Share
Assistant Manager, Citibank
The goal of this hackathon was to create and deploy a food ordering application, using speech recognition AI techniques. It was exciting to train a model using features extracted from voice samples and to integrate a machine learning classifier that could identify the correct food items and servings. It provided an excellent hands-on experience in coding an end-to-end solution, starting from pre processing the data, to training and testing the model and finally deploying the model. The final user interfaces accurately identified the food item from a voice command and confirmed the order as a response.
The goal of this hackathon was to build a conversational bot to interact with the user and achieve the desired outcome, such as booking a doctor’s appointment/ providing a movie recommendation/ assisting in the purchase of a suitable mobile phone, based on the user’s requirements. Two approaches were used in achieving the task - (i) Amazon Alexa API-based bot and (ii) Python coding-based bot. It was very interesting and helped to strengthen my understanding and skills learned in Natural Language Processing (NLP) and Dialog Management. It provided me with in-depth experience in utterance usage, intent classification, entity design, and conversational session creation.
In this Hackathon, we implemented AI-based Face and Expression recognition to build an anti-face spoofing application. The goal was to unlock a mobile application based on the accurate identification of a user’s face and expression, as a safety feature. Firstly, a Siamese Network-based representation was obtained from a large dataset of face images and used for detecting Face Similarity. Then we built a small dataset of our team members’ face images and expressions and applied a deep learning-based CNN model to perform Face and Expression Recognition. It was an amazing experience to unlock the mobile app with our Face and Expression Recognition model, at the end of the project.
The goal of this Mini-Hackathon was to predict whether the literacy rate was high/ medium/ low in the different districts of India. A real-world government-released dataset consisting of district-wise demographics, enrollments, and teacher indicator data was used for this project. Extensive data-preprocessing was required to clean the data, remove missing data and values, transform the data by normalization, select uncorrelated features and integrate the data into a single dataframe. After the data was pre-processed, a machine learning classifier was trained to obtain a model that could accurately predict the literacy rate (high/ medium/ low). Through this work, we were able to gain excellent experience in all aspects of data pre-processing, model building, and prediction.
The goal of this Mini-Hackathon was to classify the question based on the subcategory (like Algebra, Time and Distance, Permutations and Combinations, etc..) it belongs to. Talentsprint's aptitude questions which contain more than 20K questions data were used for this project. This project gave me an insight into using the Beautiful Soup and NLTK libraries. We have extensively used these libraries for data pre-processing. A machine learning model was trained on pre-processed data wherein given a question as input to the model it predicts the respective category of the question belongs to with >90% accuracy.
The goal of this Mini-Hackathon was to identify the unique writing styles of different authors using a machine learning-based approach. A manual, stylometric analysis using hand-crafted features was also used for comparison. This project helped me to understand the usefulness of machine learning based text classification for automated author prediction tasks. A machine learning model was trained on textual data with corresponding author labels, from the Gutenburg database. Bag of Words and Word2Vec based approaches were used for feature representation and analysis. Text classification and author prediction were achieved with >90% accuracy.
The goal of this Mini-Hackathon was to develop a clustering (unsupervised learning) based tool to identify academic groups working in specific research areas. It was a novel methodology that helped to find the most relevant academic labs for graduate research students, based on their research interests and goals. Using a large, real-world dataset of biomedical research topics, abstracts, research investigators, and their funding records, we performed NLP and k-Means Clustering to obtain research area-based academic group clusters. This exploratory data mining approach helped us to identify research groups working on similar research topics.
The objective of this Mini-Hackathon was to classify images of dogs and cats, after image pre processing and transformation. This was useful in learning image-based pre-processing techniques and achieving image classification using neural networks. A Pytorch framework was used to build and train a Convolutional Neural Network (CNN) model on a dataset of >22000 images of cats and dogs. Importantly, image classification was achieved with the CNN model, and accuracy of >90% was obtained.
The goal of this Mini-Hackathon was to obtain the sales forecast for a big retail store. Such a forecasting model would be very useful for the retailer in planning the budgets and investments and minimizing revenue losses. This was presented to us as a Kaggle competition.The data consisted of a real-world dataset that included the monthly sales of the store from the past 5 years. We completed the competition successfully by building a time series-based machine learning model that accurately forecasted the monthly and product-wise sales for the retail store.
The objective of this Mini-Hackathon was to classify more than 15000 images of buildings, forests, glaciers, mountains, sea, and streets that are invariant to the transformations and can recognize the varied images. Pre-trained models like Alexnet, VGG16, and Resnet50 were fine-tuned to improve the learning of the algorithm, and accuracy of >90% was obtained.
Advanced Certification in AI/ML is a 6 months in-depth and comprehensive program. It enables working professionals from around the world to build AI/ML expertise from India’s top Machine Learning Lab at IIIT Hyderabad. The program is led by collaborative faculty from academia, industry and global bluechip institutions. The unique 5-step learning process of masterclass lectures, hands-on labs, mentorship, hackathons, and workshops ensure fast-track learning.