rajeshidumalla

Bloom-Filter

Building a Bloom Filter on English dictionary words

Cat-Recognition-using-Logistic-Regression-with-a-Neural-Network-mindset

In this Cat recognition project I am building the general architecture of a learning algorithm, including: Initializing parameters, Calculating the cost function and its gradient, Using an optimization algorithm (gradient descent), Gather all three functions above into a main model function, in the right order.

Classification-tree-to-the-housing-data-using-the-R-package-rpart

This project about the fitting a classification tree to the housing data sing R package rapart.

Football-Kicks-Prediction-using-Deep-Learning

For this project, I am going to recommend positions where France's goal keeper should kick the ball so that the French team's players can then hit it with their head using deep learning regularisation and dropout methods.

GBM-Classification-on-Spam-Email

This project about the GBM classification model on spam email data set and model optimisation.

Image-Classification

An application to recognise the cat images with the Accuracy of 80 %. From this project, I've learn how to: Build the general architecture of a learning algorithm, including: Initializing parameters Calculating the cost function and its gradient Using an optimization algorithm (gradient descent) Gather all three functions above into a main model function, in the right order. Build and apply a deep neural network to supervised learning.

K-Means-PCA-the-Breast-Cancer-Wisconsin-dataset

K-means is a least-squares optimization problem, so is PCA. k-means tries to find the least-squares partition of the data. PCA finds the least-squares cluster membership vector.

M2-Kernel-Methods

Kernel Versions of various machine learning algorithms. The following algorithms are checked by applying the kernel trick: PCA • KMeans • LASVM • One class SVM • Passive aggressive online algorithm

Recognizing-a-sign-with-Deep-Learning-Tensorflow

My goal is to build an algorithm capable of recognizing a sign with high accuracy. To do so, I am going to build a tensorflow model with deep learning methods.

Speech-recognition-model-development---NLP

Using Speech Commands Dataset to build an algorithm that understands simple spoken commands. By improving the recognition accuracy of open-sourced voice interface tools, we can improve product effectiveness and their accessibility.

rajeshidumalla/K-Means-PCA-the-Breast-Cancer-Wisconsin-dataset

K-means is a least-squares optimization problem, so is PCA. k-means tries to find the least-squares partition of the data. PCA finds the least-squares cluster membership vector.

rajeshidumalla/Bloom-Filter

Building a Bloom Filter on English dictionary words

rajeshidumalla/Football-Kicks-Prediction-using-Deep-Learning

For this project, I am going to recommend positions where France's goal keeper should kick the ball so that the French team's players can then hit it with their head using deep learning regularisation and dropout methods.

rajeshidumalla/Cat-Recognition-using-Logistic-Regression-with-a-Neural-Network-mindset

In this Cat recognition project I am building the general architecture of a learning algorithm, including: Initializing parameters, Calculating the cost function and its gradient, Using an optimization algorithm (gradient descent), Gather all three functions above into a main model function, in the right order.

rajeshidumalla/Classification-tree-to-the-housing-data-using-the-R-package-rpart

This project about the fitting a classification tree to the housing data sing R package rapart.

rajeshidumalla/Classification-with-one-hidden-layer

Implementing a 2-class classification neural network with a single hidden layer. Using units with a non-linear activation function such as tanh. Computing the cross entropy loss. Implementing forward and backward propagation.

rajeshidumalla/Finding-Frequent-Pattern-Mining-Grocery-shopping-dataset-using-Spark

Building a machine learning program that can find most frequently buying products in a grocery store.

rajeshidumalla/GBM-Classification-on-Spam-Email

This project about the GBM classification model on spam email data set and model optimisation.

rajeshidumalla/Image-Classification

An application to recognise the cat images with the Accuracy of 80 %. From this project, I've learn how to: Build the general architecture of a learning algorithm, including: Initializing parameters Calculating the cost function and its gradient Using an optimization algorithm (gradient descent) Gather all three functions above into a main model function, in the right order. Build and apply a deep neural network to supervised learning.

rajeshidumalla/node2vec

Building node2vec algorithm

rajeshidumalla/PageRank

Building PageRank algorithm on Web Graph around Stanford.edu using NetworkX python library

rajeshidumalla/Spam-Email-using-NNET

Building Spam Email Classifier using NNET. Please read README.md for more info. Thanks

rajeshidumalla/Speech-recognition-model-development---NLP

Using Speech Commands Dataset to build an algorithm that understands simple spoken commands. By improving the recognition accuracy of open-sourced voice interface tools, we can improve product effectiveness and their accessibility.

rajeshidumalla/M2-Kernel-Methods

Kernel Versions of various machine learning algorithms. The following algorithms are checked by applying the kernel trick: PCA • KMeans • LASVM • One class SVM • Passive aggressive online algorithm

rajeshidumalla/Recognizing-a-sign-with-Deep-Learning-Tensorflow

My goal is to build an algorithm capable of recognizing a sign with high accuracy. To do so, I am going to build a tensorflow model with deep learning methods.

rajeshidumalla/Character-level-language-model---Dinosaurus-land

To giving names to these dinosaurs using character level language. Major tasks of this project are: How to store text data for processing using an RNN, How to synthesize data by sampling predictions at each time step and passing it to the next RNN-cell unit, How to build a character-level text generation recurrent neural network, Why clipping the gradients is important

rajeshidumalla/Computer-Vision-CV-Project

rajeshidumalla/Decision-Trees-on-Spark-Pridicting-Hand-Written-Numbers

Hello there! In this repository I will explain how to predict hand written digits using Spark Machine Learning decision tree classifier algorithm which will produce 88% accurate predictions at the depth of 15.

rajeshidumalla/Improvise-a-Jazz-Solo-with-an-LSTM-Network

Implementing a model that uses an LSTM to generate jazz music and will even be able to listen to our own music at the end of this project.

rajeshidumalla/Linear-regression-model-for-Melbourne-housing-price-analysis

rajeshidumalla/llm-course

Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.

rajeshidumalla/machine-learning-interview

Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.

rajeshidumalla/Machine-Learning-Interviews

This repo is meant to serve as a guide for Machine Learning/AI technical interviews.

rajeshidumalla/MySQL-Python-for-Data-Analysis

rajeshidumalla/nlp-papers

Collections of NLP Papers in my to-read list

rajeshidumalla/rajeshidumalla

rajeshidumalla/Recommendation-Systems-RecSys-

Personal projects - Recommendation Systems (RecSys)

rajeshidumalla/recommenders

Best Practices on Recommendation Systems

rajeshidumalla/Reinforcement-Learning

Focuses on the application of Deep Q-Learning on different OpenAI environments like CartPole, MsPacman, etc.

rajeshidumalla/Twitter-Sentiment-Analysis-and-Tweet-Extraction-