Welcome to my GitHub repository for the InnoQuest Emerging Tech Bootcamp Cohort-1! I’m thrilled to be participating in this immersive bootcamp, designed to help learners like myself build skills and gain hands-on experience in Data Science, Machine Learning (ML), Deep Learning (DL), Computer Vision (CV), Natural Language Processing (NLP), MLOps, and AI Agents 🤖.
Throughout this journey, I’ll be completing assignments, projects, and hands-on exercises that will help me apply the concepts I learn to real-world problems. The bootcamp provides an opportunity to dive deep into these fields, work with industry-standard tools like Python, TensorFlow, PyTorch, and more, and develop practical applications of AI 💡.
The goal of my participation in this bootcamp is to:
- Build a Solid Foundation: Start with core concepts in Data Science and progressively explore more specialized topics such as Machine Learning, Deep Learning, Computer Vision, NLP, and AI Agents.
- Gain Hands-on Experience: Complete assignments and projects that allow me to work with real datasets and solve complex problems using machine learning and AI techniques.
- Apply Learning to Real-World Problems: Implement what I learn to build and deploy models, focusing on AI agents and their practical applications in different domains.
This repository will contain all the assignments, projects, and resources that I complete as part of the InnoQuest Emerging Tech Bootcamp Cohort-1. It will showcase my work and demonstrate how I’m applying the knowledge I’m gaining throughout the course.
In this repository, you will find:
- Assignments: Completed exercises and tasks from each module 📚.
- Projects: Real-world AI and data science projects that demonstrate my learning 🔧.
- Resources: Links to articles, tutorials, and other helpful materials I’ve used during the bootcamp 🌐.
This repository serves as both a personal record of my journey through the bootcamp and a valuable resource for anyone else looking to explore the emerging technologies in AI and data science.
In this module, I’m learning the fundamentals of Data Science with Python, using key libraries like Numpy and Pandas for data analysis 📊.
- Data Science with Python (Fundamentals)
- Numpy and Pandas
- Advanced Pandas
- Descriptive Statistics using Excel Sheets
- Data Analytics
- Pipeline Project (Continued)
- Using ChatGPT for Data Analysis
This module focuses on Machine Learning and Deep Learning, with hands-on work using different algorithms and frameworks 💻.
- ML Overview
- Regression
- Classification
- Jupyter Notebook / Colab / Kaggle
- Pytorch Session
- Neural Networks as Classifier
- Multiclass Classification
- Unsupervised Learning and Anomaly Detection
- Recommendation Systems
In this module, I’ll explore Computer Vision techniques, including image classification, object detection, and segmentation 🖼️.
- Introduction to Computer Vision (CV)
- Image Classification
- CV Architectures and their Usage
- Object Detection and Segmentation Tasks
- Generative AI and Multimodal Learning
- CV Models Deployment
This module dives into Natural Language Processing (NLP) and Large Language Models (LLM), with a focus on transformer architectures and pre-trained models 🧠.
- Introduction to NLP
- Dense Embedding
- Text Classification using CNN
- Sequence Models
- Large Language Models (LLMs) and Transformer Architecture
- Fine-Tuning Pre-Trained Models
- Retrieval-Augmented Generation (RAG) Pipelines
In the final module, I’ll learn about MLOps and its importance in optimizing and deploying machine learning models in production 🛠️.
- Optimizing Model Inference and Deployment
- Generative AI for Creative Tasks
- Engineering and Production
- MLOps and Scaling AI Solutions
- Containerizing AI Models with Docker and Kubernetes
As part of this bootcamp, I will be developing AI agents that leverage the latest Generative AI technologies. These AI agents will automate tasks, generate insights, and provide intelligent solutions across various fields, including:
- Data Science Agents: AI systems for automatic data analysis, processing, and insights generation using libraries like Pandas and Numpy 📊.
- Machine Learning Agents: AI agents capable of training and deploying predictive models, making data-driven decisions 🔍.
- Generative AI Agents: AI agents for content generation, including text, images, and creative media 🎨.
- Chatbot and NLP Agents: Using Large Language Models (LLMs) to build interactive chatbots that understand and generate natural language 💬.
- Multimodal AI Agents: AI agents that process and generate outputs from both textual and visual data, for tasks like image captioning and video content generation 🎥.
I will explore and create a variety of these AI agents, focusing on their real-world applications in solving problems efficiently and innovatively.
Throughout this bootcamp, I’ll be using the following tools, frameworks, and libraries to build projects and assignments:
- Programming Languages: Python, SQL
- Libraries: Pandas, Numpy, Scikit-learn, TensorFlow, PyTorch, OpenCV, Hugging Face 🤗
- Development Tools: Jupyter Notebook, Colab, Kaggle 🧑💻
- MLOps Tools: Docker, Kubernetes 🚢
- Cloud Platforms: AWS, Google Cloud ☁️
By the end of this bootcamp, I aim to have built a solid foundation in AI, Machine Learning, Deep Learning, Computer Vision, NLP, and MLOps, while gaining practical, hands-on experience with real-world projects. This repository will document my progress and showcase the skills I acquire throughout the course.
I look forward to continuing this learning journey and contributing to the field of AI and Machine Learning 🌟!
For any inquiries or to connect, feel free to reach out:
- LinkedIn: Engr. Hamesh Raj
- InnoVista Website: Innovista