Regression Models – Statistical Machine Learning

What you'll learn

Thorough understanding on Multiple Linear Regression models - Model estimation, building methods and many more statistical measures for model evaluation
Comprehensive understanding on Logistic regression models - building the understanding based on the concepts of Multiple Linear Regression models
A strong conceptual understanding on Simple linear regression models that plays a vital role in understanding the multiple regression model concepts
A complete understanding on concepts like Correlation, linear regression fits & Nonlinear transformations that are basic and essential for the couresential

Requirements

A good understanding on basic concepts on Probability and statistics involving probability distributions, Mean, median, Central tendency, Variance, Sampling distributions, Point estimates , Confidence intervals hypothesis tests,

Description

Regression modelsare supervised machine learning techniques used to predict continuous numerical values. By analyzing relationships between independent variables (features) and a dependent variable (target), they identify trends to forecast future outcomes.Statistical machine learningcombines traditional statistical inference with computational algorithms to learn patterns from data, quantify uncertainty, and make predictions.
This course provides an in depth and comprehensive coverage onMultiple Linear Regression models and Logistic Regressionand focusses oncomplete breadth and depth of statistical measuresthat play a pivot role in carrying out the regression analysis.
The course provides a detailed stepwise understanding beginning with concepts oncorrelation, all variants of R-Square, least squares line fit, concept of regressionand gradually introduces the student to a complete understanding onSimple linear regression modelsand finally leading the student to a comprehensive understanding on Multiple linear and logistic regression models. This course also ensures that the student understandtransformations on non-linear relationshipsbetween the predictor and the response variables where necessary so that even non-linear relationships are effectively handled and accommodated within linear models
Regression analysis helps organizations and researchers replace guesswork with data-driven insights. Common use cases include
- Forecasting: Predicting future sales, housing prices, or temperatures.
- Risk Analysis: Estimating the likelihood of a financial event or assessing credit risks.
- Causal Analysis: Determining how changes in one factor (e.g., marketing spend) affect a target outcome (e.g., revenue)

Who this course is for

Intended for Data Scientists, Data Analysts and for those in academics in the area of Statistics and Machine learning

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