100-Day Python Mastery Plan

1. About Python

Python is a beginner-friendly programming language used for web development, data science, automation, AI, and more. Its simple syntax makes it easy to learn, while its powerful libraries make it versatile for advanced tasks.

2. Why Learn Python?

Easy to Learn : Simple syntax, perfect for beginners.
Versatile : Used in web development, data analysis, AI, automation, etc.
High Demand : Python developers are in demand across industries.
Automation : Automate repetitive tasks like file handling, scraping, etc.
Future-Proof : Essential for AI, machine learning, and emerging tech.

3. Full Syllabus

Phase 1: Basics (Weeks 1–4)

Introduction to Python
- Install Python and set up tools (VS Code, Jupyter Notebook).
- Write your first “Hello World” program.
- Learn basic syntax (indentation, comments).
Variables & Data Types
- Numbers (int, float), Strings, Booleans.
- Type conversion (e.g., str(), int()).
Operators
- Arithmetic (+, -, *, /), Comparison (==, !=), Logical (and, or).
Control Flow
- If-Else statements.
- Loops (For, While).
- Break, Continue.
Input/Output
- Take user input (input()).
- Display output (print()).

Phase 2: Intermediate (Weeks 5–8)

Functions
- Define functions (def).
- Parameters and return values.
- Lambda functions.
Data Structures
- Lists, Tuples, Sets, Dictionaries.
- List comprehensions.
File Handling
- Read/write files.
- Handle exceptions (try-except).

Phase 3: Advanced (Weeks 9–12)

Object-Oriented Programming (OOP)
- Classes and objects.
- Inheritance, Encapsulation, Polymorphism.
Regular Expressions (Regex)
- Pattern matching (re module).
Multithreading
- Run tasks concurrently.
Libraries
- NumPy (arrays), Pandas (dataframes), Matplotlib (graphs).

Phase 4: Real-World Applications (Weeks 13–16)

Web Development
- Flask/Django for building websites.
Automation
- Automate tasks like file organization, email sending.
APIs & Web Scraping
- Use APIs to fetch data.
- Scrape websites using Beautiful Soup or Selenium.
Machine Learning
- Train models with Scikit-learn or TensorFlow.

4. Projects to Do

Beginner Projects

Calculator : Build a calculator using functions.
- Concepts: Functions, Input/Output.
- Tutorial .
To-Do List App : Create a command-line app to manage tasks.
- Concepts: Lists, File Handling.
- Tutorial .
Number Guessing Game : Guess a random number.
- Concepts: Random module, Loops.
- Tutorial .

Intermediate Projects

File Organizer : Automate organizing files into folders.
- Concepts: File Handling, OS Module.
- Tutorial .
Weather App : Fetch weather data using an API.
- Concepts: APIs, JSON Parsing.
- Tutorial .
Expense Tracker : Track daily expenses and generate reports.
- Concepts: File Handling, Data Structures.
- Tutorial .

Advanced Projects

Portfolio Website : Build a personal website using Flask/Django.
- Concepts: Web Frameworks, HTML/CSS.
- Flask Tutorial .
Chatbot : Create a chatbot using NLP.
- Concepts: Machine Learning, APIs.
- Tutorial .
Automated Social Media Bot : Automate posting or liking on social media.
- Concepts: Automation, Selenium.
- Tutorial .

5. YouTube Links for Learning Python

English Resources

Programming with Mosh : Python Full Course .
Corey Schafer : Python Tutorials .
freeCodeCamp : Python Projects .

Hindi Resources

CodeWithHarry : Python Playlist .
Telusko : Python Projects .
MySirG.com : Python Tutorials .

6. Final Tips

Practice Daily : Spend at least 1 hour coding every day.
Build Projects : Apply what you learn by building real-world projects.
Join Communities : Engage with forums like Reddit, Stack Overflow, or local Python groups.
Stay Consistent : Follow the roadmap step-by-step without skipping topics.
Document Progress : Keep a journal or GitHub repository to track your learning.

100 Days Master Plans

Sr.no	Topics To be Cover	Resources
1	Introduction to Python	Python.org
2	Setting Up Python Environment	Anaconda Installation
3	Basic Syntax and Variables	W3Schools – Python Basics
4	Data Types: Numbers, Strings	Real Python – Data Types
5	Operators (Arithmetic, Comparison, Logical)	Programiz – Python Operators
6	Input and Output	GeeksforGeeks – Input/Output
7	Conditional Statements (if, elif, else)	Tutorialspoint – Conditionals
8	Loops (for, while)	Real Python – Loops
9	Break, Continue, Pass	Programiz – Control Flow
10	Lists and List Comprehensions	W3Schools – Lists
11	Tuples and Tuple Methods	GeeksforGeeks – Tuples
12	Dictionaries and Dictionary Methods	Real Python – Dictionaries
13	Sets and Set Operations	Programiz – Sets
14	Functions: Defining and Calling	W3Schools – Functions
15	Function Arguments and Return Values	GeeksforGeeks – Functions
16	Lambda Functions	Real Python – Lambda
17	Modules and Importing	Programiz – Modules
18	Standard Library Overview	Python Docs – Standard Library
19	File Handling: Reading and Writing Files	W3Schools – File Handling
20	Exception Handling (try, except, finally)	Real Python – Exceptions
21	Custom Exceptions	GeeksforGeeks – Custom Exceptions
22	Object-Oriented Programming (OOP) Basics	Programiz – OOP
23	Classes and Objects	W3Schools – Classes
24	Constructors (init)	Real Python – Constructors
25	Inheritance	GeeksforGeeks – Inheritance
26	Polymorphism	Programiz – Polymorphism
27	Encapsulation	W3Schools – Encapsulation
28	Abstraction	Real Python – Abstraction
29	Magic Methods (str,repr, etc.)	GeeksforGeeks – Magic Methods
30	Iterators and Iterables	Programiz – Iterators
31	Generators and Yield	Real Python – Generators
32	Decorators	W3Schools – Decorators
33	Advanced Decorators	Real Python – Advanced Decorators
34	Working with Dates and Time	Programiz – Datetime
35	Regular Expressions (Regex)	W3Schools – Regex
36	JSON Parsing	Real Python – JSON
37	CSV File Handling	Programiz – CSV
38	XML Parsing	GeeksforGeeks – XML
39	Web Scraping with BeautifulSoup	Real Python – BeautifulSoup
40	APIs and Requests Library	Programiz – Requests
41	Building REST APIs with Flask	Flask Official Docs
42	Flask Routing and Templates	Real Python – Flask
43	Flask Forms and Validation	Flask-WTF Docs
44	Flask Database Integration (SQLAlchemy)	Flask-SQLAlchemy Docs
45	Testing Flask Applications	Real Python – Flask Testing
46	NumPy Basics	NumPy Official Docs
47	NumPy Arrays and Operations	W3Schools – NumPy
48	Pandas Basics	Pandas Official Docs
49	DataFrames and Series	Real Python – Pandas
50	Data Cleaning with Pandas	GeeksforGeeks – Data Cleaning
51	Matplotlib Basics	Matplotlib Official Docs
52	Plotting with Matplotlib	W3Schools – Matplotlib
53	Seaborn for Statistical Plots	Seaborn Official Docs
54	Data Visualization with Seaborn	Real Python – Seaborn
55	Exploratory Data Analysis (EDA)	Kaggle – EDA
56	Machine Learning Basics	Scikit-Learn Docs
57	Supervised Learning (Regression)	Real Python – Regression
58	Supervised Learning (Classification)	GeeksforGeeks – Classification
59	Unsupervised Learning (Clustering)	W3Schools – Clustering
60	Model Evaluation Metrics	Scikit-Learn Metrics
61	Hyperparameter Tuning	Real Python – Hyperparameter Tuning
62	Feature Engineering	Kaggle – Feature Engineering
63	Natural Language Processing (NLP) Basics	NLTK Official Docs
64	Text Preprocessing with NLTK	Real Python – NLP
65	Sentiment Analysis	GeeksforGeeks – Sentiment Analysis
66	Word Embeddings (Word2Vec, GloVe)	Medium – Word Embeddings
67	TensorFlow Basics	TensorFlow Official Docs
68	Building Neural Networks with TensorFlow	Real Python – TensorFlow
69	Keras for Deep Learning	Keras Official Docs
70	Convolutional Neural Networks (CNNs)	W3Schools – CNN
71	Recurrent Neural Networks (RNNs)	Real Python – RNN
72	Transfer Learning	TensorFlow – Transfer Learning
73	PyTorch Basics	PyTorch Official Docs
74	Building Models with PyTorch	Real Python – PyTorch
75	Debugging and Profiling Python Code	Real Python – Debugging
76	Logging in Python	GeeksforGeeks – Logging
77	Multithreading in Python	Programiz – Multithreading
78	Multiprocessing in Python	Real Python – Multiprocessing
79	Asyncio for Asynchronous Programming	W3Schools – Asyncio
80	Unit Testing with unittest	Real Python – Unit Testing
81	Test-Driven Development (TDD)	GeeksforGeeks – TDD
82	Version Control with Git	Git Official Docs
83	GitHub for Collaboration	GitHub Guides
84	Docker Basics	Docker Official Docs
85	Deploying Python Apps with Docker	Real Python – Docker
86	AWS Basics for Python Developers	AWS Official Docs
87	Deploying Flask Apps on AWS	Real Python – Flask on AWS
88	Google Cloud Platform (GCP) Basics	GCP Official Docs
89	Deploying Python Apps on GCP	Real Python – GCP
90	Heroku Deployment	Heroku Dev Center
91	CI/CD Pipelines with GitHub Actions	GitHub Actions Docs
92	Building APIs with FastAPI	FastAPI Official Docs
93	GraphQL with Python	Graphene Official Docs
94	WebSockets with Python	Real Python – WebSockets
95	Building GUIs with Tkinter	Tkinter Official Docs
96	PyQt for Advanced GUIs	PyQt Official Docs
97	Game Development with Pygame	Pygame Official Docs
98	Building Chatbots with Python	ChatterBot Docs
99	Final Project Planning	Project Ideas
100	Final Project Implementation	GitHub Repository Template

How to Use This Plan

Dedicate 1-2 hours daily to complete the topic.
Follow the provided resource links for detailed explanations and examples.
Practice coding exercises after learning each concept.
On Day 99 and 100, work on a final project to consolidate your knowledge.

100 Days Challange, Blog

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