AI-ML-Project

These are all the projects I completed under my AI/ML scholarship by AWS.

AWS AI/ML Scholarship - Basic + Advanced Cohort

1- Nanodegree 1: AI Programming with Python

2- Nanodegree 2: Machine Learning Fundamentals

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Welcome to a series of diverse and challenging machine learning engineering projects, each designed to showcase skills in various domains. These projects cover a wide range of applications, from image classification and natural language processing to deploying scalable models on cloud platforms. As you progress through these projects, you'll demonstrate your ability to tackle real-world machine learning problems and deploy solutions effectively.

Project Titles:

1. Image Classification for a City Dog Show:

   • Description: Develop an image classifier to identify dog breeds. This project focuses on Python programming skills and provides a hands-on experience in training and deploying a deep neural network.
   • Tools and Tech Stack: Python, PyTorch, Command Line App
   • Skills: Deep Learning, Image Classification, Command Line App Development

2. Bike Sharing Demand Prediction with AutoGluon:

   • Description: Utilize AutoGluon to predict bike-sharing demand. This project demonstrates your ability to train models, optimize hyperparameters, and submit entries to a Kaggle competition.
   • Tools and Tech Stack: AutoGluon, Jupyter Notebook, Kaggle
   • Skills: Tabular Prediction, Kaggle Competition, Hyperparameter Tuning

3. Handwritten Digits Classifier with PyTorch:

   • Description: Build a PyTorch-based classifier for handwritten digits. This project involves data preprocessing, neural network development, and model evaluation.
   • Tools and Tech Stack: PyTorch, Jupyter Notebook
   • Skills: Deep Learning, Neural Network, Model Evaluation

4. Landmark Classification & Tagging for Social Media:

   • Description: Develop an image classification model to identify landmarks. This project covers CNNs, transfer learning, and app deployment.
   • Tools and Tech Stack: Convolutional Neural Network, Transfer Learning, AWS SageMaker
   • Skills: CNN, Transfer Learning, AWS Deployment

5. Build a ML Workflow For Scones Unlimited on Amazon SageMaker:

   • Description: Create a scalable image classification model for a scone-delivery company. This project involves AWS SageMaker, Lambda functions, and Step Functions to build a complete ML workflow.
   • Tools and Tech Stack: AWS SageMaker, Lambda Functions, Step Functions
   • Skills: AWS Integration, Model Deployment, Scalable ML Workflow