This repository contains all the Lab and Assignments from Andrew NG Machine Learning Specialization Course on Coursera.

About A classification machine learning problem for predicting customers churn from the company based on customers who left within the last month labeled by 'yes' or 'no'

Our Project generates Monet-style images using a Generative Adversarial Network (GAN) consisting of a generator and discriminator neural network. Also, It contains some additional features like Chatbot which tells you about what actually Monet-style images are, along with feature of Object Detection for generating its images using GAN architecture.

Solution to freeCodeCamp.org Cat or Dog Image Classifier with Google Colaboratory

MACHINE LEARNING ALGORITHM MINDMAP

A pure implemention of BackPropagating artificial neutral net

reasrech on the best Machine Learning IDS model

Generating Shakespearean Text with LSTM Neural Networks: This project uses LSTM networks to generate text in the style of William Shakespeare. It explores the intersection of literature and AI by mimicking the rich linguistic nuances and poetic depth of Shakespearean prose and poetry.

Neural network implementation of XOR using PyTorch Lightning. Includes detailed explanations, troubleshooting tips, and discussions on real-world applications of neural networks.

This repository contains implementations of all Deep Learning Algorithms from scratch in Python. Mathematics required for DL and many projects have also been included. It also has practical tutorials on Generative AI.

Alphabet Soup Charity Analysis (Deep learning model), to predict the success of charity funding applications for the Alphabet Soup organization.

This is an assignment from my Machine Learning for Mechanical Engineers course that demonstrates an understanding in binary classification using neural networks with scikit-learn and TensorFlow.

🤖📖 Application that compares most known ML algorithms and generates plots and markdown files.

A Use Case of Neural Networks, classification of images: dogs and cats.

This repository contains multiple projects of my Bharat Intern Internship

Used a pre-trained model, ensemble duonet, to detect the type of emotion displayed by the image selected at random from a dataset consisting of 35,000 photographs. Provided a confusion matrix for error detection showing an accuracy of 74%.

Using TensorFlow and deep learning neural networks to analyze and classify the success outcome of charitable organizations.

Implementing Neural Network from scratch (MLP and CNN), purely in numpy with optimizers

A simple Neural Network model to detect Breast Cancer.

This project performs object recognition using CIFAR-10 and CNN/Random Forest models in Python. It preprocesses data, trains models, evaluates performance and compares results with the goal of high accuracy and feature importance understanding. The final output includes a comparison of the models and insights.