Regression Analysis:

Regression analysis is a predictive modeling technique that estimates the relationship between two or more variables. Recall that a correlation analysis makes no assumption about the causal relationship between two variables. Regression analysis focuses on the relationship between a dependent (target) variable and an independent variable(s) (predictors). Here, the dependent variable is assumed to be the effect of the independent variable(s). The value of predictors is used to estimate or predict the likely-value of the target variable.

For example to describe the relationship between diesel consumption and industrial production, if it is assumed that “diesel consumption” is the effect of…


  • Computer vision is an application of deep learning that enables a computer to see, identify and locate the objects in an image (or videos) in the same way a human does.
  • One of the important applications of Computer vision is self-driving cars wherein the machine learns to identify what is in front of it to decide on its next move.
  • Other applications include face recognition, video surveillance, gesture recognition, etc.

Classification Loss


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Logistic Regression

Regression allows us to predict an output based on some input parameters. For instance, we can predict someone’s height based on their parents height and age. This type of regression is called linear regression because the outcome variable is a continuous real number.

But what if we wanted to predict something that is not a continuous number?

Why Not Linear Regression?

Let us say we want to predict likelihood of a candidate to pass the Math’s Olympiad for class X. Using ordinary linear regression will not work in this scenario because it doesn’t make sense to treat our outcome as a continuous number —…


Generative Pre-trained Transformer 3 (GPT-3) is an auto-regressive language model that uses deep learning to produce human-like text. It is the third-generation language prediction model in the GPT-n series (and the successor to GPT-2) created by OpenAI, a San Francisco-based artificial intelligence


Boosting

Boosting is an ensemble method that combines several weak learners into a strong learner sequentially. In boosting methods, we train the predictors sequentially, each trying to correct its predecessor.

Gradient Boosting

Gradient Boosting is the grouping of Gradient descent and Boosting. In gradient boosting, each new model minimizes the loss function from its predecessor using the Gradient Descent Method. This procedure continues until a more optimal estimate of the target variable has been achieved

Unlike other ensemble techniques, the idea in gradient boosting is that they build a series of trees where every other tree tries to correct the mistakes of its…


Clustering

Clustering is one of the major data mining methods for knowledge discovery in large databases. It is the process of grouping large data sets according to their similarity. Cluster analysis is a major tool in many areas of engineering and scientific applications including data segmentation, discretization of continuous attributes, data reduction, outlier detection, noise filtering, pattern recognition and image processing

DBSCAN (Density-Based Spatial Clustering of Applications with Noise)

DBSCAN (Density-based spatial clustering of applications with noise) is an important spatial clustering technique that is widely adopted in numerous applications. DBSCAN is a clustering method that is used in machine learning to separate clusters of high density from clusters…

RAJAT PANCHOTIA

Fascinated by computer science and want to be part of an exciting and continually developing industry. An enthusiast machine learner.

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