Rajesh Idumalla's repositories
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.
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.
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.
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.
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.
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.
Spam-Email-using-NNET
Building Spam Email Classifier using NNET. Please read README.md for more info. Thanks
Spark-Applied-Data-Analysis-Vietnam-War
This project is about applying data analysis on Vietnam war data using Spark on google colab environment.
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.
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
Collaborative-Filtering-the-MovieLens-dataset
A simple movie recommendation system by collaborative filtering based on MovieLens dataset
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.
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.
llm-course
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
machine-learning-interview
Machine Learning Interviews from FAANG, Snapchat, LinkedIn. I have offers from Snapchat, Coupang, Stitchfix etc. Blog: mlengineer.io.
Machine-Learning-Interviews
This repo is meant to serve as a guide for Machine Learning/AI technical interviews.
nlp-papers
Collections of NLP Papers in my to-read list
recommenders
Best Practices on Recommendation Systems
Reinforcement-Learning
Focuses on the application of Deep Q-Learning on different OpenAI environments like CartPole, MsPacman, etc.