Within this repository, you will find a comprehensive assortment of algorithms and implementations pertaining to the field of machine learning. These materials encompass a diverse range of topics, with particular emphasis on the following areas:
Linear Regression Algorithm for a Single Feature Variable: This algorithm focuses on the estimation of relationships between a single independent variable and a dependent variable through linear regression techniques.
Linear Regression Algorithm for Multiple Feature Variables: Expanding upon the previous algorithm, this implementation incorporates multiple independent variables to model the relationship with a dependent variable.
Cost Function: The repository also provides an in-depth exploration of cost functions, which serve as essential tools for evaluating the accuracy and performance of machine learning models.
Gradient Descent Algorithm: A core optimization algorithm in machine learning, gradient descent is extensively covered in this repository. You will find various implementations and discussions on its application to minimize cost functions and optimize model parameters.
Normal Equations: This section delves into the utilization of normal equations for obtaining closed-form solutions in linear regression problems, providing an alternative approach to gradient descent.
Vectorizations: The repository offers insights into the use of vectorized computations, enabling efficient and accelerated numerical operations, particularly when dealing with large datasets.
NumPy Library in Python: An indispensable library for scientific computing in Python, NumPy is extensively employed in the implementations provided within this repository. It offers a wide array of numerical operations and data manipulation tools.
Learning Rate: Within the context of gradient descent, the significance of learning rate in determining the convergence and optimization efficiency of machine learning models is thoroughly discussed.
Feature Scaling: This repository emphasizes the importance of feature scaling techniques to normalize and standardize input variables, thereby enhancing the performance and stability of machine learning models.
Polynomial Regression: The repository explores the application of polynomial regression, a technique that extends linear regression models by introducing polynomial terms, enabling more complex relationships to be captured.
Logistic Regression and Classification: The algorithms and implementations encompass logistic regression, a popular method for binary classification, as well as broader discussions on classification techniques in general.
Addressing Overfitting: Overfitting, a common challenge in machine learning, is addressed within this repository, offering strategies and techniques to mitigate its adverse effects on model performance.
By perusing this repository, you will gain access to a practical implementations of these machine learning concepts.