Duration:
400 hours (Combined total for all 8 projects)
 
Bundle Overview:
This comprehensive bundle offers an in-depth journey into data science, machine learning, and deployment. Starting from foundational projects in Python programming and data analysis, you'll progress through machine learning modeling, customer segmentation, and advanced deployment with APIs and dashboards. Designed to cover the complete data science lifecycle, this bundle is ideal for developing expertise in multiple areas, including predictive analytics, NLP, computer vision, and cloud deployment.
 
Each project builds on the previous, offering a cohesive learning experience that equips you with a well-rounded skillset in data science and deployment, while also helping you build a diverse project portfolio.
 
Projects Included in This Bundle:
Tic Tac Toe Game Development with Python
Build a fully functional Tic Tac Toe game, implementing core game logic using Python.
Key Focus: Python programming, game logic design, interactive command-line interfaces.
Data Analysis and Statistical Exploration
Analyze data and uncover insights with statistics and visualizations, ideal for strengthening your foundational analysis skills.
Key Focus: Data manipulation, exploratory data analysis (EDA), data visualization.
Supervised Machine Learning for Energy Consumption Prediction
Build a model to forecast energy consumption, focusing on feature selection, model tuning, and evaluation.
Key Focus: Regression analysis, feature engineering, model evaluation, predictive analytics.
Customer Segmentation Using Clustering
Apply clustering techniques to categorize customers based on their spending behavior, aiding targeted marketing.
Key Focus: Clustering analysis, customer segmentation, unsupervised learning.
Computer Vision with Image Classification
Develop an image classification model to recognize and categorize images, leveraging popular computer vision techniques.
Key Focus: Convolutional neural networks (CNNs), image preprocessing, model accuracy improvement.
Natural Language Processing for StackOverflow Tag Prediction
Train a model to predict tags for StackOverflow questions, focusing on NLP techniques and feature extraction.
Key Focus: Text preprocessing, NLP feature engineering, classifier training.
API Deployment with FastAPI and Azure
Create a RESTful API using FastAPI to serve your machine learning model, deploying it to Azure for real-time use.
Key Focus: API development, CI/CD with GitHub Actions, cloud deployment, Docker.
Streamlit Dashboard for Interactive Predictions
Develop an interactive dashboard with Streamlit, integrating a deployed API to provide real-time iris classification predictions.
Key Focus: Dashboard development, API integration, user-friendly UI.
 
Who Should Enroll:
Aspiring data scientists and machine learning enthusiasts seeking hands-on, real-world projects across diverse areas.
Professionals looking to broaden their expertise in data science, deployment, and model-serving applications.
Anyone interested in building an impressive portfolio covering Python programming, machine learning, cloud deployment, and interactive dashboards.
 
Why This Bundle?
With this bundle, you’ll gain practical skills by tackling projects in data analysis, machine learning, deployment, and dashboarding. Each project prepares you to handle real-world data science challenges, from data preprocessing and model evaluation to cloud deployment and API integration. You’ll finish the bundle with a well-rounded skillset and a portfolio that showcases your ability to deliver full-stack data science solutions.
 
**If you’re a student or find it difficult to afford this bundle, or if you have questions or need guidance during your learning journey, please don’t hesitate to reach out. I’m here to support your growth, and I’m open to making these resources accessible for those in need.
 
Tools & Technologies:
Programming Language: Python
Libraries & Frameworks: scikit-learn, FastAPI, Streamlit, MLflow, OpenCV, NLTK, spaCy
Cloud Platform: Azure (with alternatives like AWS or Google Cloud if preferred)
Data Visualization Tools: Matplotlib, Seaborn, Plotly
Version Control and CI/CD: Git, GitHub Actions
Containerization: Docker
 
Deliverables:
End-to-End Project Notebooks: Detailed code for each project, covering preprocessing, model training, and evaluation.
Deployed Cloud Applications: Fully functional APIs and dashboards accessible via the cloud.
Documentation: Comprehensive guides for each project, including setup, methodology, and insights.
Final Reports: Summaries of findings, model performance, and areas for improvement.