Pinned Repositories
contactmanager
My first react contact manager application
Ensemble-Techniques
The file "Data - Parkinsons" contains the 195 Parkinson's Disease patients Data. Data includes PD patients characteristic vocal features voice recordings. Goal is to classify the patients into the respective labels using the attributes from their voice recordings
Face-Mask-Segmentation
In this hands-on project, the goal is to build a Face Mask Segmentation model which includes building a face detector to locate the position of a face in an image
Face-Recognition
In this project, the goal is to build a face identification model to recognize faces.
Futurization-and-Model-Tuning
Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients. These ingredients include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate. Objective - Modeling of strength of high performance concrete using Machine Learning
Mutual-fund-rating-prediction
The goal of this to predict rating of a mutual fund. In order to help investors decide on which mutual fund to pick for an investment, the task is to build a model that can predict the rating of a mutual fund.
Neural-Networks-SVHN
SVHN is a real-world image dataset for developing machine learning and object recognition algorithms with minimal requirement on data formatting but comes from a significantly harder, unsolved, real world problem (recognizing digits and numbers in natural scene images). SVHN is obtained from house numbers in Google Street View images.
NLP-Sarcasm-Detection
Past studies in Sarcasm Detection mostly make use of Twitter datasets collected using hashtag based supervision but such datasets are noisy in terms of labels and language. Furthermore, many tweets are replies to other tweets and detecting sarcasm in these requires the availability of contextual tweets. In this hands-on project, the goal is to build a model to detect whether a sentence is sarcastic or not, using Bidirectional LSTMs.
NLP-Sentiment-Classification-Sequential-Models
The objective of this project is to build a text classification model that analyses the customer's sentiments based on their reviews in the IMDB database. The model uses a complex deep learning model to build an embedding layer followed by a classification algorithm to analyze the sentiment of the customers.
Pneumonia-Detection
The goal is to build a pneumonia detection system, to locate the position of inflammation in an image. Here we built an algorithm to detect a visual signal for pneumonia in medical images using CNN through the bounding boxes.
amol-matkar's Repositories
amol-matkar/Mutual-fund-rating-prediction
The goal of this to predict rating of a mutual fund. In order to help investors decide on which mutual fund to pick for an investment, the task is to build a model that can predict the rating of a mutual fund.
amol-matkar/Pneumonia-Detection
The goal is to build a pneumonia detection system, to locate the position of inflammation in an image. Here we built an algorithm to detect a visual signal for pneumonia in medical images using CNN through the bounding boxes.
amol-matkar/contactmanager
My first react contact manager application
amol-matkar/Ensemble-Techniques
The file "Data - Parkinsons" contains the 195 Parkinson's Disease patients Data. Data includes PD patients characteristic vocal features voice recordings. Goal is to classify the patients into the respective labels using the attributes from their voice recordings
amol-matkar/Face-Mask-Segmentation
In this hands-on project, the goal is to build a Face Mask Segmentation model which includes building a face detector to locate the position of a face in an image
amol-matkar/Face-Recognition
In this project, the goal is to build a face identification model to recognize faces.
amol-matkar/Futurization-and-Model-Tuning
Concrete is the most important material in civil engineering. The concrete compressive strength is a highly nonlinear function of age and ingredients. These ingredients include cement, blast furnace slag, fly ash, water, superplasticizer, coarse aggregate, and fine aggregate. Objective - Modeling of strength of high performance concrete using Machine Learning
amol-matkar/Neural-Networks-SVHN
SVHN is a real-world image dataset for developing machine learning and object recognition algorithms with minimal requirement on data formatting but comes from a significantly harder, unsolved, real world problem (recognizing digits and numbers in natural scene images). SVHN is obtained from house numbers in Google Street View images.
amol-matkar/NLP-Sarcasm-Detection
Past studies in Sarcasm Detection mostly make use of Twitter datasets collected using hashtag based supervision but such datasets are noisy in terms of labels and language. Furthermore, many tweets are replies to other tweets and detecting sarcasm in these requires the availability of contextual tweets. In this hands-on project, the goal is to build a model to detect whether a sentence is sarcastic or not, using Bidirectional LSTMs.
amol-matkar/NLP-Sentiment-Classification-Sequential-Models
The objective of this project is to build a text classification model that analyses the customer's sentiments based on their reviews in the IMDB database. The model uses a complex deep learning model to build an embedding layer followed by a classification algorithm to analyze the sentiment of the customers.
amol-matkar/react-typescript-webpack
Setup react with webpack
amol-matkar/Recommendation-System
Online E-commerce websites like Amazon, Flipkart uses different recommendation models to provide different suggestions to different users. Amazon currently uses item-to-item collaborative filtering, which scales to massive data sets and produces high-quality recommendations in real-time. Build a recommendation system to recommend products to customers based on the their previous ratings for other products.
amol-matkar/SpringBoot
for Java Microservices Course
amol-matkar/Supervised-Learning---Bank-Personal-Loan-Modelling
This case is about a bank whose management wants to explore ways of converting its liability customers to personal loan customers (while retaining them as depositors). A campaign that the bank ran last year for liability customers showed a healthy conversion rate of over 9% success. This has encouraged the retail marketing department to devise campaigns with better target marketing to increase the success ratio with minimal budget.
amol-matkar/Unsupervised-Learning
The data contains features extracted from the silhouette of vehicles in different angles. Four "Corgie" model vehicles were used for the experiment: a double decker bus, Cheverolet van, Saab 9000 and an Opel Manta 400 cars. This particular combination of vehicles was chosen with the expectation that the bus, van and either one of the cars would be readily distinguishable, but it would be more difficult to distinguish between the cars.