About

Siddhant Sanjeev is a highly skilled Machine Learning and Artificial Intelligence expert with 5.19 years of relevant experience in the field. He has implemented several algorithms and research papers and worked with various business stakeholders across different units. Currently, he is working as a Software Engineer, Machine Learning at Google. Siddhant has a strong educational background with three Master's degrees in Data Science from Indian Institute of Management, Calcutta, IIT, and Indian Statistical Institute, Kolkata. He also holds a Bachelor of Engineering in Computer Science from Delhi University and a Bachelor of Engineering in Computer Engineering from NSIT. Siddhant has worked with several renowned companies such as Goldman Sachs, PricewaterhouseCoopers, Deloitte, Lenskart, McAfee, and Intel. He has expertise in programming languages such as C, C++, Python, R, SQL, Scala, and more. He has also worked with tools like Tensorflow, Keras, Numpy, Pandas, Matplotlib, Scikit-learn, Tableau, Excel, Powerpoint, Pyspark, Spark, Hive, and HBase. Siddhant's expertise in Artificial Intelligence includes Artificial Neural Networks, Feedforward Neural Networks, Convolutional Neural Networks, LeNet, AlexNet, VGG, Inception, ResNet, Object Detection, R-CNN, Fast R-CNN, Faster R-CNN, YOLO, SSD, and Recurrent Neural Networks. His proficiency in Machine Learning includes Natural Language Processing, SVM, Linear Regression, Logistic Regression, Neural Networks, Decision Trees, Random Forest, k-Means, Deep Leaning, Adaboost, Gradient Boosting, and xgboost. Siddhant's skills in statistics include Distributions, Normal Distribution, Confidence Interval, Hypothesis Testing, Correlation, and Multivariate Statistics. He is also proficient in Linear Algebra concepts like Eigen Values, Principal Component Analysis, Singular Value Decomposition, and LU factorization. Siddhant has worked as an Applied Data Scientist at Tookitaki and a Senior Data Scientist at Ittiam Systems Pvt Ltd. At Ittiam Systems, he developed models and algorithms to assist retail stores for better understanding of their customers. He also worked on several projects such as training a CNN classifier to detect whether a person in the image view wears a face mask or not, detecting people occurring in image frames using bounding box regression from object detection, and