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Manager - Consulting, Tiger Analytics
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 fashion clothing by building a machine learning model and improving its performance in recognizing fashion clothing images. This work has practical applications in e-commerce and online advertising, such as identifying similar clothing from a large set of images, automatic inventory assessment in clothing stores, automatic segmentation of clothing for fashion trend prediction.
The dataset used for this project was the Fashion MNIST dataset that consists of 7000 images each, for 10 clothing or accessories categories such as shirts, trousers, bags, dresses, shoes etc. Firstly, features were extracted from the raw images or PCA applied images. A multi-layer perceptron was trained on the features and model performance was evaluated. The model performance was improved using different hyperparameter values, regularizers etc
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 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 interface 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 learnt in Natural Language Processing (NLP) and Dialog Management. It provided me with an 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.
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.