SCHOLARIO ACADEMY

🟩 DAY 1 — Python & Backend Fundamentals

Goal: Build strong backend foundations

• Python overview for backend development
• Virtual environments & project structure
• REST API fundamentals
• Client–Server architecture

Task: Setup Python backend environment.

🟩 DAY 2 — Introduction to FastAPI

Goal: Create high-performance APIs

• Why FastAPI?
• Installing FastAPI & Uvicorn
• Path & query parameters
• Automatic Swagger & ReDoc docs

Task: Build your first FastAPI application.

🟦 DAY 3 — Request, Response & Validation

Goal: Handle API data properly

• Pydantic models
• Request body validation
• Response models
• Status codes & error handling

Task: Create validated CRUD endpoints.

🟦 DAY 4 — MongoDB Integration

Goal: Persist data using NoSQL

• MongoDB basics & schema design
• Connecting FastAPI with MongoDB
• Using Motor / PyMongo
• CRUD operations with MongoDB

Task: Store and retrieve data from MongoDB.

🟦 DAY 5 — Async Programming & Performance

Goal: Build scalable APIs

• Async vs sync in FastAPI
• Async database calls
• Background tasks
• Performance best practices

Task: Convert APIs to async.

🟦 DAY 6 — Authentication & Authorization

Goal: Secure backend APIs

• JWT authentication
• Password hashing
• Token-based authorization
• Role-based access control

Task: Implement secure login & protected routes.

🟦 DAY 7 — API Architecture & Best Practices

Goal: Write clean & maintainable code

• Project folder structure
• Service & repository pattern
• Environment variables
• Reusable components

Task: Refactor project using best practices.

🟦 DAY 8 — Testing & Error Handling

Goal: Build reliable APIs

• Exception handling
• Custom error responses
• API testing with Postman
• Basic unit testing concepts

Task: Test all APIs using Postman.

🟦 DAY 9 — Deployment & Production Readiness

Goal: Prepare APIs for real-world usage

• Production configurations
• Deployment overview (VPS / Cloud)
• Environment separation
• API security checklist

Task: Prepare backend for deployment.

🟦 DAY 10 — Mini Project Kickoff

Goal: Start real-world backend project

• Project requirement discussion
• Database & API design
• Initial FastAPI setup

Task: Create project base structure.

🟨 DAY 11 — Git & GitHub for Developers

Goal: Learn version control & collaboration

• Git basics (init, commit, branch)
• GitHub repositories
• Push, pull & merge workflows
• Hosting backend project on GitHub

Task: Push project to GitHub.

🟨 DAY 12 — Advanced API Concepts

Goal: Understand industry-level backend usage

• Third-party API integration
• Environment variables & secrets
• API security best practices
• Logging & monitoring basics

Task: Integrate an external API.

🟨 DAY 13–15 — Capstone Project Development

Goal: Build and finalize a complete backend system

• FastAPI backend with MongoDB
• Authentication & CRUD APIs
• Testing & optimization
• Documentation & cleanup
• Final project presentation

Outcome: Industry-ready backend project hosted on GitHub.

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🟩 DAY 1 — Python & ML Basics

Goal: Understand fundamentals

Topics:

• Python basics (variables, loops, functions)
• Numpy, Pandas basics
• ML workflow overview

Task: Write small code: load CSV, print first 5 rows.

🟩 DAY 2 — Data Handling + EDA Basics

Goal: Data cleaning & analysis

Topics:

• Handling missing values
• Data types
• Basic visualizations

Task: Plot scatter plot of price vs area.

🧠 ML Project: House Price Prediction (8 Days)

🟦 DAY 3 — Dataset Understanding

Goal: Explore dataset

• Download dataset (Kaggle: House Prices)
• Understand features
• Check correlations

Task: Find top 5 features correlated with price.

🟦 DAY 4 — Data Preprocessing

Goal: Clean the data

• Handle missing values
• Encode categorical features
• Feature scaling

Task: Prepare final X and y.

🟦 DAY 5 — Train/Test Split & Model Selection

Goal: Train first ML model

• Split data into train/test
• Apply Linear Regression

Task: Print accuracy (R² score).

🟦 DAY 6 — Model Improvement

Goal: Try better models

• RandomForestRegressor
• DecisionTreeRegressor
• XGBoost (optional)

Task: Compare model accuracies.

🟦 DAY 7 — Hyperparameter Tuning

Goal: Improve model using GridSearchCV

• Tune Random Forest parameters
• Find best estimator

Task: Save best model accuracy.

🟦 DAY 8 — Prediction System

Goal: Make a real prediction feature

• Build a script that takes input (area, rooms, age…)
• Returns predicted price

Task: Test with sample inputs.

🟦 DAY 9 — Model Saving

Goal: Save & load ML model

• Using pickle or joblib

Task: Save model.pkl and load it again to predict.

🟦 DAY 10 — Project Completion

Goal: Final summary + documentation

• Create README
• Add screenshots
• Write project explanation
• Final report format