Manoj Dakuwa

I have my post graduation in artificial intelligence and machine learning with 7+ years experience. completed my graduation in information technology. I am a Data Scientist with Full stack and mean stack expertise. I have experience in data analytics, EDA, CNN, pandas, numpy, Machine Learning, algorithm, Regression, RAG, GAN, Attention, Transformer, LangChain, llama etc. At frontend I know angular, html, CSS and in backend I have experience of ,C#, net Core, NodeJS, java script, typescript. I have 6+ experience in Full Stack / Mean Stack Development. 2+ years in Team Management. 4+ in AWS

• Generative AI
• Big data
• Artificial intelligence and machine learning
• Statistics for Data Science
• AWS Sagemaker
• AWS for Beginner

• AI WorkShop, This one-day introductory workshop will provide participants with a hands-on overview of the basics concepts of artificial intelligence. The workshop will cover what AI is, its history and applications. Participants will learn about different types of AI such as machine learning, deep learning and natural language processing. They will get an introduction to the different techniques used in AI like neural networks, computer vision and recommender systems. The workshop will be highly practical with several demonstration sessions. Participants will get a chance to experiment with real AI tools and algorithms. They will build simple predictive models using a machine learning library. They will also work on sample computer vision and natural language processing tasks. By the end of the workshop, participants will have basic understanding of the AI field and how its techniques are being applied across various domains.
• CAR DETECTION, This project attempts to use computer vision techniques to automate supervision and generate suitable action triggers based on predicted occurrences from images of interest. The emphasis is on recognizing cars in photos and collecting pertinent information such as make, model, colour, and license plate numbers. The ResNet model built for predicting both the bounding boxes & classification of car image data shows promising results. With an accuracy of 0.58, the model demonstrates its ability to classify car images accurately. The use of mean squared error loss for bounding box regression & categorical cross-entropy loss for classification allows the model to effectively optimize these two tasks simultaneously. The choice of Adam optimizer with a learning rate of 1e-3 provides efficient updates to the model's parameters during training.

Total Experience: 7+ years

• Employee Of the month - Deloitte