ML Blogs

Linear Regression With One Variable

Linear Regression with one variable is a statistical method used for predicting the relationship between a dependent variable and a single independent variable. It assumes a linear relationship, aiming to find the best-fit line that minimizes the sum of squared differences between actual and predicted values.

Linear Regression With Multiple Variable

Linear Regression with Multiple Variables extends the concept to predict a dependent variable based on two or more independent variables. It involves finding a hyperplane that best fits the data in a multidimensional space, aiming to minimize the difference between observed and predicted outcomes across multiple input features.

Polynomial Regression

Polynomial Regression is a regression technique that models the relationship between a dependent variable and one or more independent variables by fitting a polynomial equation. It expands on linear regression by introducing higher-degree polynomial terms, allowing for a more flexible curve to capture non-linear patterns in the data.

Linear Regression For Classification

Linear Regression isn’t suitable for classification due to its inability to predict probabilities and handle categorical outcomes; Logistic Regression addresses these issues by modeling probabilities and accommodating binary classification tasks effectively.

Logistic Regression

Logistic regression is a statistical tool for understanding the probability of an event occurring based on input variables. It estimates the likelihood using a logistic curve, making it valuable for classification tasks like predicting outcomes or determining categories.

Basic Tensorflow

The Tensorflow library integrates neurons for linear and logistic regressions, demonstrating the library’s capabilities in implementing fundamental components of neural networks efficiently.

Softmax Function

Basic Softmax Function along with an example.

Simple Neural Network

I have created a basic neural network with both Tensorflow and Numpy. Finally, I had compared the results.

Decision Trees

Decision trees are a machine learning algorithm that models decisions through a tree-like structure. Each node represents a decision based on a specific feature, and branches lead to possible outcomes. They’re used for classification and regression tasks.

Handwritten Digit Recognition

Handwritten digit classification with neural networks involves training a model to recognize digits from images. Using layers of interconnected nodes, the network learns to map pixel values to digit classes through training. This application is crucial for tasks like optical character recognition and digitizing handwritten documents.

K Means

KMeans is a clustering algorithm that partitions a dataset into K distinct, non-overlapping subsets (clusters) based on similarity patterns within the data. It minimizes the sum of squared distances between data points and their assigned cluster centroids.

Principal Component Analysis ( P.C.A. )

PCA reduces data dimensions by finding key patterns through orthogonal axes (principal components), simplifying complexity while retaining essential information.

Anomaly Detection

Anomaly Detection identifies unusual patterns or outliers in data, crucial for detecting deviations from the norm and highlighting potential irregularities or abnormalities.

State Action Value Function

The state-action value function, commonly denoted as Q(s,a), represents the expected cumulative rewards of taking action a in state s and following a particular policy thereafter. It is a fundamental concept in reinforcement learning, helping agents evaluate and select actions based on their potential long-term outcomes in a given environment.

Content Filtering Recommender System

Content-based filtering in recommender systems involves recommending items to users based on the characteristics of the items and the preferences expressed by the user. It assesses the content of items and user preferences to make personalized recommendations, offering suggestions similar to those the user has shown interest in.