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Regression and Gradient Descent

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Overview

Dig deep into regression and learn about the gradient descent algorithm. This course does not rely on high-level libraries like scikit-learn, but focuses on building these algorithms from scratch for a thorough understanding. Master the implementation of simple linear regression, multiple linear regression, and logistic regression powered by gradient descent.

Syllabus

Unit 1: Understanding and Implementing Simple Linear Regression from Scratch

Unveiling the Magic of Sales Prediction
Predicting Sales Using Simple Linear Regression Constants
Calculating the Coefficients of Linear Regression
Mysterious Prediction Model Failure

Unit 2: Implementing Multiple Linear Regression from Scratch

Determining House Prices with Multiple Features
Predicting Housing Prices with Multiple Linear Regression
Calculating Coefficients in Multiple Linear Regression
House Price Prediction with Multiple Linear Regression

Unit 3: Gradient Descent Optimization in Linear Regression

Adjust the Learning Rate
Applying Gradient Descent in Real Estate Pricing
Implementing Gradient Descent in Real Estate Analysis
Trying New Approach

Unit 4: Understanding Logistic Regression and Its Implementation Using Gradient Descent

Sigmoid Function: From Input to Probability
Implementing the Sigmoid Function
Evaluating Spam Filter Accuracy with Logistic Regression
Adding the Gradient to Logistic Regression
Implementing the Sigmoid Function in Logistic Regression

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