that organises unlabelled data based on their similarities or differences. Clustering techniques are used to organise unclassified, unprocessed data items into groups according to structures or patterns in the data. There are many types of clustering algorithms, including exclusive, overlapping, hierarchical, and probabilistic.

K-means Clustering is a popular example of an clustering approach in which data points are allocated to K groups based on their distance from each group's centroid. The data points closest to a certain centroid will be grouped into the same category. A higher K number indicates smaller groups with more granularity, while a lower K value indicates bigger groupings with less granularity. Common applications of K-means clustering include market segmentation, document clustering, picture segmentation, and image compression.

Dimensionality Reduction − Although more data typically produces more accurate findings, it may also affect the effectiveness of machine learning algorithms (e.g., overfitting) and make it difficult to visualize datasets. Dimensionality reduction is a strategy used when a dataset has an excessive number of characteristics or dimensions. It decreases the quantity of data inputs to a manageable level while retaining the integrity of the dataset to the greatest extent feasible. Dimensionality reduction is often employed in the data pre-processing phase, and there are a number of approaches, one of them is −

Principal Component Analysis (PCA) − It is a dimensionality reduction approach used to remove redundancy and compress datasets through feature extraction. This approach employs a linear transformation to generate a new data representation, resulting in a collection of "principal components." The first principal component is the dataset direction that maximises variance. Although the second principal component similarly finds the largest variance in the data, it is fully uncorrelated with the first, resulting in a direction that is orthogonal to the first. This procedure is repeated dependent on the number of dimensions, with the next main component being the direction orthogonal to the most variable preceding components.

Reinforcement Learning

Reinforcement Learning (RL) is a type of machine learning that allows an agent to learn in an interactive setting via trial and error utilising feedback from its own actions and experiences.

Key terms in Reinforcement Learning

Some significant concepts describing the fundamental components of an RL issue are −

• Environment − The physical surroundings in which an agent functions

• Condition − The current standing of the agent

• Reward − Environment-based feed-back

• Policy − Mapping between agent state and actions

• Value − The future compensation an agent would obtain for doing an action in a given condition.

Data Science vs Machine Learning

Data Science is the study of data and how to derive meaningful insights from it, while machine learning is the study and development of models that use data to enhance performance or inform predictions. Machine learning is a subfield of artificial intelligence.

In recent years, machine learning and artificial intelligence (AI) have come to dominate portions of data science, playing a crucial role in data analytics and business intelligence. Machine learning automates data analysis and makes predictions based on the collection and analysis of massive volumes of data about certain populations using models and algorithms. Data Science and machine learning are related to each other, but not identical.

Data Science is a vast field that incorporates all aspects of deriving insights and information from data. It involves gathering, cleaning, analysing, and interpreting vast amount of data to discover patterns, trends, and insights that may guide business choices.

Machine learning is a subfield of data science that focuses on the development of algorithms that can learn from data and make predictions or judgements based on their acquired knowledge. Machine learning algorithms are meant to enhance their performance automatically over time by acquiring new knowledge.

In other words, data science encompasses machine learning as one of its numerous methodologies. Machine learning is a strong tool for data analysis and prediction, but it is just a subfield of data science as a whole.

Given below is the table of comparison for a clear understanding.