Can we make prediction without need of going through iteration ? yes with the Normal Equation _ Day 6

Understanding Linear Regression: The Normal Equation and Matrix Multiplications Explained Understanding Linear Regression: The Normal Equation and Matrix Multiplications Explained Linear regression is a fundamental concept in machine learning and statistics, used to predict a target variable based on one or more input features. While gradient descent is a popular method for finding the best-fitting line, the normal equation offers a direct, analytical approach that doesn’t require iterations. This blog post will walk you through the normal equation step-by-step, explaining why and how it works, and why using matrices simplifies the process. Table of Contents Introduction to Linear Regression Gradient...

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Models based, Instance Models, Train-Test Splits: The Building Blocks of Machine Learning Explained – Day 3

In machine learning and deep learning, the concepts of Model vs Instance Models and Train-Test Split are closely intertwined. A model serves as the blueprint for learning patterns from data, while an instance model represents the specific realization of that blueprint after training. The train-test split, on the other hand, plays a critical role in the creation and evaluation of these instance models by dividing the dataset into subsets for training and testing. This blog post will delve into the relationship between these concepts,   first we explain model vs instance based and then we explain train- test spilt and...

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