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CAPSTONE PROJECT – NLP Industrial safety NLP based Chabot: The database comes from one of the biggest industry in Brazil and in the world. It is an urgent need for industries/companies around the globe to understand why employees still suffer some injuries/accidents in plants. Sometimes they also die in such environment. Skills and Tools: GloVe, Classication, LSTM, Flask frame work, html5, css, js, bootstrap, autonlp, Dialogflow GCP,PIP installation,NLTK

COURSE PROJECTS

Sarcasm Detection -Natural Language Processing: The goal of this hands-on project is to analyse the headlines of the articles from news sources and detect whether they are sarcastic or not.Skills and Tools: GloVe, Classication, LSTM

Sentiment Analysis-Natural Language Processing: 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.Skills and Tools: RNN Word embedding LSTM Classification

Face Recognition Computer Vision: Face recognition deals with Computer Vision a discipline of Artificial Intelligence and uses techniques of image processing and deep learning. The objective of this project is to build a face recognition system, which includes building a face detector to locate the position of a face in an image and a face identification model to recognize whose face it is by matching it to the existing database of faces.Skills and Tools: Keras, CNN, Siamese Networks, Triplet loss

Face mask segmentation Computer Vision: Predict and apply masks over the faces within images using CNN and image recognition algorithms. In this hands-on project, the goal is to build a system, which includes building a face detector to locate the position of a face in an image and apply a segmentation mask on the face.Skills and Tools: Computer Vision, CNN, Transfer Learning, Object detection

Implementing a Image classification neural network to classify Street House View Numbers:Introduction to Neural Network and Deep Learning: SVHN is a real-world image dataset for developing object recognition algorithms with a 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. The objective of the project is to learn how to implement a simple image classification pipeline based on the k-Nearest Neighbour and a deep neural network.Skills and Tools: Neural Networks, Deep Learning, Keras, Image Recognition

Product Recommendation Systems: Recommendation Systems: This project involved building recommendation systems for Amazon products. A popularity-based model and a collaborative Filtering model were used and evaluated to recommend top-10 products for a user. Skills and Tools: Collaborative Filtering, Recommender Systems, Python

Predicting the Strength of high performance concrete: Featurization, Model Selection & Tuning: This project involved feature exploration and selection to predict the strength of high-performance concrete. Used Regression models like Decision tree regressors to find out the most important features and predict the strength. Cross-validation techniques and Grid search were used to tune the parameters for best model performance. Skills and Tools: Regression, Decision trees, feature engineering

Classifying silhouettes of vehicles Unsupervised Learning: Classified vehicles into different types based on silhouettes which may be viewed from many angles. Used PCA in order to reduce dimensionality and SVM for classification. Skills and Tools: Support Vector Machines, Principal Component Analysis, Classification

Diagnosing Parkinson's disease using Random Forests: Ensemble Techniques: This project involved using classification algorithms and Ensemble techniques to diagnose Parkinson’s Disease (PD) using the patient voice recording data. Various models were used including Naive Bayes, Logistic Regression, SVM, Decision Tree, Random Forest etc. and comparison of accuracy across these models was done to finalise the model for prediction. Skills and Tools: EDA, Logistic regression, Decision Trees

Identifying potential customers for loans: Supervised Learning: Identified potential loan customers for Thera Bank using classification techniques. Compared models built with Logistic Regression and KNN algorithm in order to select the best performing one. Skills and Tools: Logistic Regression, KNN, Classification

Health Insurance: Applied Statistics: This project used Hypothesis Testing and Visualization to leverage customer's health information like smoking habits, bmi, age, and gender for checking statistical evidence to make valuable decisions of insurance business like charges for health insurance. Skills and Tools: Hypothesis Testing, Data visualisation, statistical Inference