75-Day Python Developer Roadmap 🚀

This roadmap is designed to guide you through becoming a proficient Python developer in 75 days. It covers foundational Python, full-stack development, network programming, advanced OOP, machine learning (ML), artificial intelligence (AI), and hosting projects. Each phase includes tasks and real-world projects for practical learning.

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📅 Roadmap Breakdown

Phase 1: Foundations of Python (Days 1–20)

Day	Topics	Details	Tasks/Exercises
1	Python Basics	- Install Python, IDE setup (VS Code, PyCharm). - Syntax, variables, data types. - print() and input().	- Write a "Hello World" program. - Experiment with variables of different data types.
2	Operators and Expressions	- Arithmetic, logical, comparison operators. - Expressions and operator precedence.	- Create a calculator app.
3	Strings and Operations	- String slicing, concatenation. - String methods like .split(), .join().	- Write a program to reverse a string and check for palindromes.
4-5	Control Flow	- Conditional statements (if, elif, else). - for, while, and nested loops.	- Create a number guessing game. - Print prime numbers in a range.
6-7	Lists and Tuples	- List operations: append, remove, slicing. - Tuples and their immutability.	- Build a program to find the largest number in a list.
8-9	Sets and Dictionaries	- Set operations: union, intersection. - Dictionary methods (get, keys, values).	- Create a phone book app using dictionaries.
10	Functions and Scope	- Function declaration, arguments, return values. - Global and local scope.	- Write a function to calculate Fibonacci series recursively.
11-12	File Handling Basics	- Reading and writing text files. - Using os and shutil for file tasks.	- Write a script to log user activities into a file.
13-14	JSON and CSV Files	- Parsing and writing JSON. - Reading and writing CSV files using csv module.	- Create a program to parse user details from a CSV file.
15	Modules and Packages	- Built-in modules (math, random, etc.). - Custom module creation.	- Build a custom math utility module.
16-17	Error Handling	- try-except blocks. - Custom exceptions.	- Write a banking app with error handling for invalid transactions.
18-20	Beginner Project	- Create a basic text-based application (e.g., To-Do List Manager).	- Host the project on GitHub and share the link.

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Phase 2: Object-Oriented Programming (Days 21–30)

Day	Topics	Details	Tasks/Exercises
21-22	Classes and Objects	- Creating classes and objects. - Attributes and methods. - Using self.	- Model a real-world entity like a Car or Student.
23-24	Advanced OOP Concepts	- Inheritance, polymorphism. - Encapsulation, abstraction.	- Build a class hierarchy for shapes (e.g., Circle, Square).
25	Dunder Methods	- Special methods like __init__, __str__, __repr__.	- Implement operator overloading in a custom Vector class.
26-27	Decorators and Metaclasses	- Function decorators. - Basics of metaclasses.	- Write a decorator to log execution time of functions.
28-30	OOP Project	- Develop a project using all OOP concepts (e.g., Library Management System).	- Host the project on Heroku or PythonAnywhere.

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Phase 3: Full-Stack Development (Days 31–50)

Day	Topics	Details	Tasks/Exercises
31-33	Flask or Django Basics	- Introduction to web frameworks. - Setting up views and templates.	- Create a "Hello World" web application.
34-36	CRUD Operations	- Create, Read, Update, Delete operations. - Form handling and validations.	- Build a blog application with CRUD features.
37-38	User Authentication	- Login/logout mechanisms. - Password hashing.	- Add user authentication to the blog application.
39-40	REST APIs	- Create RESTful APIs using Flask/Django REST Framework.	- Build APIs for the blog application (e.g., retrieve posts via API).
41-42	Front-End Integration	- Integrating React or Vue.js. - AJAX requests and API consumption.	- Add a React-based front end to the blog application.
43-44	Deployment	- Deploy the application to Heroku or AWS. - Using Docker for containerization.	- Deploy the blog application with a custom domain.
45-50	Full-Stack Project	- Develop a complete e-commerce application. - Integrate front-end and back-end.	- Host the e-commerce app on Heroku, AWS, or DigitalOcean.

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Phase 4: Advanced Python + Network Programming (Days 51–60)

Day	Topics	Details	Tasks/Exercises
51-52	Sockets and Networking	- Basics of sockets. - Creating a client-server application.	- Build a chat application using Python sockets.
53-54	Web Scraping	- Using BeautifulSoup and requests. - Scraping tables, forms, images.	- Scrape product details from an e-commerce website.
55-56	Async Programming	- asyncio basics. - Coroutines and event loops.	- Create an asynchronous downloader for large files.
57-58	Multithreading and Multiprocessing	- Basics of concurrency. - Using ThreadPoolExecutor.	- Write a program to process multiple files concurrently.
59-60	Network Programming Project	- Build a REST API-based service or an IoT dashboard.	- Host the project on a cloud platform and create documentation.

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Phase 5: Machine Learning & AI (Days 61–75)

Day	Topics	Details	Tasks/Exercises
61-62	ML Basics	- Setting up scikit-learn, numpy, pandas. - ML workflow: preprocessing to evaluation.	- Train a simple linear regression model for predicting house prices.
63-64	Data Preprocessing	- Handling missing data, scaling, and normalization. - Encoding categorical data.	- Clean and preprocess the Titanic dataset.
65-66	Classification Models	- Logistic Regression. - K-Nearest Neighbors (KNN).	- Build a KNN-based classifier for the Iris dataset.
67-68	Clustering and Unsupervised Learning	- K-Means clustering. - Dimensionality reduction (PCA).	- Cluster customers based on purchase behavior.
69-70	Advanced ML Concepts	- Decision Trees, Random Forests. - Hyperparameter tuning.	- Build a decision tree classifier for predicting loan approval.
71-72	AI: Natural Language Processing (NLP)	- Tokenization, stopword removal. - Sentiment analysis.	- Train a sentiment analysis model on a movie review dataset.
73-74	AI Project	- End-to-end ML project (e.g., recommendation system, fraud detection).	- Develop and deploy a recommendation system.
75	Hosting AI Models	- Deploy AI/ML models with Flask. - Use cloud platforms (Heroku, AWS).	- Host the sentiment analysis model with a web-based interface.

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🛠️ Tools and Resources

1. IDE: VS Code or PyCharm.
2. Frameworks: Django, Flask.
3. Libraries: numpy, pandas, scikit-learn, matplotlib, asyncio.
4. Hosting Platforms: Heroku, AWS.

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Feel free to contribute and improve this roadmap! 😄