Welcome fellas!
Pranay singh
Machine Learning Engineer
ABOUT ME
Machine Learning Engineer
I'm a Machine Learning Engineer. My interest lies in the depth of Machine Learning Algorithms, fundamental mathematics, and areas of Deep Learning. I am working on various things in the field of Computer Vision. My current projects deal with Image and Video Processing frameworks that work with surveillance and Recognition cameras. Skills that I am building currently are Object and Feature Detection, Segmentation and Classification. I love to follow the ongoing advancements and research in AI.
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I also love to travel, listen to music, and make acquaintances.
WORK EXPERIENCE
​Machine Learning Engineer - Spectral Tech Private Limited(KMG)
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Led research and development of Surveillance Software based on Video Processing with Object Detection.
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Significantly optimized solutions to decrease the False Detection rate and bring down the boot time and
processing speed of algorithms. -
Developed solutions for new problems such as identification of a person under a face mask.
November 2020 - Present
​Research & Development Engineer - Ampviv Healthcare Private Limited
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Responsible for analysis, implementation, development, performing preliminary testing and deployment of the product being developed by the company along with writing, editing and maintaining papers and reports of the work undertaken.
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Led the complete development of the Machine Learning framework used by the product and partly of the Image Processing work along with other team members under the guidance of the mentor.
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Led the iOS application development for deployment of our product.
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Research, study and implementation of various research papers and articles, engage with other developers in open-source communities and forums in order find solutions, curate ideas and problem solving related to the development of our product.
September 2020 - September 2021
EVERY MACHINE LEARNING MONOLOGUE EVER...
(..Not Interested ? Jump Right To The Projects Instead)
My Machine learning competencies include:
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Building efficient Machine Learning Pipeline
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Supervised Learning: Classification and Regression, KNN, Support Vector Machines, Decision Trees.
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Ensemble Learning: Random Forests, Bagging and Pasting
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Boosting Algorithms- AdaBoosting, Gradient Boosting
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Dimensionality Reduction Algorithms: PCA and T-SNE
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Deep Neural Networks: CNN, RNN, LSTM
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Transfer Learning
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Autoencoders
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Computer Vision: Image & Video Processing, Image Segmentation and Localization, Object detection, Feature Extraction, HOG, ORB, YOLO, RCNN, Detectron.
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Libraries: PyTorch, TensorFlow, Keras, Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn
I have an active interest in Deep Learning and Computer Vision Research and I'm still learning to add skills to my competence.
My favorite source of references for Machine Learning:
1. Hands-on Machine Learning using Scikit-Learn, Tensorflow & Keras by Aurelien Geron.
2. Deep Learning by Aaron Courville, Ian Goodfellow, and Yoshua Bengio.
3. www.kaggle.com
PROJECTS
My Projects revolve around applications of Deep Learning and Computer Vision that try to solve some of the interesting problems we have, along with those of advanced Machine Learning algorithms that generate interesting insights from the data, and produce reliable inferences.
AUTOMATIC IMAGE CAPTIONING
Generate Descriptive Captions For An Image
A CNN-RNN neural network architecture to automatically generate captions from images describing that image. The network consists of a pre-trained ResNet50 CNN encoder connected to an RNN decoder.
FACIAL KEYPOINTS DETECTION
Identify Distinguishing Keypoints On a face
A Facial Keypoints Detection model using a CNN that takes in any image with faces, predicts the location of 68 distinguishing Keypoints on each face, and marks them at their correct position on the face.
GENERATING TV SCRIPTS
Using RNN To Generate New Scripts For Seinfeld
Generated new TV scripts for Seinfeld using RNNs by training the model on Seinfeld dataset of scripts from 9 seasons. New "Fake" scripts were based on patterns it learned from data.
EMPLOYEE ATTRITION ANALYSIS
Predicting The Chances of Attrition
Built a model that predicts the chances of Attrition of an employee working at IBM. The model achieved 84% Precision on testing data using XGBoost.
DOG BREED CLASSIFIER
Predict Breed of a Dog
Algorithm that works on user supplied image. If a dog is detected, it estimates the breed of the dog amongst 133 breeds.
CREDIT CARD FRAUD DETECTION
Predicting Fraudulence on Highly Unbalanced Data
Built a Classifier to detect Fraud Credit Card Transactions trained over a dataset listing 284,807 transaction details of anonymous European cardholders. A Random Forest classifier achieved 90% Precision, 70% Recall, and 85% AUC score.
SUPERVISE LEARNING ALGORITHMS WITHOUT SCIKIT-LEARN
Implement Algorithms Using NumPy
Implemented following Supervised Learning algorithm using NumPy: Gradient Descent, KNN, Decision Tree Classifier to understand under the hood working of these basic Machine Learning Algorithms.
REAL TIME FACE RECOGNITION USING KNN AND OPENCV
Implementing KNN on Real Time Webcam Input
Constructed a dataset consisting of faces of my own friends in real-time using Haar Cascades Classifier, trained it using KNN and then tested the algorithm by running it against real-time test data in different lighting conditions.
DIMENSIONALITY REDUCTION AND DATA VISUALIZATION
Experimental Study of Reduction Algorithms
Comprehensive Analysis of Dimensionality Reduction methods, I practically tested different reduction methods like PCA, T-SNE, LLE and LDA to analyze their speed of compression and quality of cluster formation
COURSES
COMPUTER VISION
Udacity
Learn about Gradient Descent Algorithm, the idea behind it, break down the equation and implement the algorithm from scratch on Boston Housing dataset.
LET'S CONNECT!
Feel free to hit me up for any discussions, collaborations, recruitment, or feedback!
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+91 8882828173