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 Descent vs. Normal Equation Step-by-Step Explanation of the Normal Equation Step 1: Add Column of Ones Step 2: Transpose of X (XT) Step 3: Matrix Multiplication (XTX) Step 4: Matrix Multiplication (XTy) Step 5: Inverse of XTX ((XTX)-1) Step 6: Final Multiplication to Get θ Why the Normal Equation Works Without Gradient Descent Advantages of Using Matrices Conclusion Introduction to Linear Regression Linear regression aims to fit a line to a dataset, predicting a target variable $y$ based on input features $x$. The model is defined as: $$ y = \theta_0 + \theta_1 x $$ For multiple features, it generalizes...

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Regression & Classification with MNIST. _ day 4

  A Comprehensive Guide to Machine Learning: Regression and Classification with the MNIST Dataset Introduction to Supervised Learning: Regression and Classification In the realm of machine learning, supervised learning involves training a model on a labeled dataset, which means the dataset includes both input data and the corresponding output labels. Supervised learning tasks can be broadly categorized into two types: regression and classification.     Regression tasks aim to predict continuous numerical values. For example, predicting house prices based on various features such as location, size, and number of bedrooms. The output is a continuous value that can range over an infinite set of possible values. Common regression algorithms include linear regression, decision trees, and support vector regression.     Classification, on the other hand, deals with predicting discrete categorical values. The goal is to assign input data to one of several predefined classes. For instance, classifying emails as either spam or not spam, or recognizing handwritten digits as one of the digits from 0 to 9. The output is a discrete value representing the class label. Popular classification algorithms include logistic regression, support vector machines, decision trees, and neural networks. The MNIST Dataset: A Benchmark for Classification The MNIST...

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