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Basic Concepts of Data Science

Data Science is the study of every kind of data. It is used to infer useful insights from datasets. Data science combines mathematics, technology, and business acumen. Nowadays, a tremendous amount of data is being produced every second, and the need to understand the basic concepts of data science is more than ever. Data science and machine learning are often talked about collectively. In data science, information and insights are deduced from structured and unstructured data by the effective use of scientific processes and methodologies, algorithms, and frameworks.

Artificial Intelligence

Artificial intelligence is a ​​computer science field that aims to create intelligent computers and machines. The primary purpose of AI is to emulate human brain intelligence in computers using data processing and algorithms.

Machine Learning

Machine learning is a subcategory of AI. Machine learning is the method of using algorithms and data to enable machines and computers to learn on their own. A computer learns with experience. The more exposure to the data an algorithm gets, the more efficient it becomes.

Deep Learning

It is a type of machine learning. In deep learning, computers learn by example. Common examples of deep learning include self-driving cars, image and text recognition, voice recognition, etc. The accuracy of deep learning models is usually very high. Deep learning neural networks have a large number of layers.

Model and Algorithm

Data science generally involves real-world scenarios, and a model is a mathematical representation of that real-world scenario. For example, there are many forecasting and prediction models for weather, population, financial activities, etc. Moreover, the algorithms provide directions to the models in the form of rules and regulations. These rules are used for calculating the solution to a problem.

Dataset and Training

Dataset is the collection of any form of data. Data can be in an organized form or raw form. On the other hand, training is the process of generating a model from the data using various algorithms. Datasets are split into training-sets and test-sets. The training set is fed to the model, and the model can recognize patterns if there exist any patterns. Test sets contain data with desired results, and it is used to validate the results of training sets. Additionally, there is a target value, and it is known as the dependent variable. It is the output that a model should produce.

Regression and Classification

Regression uses previous values of a dataset and predicts future values by recognizing patterns between input and output variables. For example, the prediction of fuel consumption of a vehicle is a regression problem. Classification, on the other hand, classifies the input data into predefined categories.

Feature and Feature Set

Features are the patterns which are deduced from datasets such as faces and objects in images. Features are observed parameters and are also called prediction variables. A dataset that contains all features of a particular problem is called a feature set.

Overfitting and Regularization

Overfitting occurs when the model is not appropriately trained because the dataset does not have enough features to properly generalize the relationship. If a model is overfitted, then the predictions from the features are not accurate. Regularization is used to prevent overfitting by simplifying the model so that the prediction accuracy is increased.

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