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1. Roboto – Clean & Corporate :- https://fonts.google.com/specimen/Roboto
2. Montserrat – Bold & Catchy :- https://fonts.google.com/specimen/Montserrat
3. Open Sans – Perfect for Readability :- https://fonts.google.com/specimen/Open+Sans
4. Lato – Simple Yet Stylish :- https://fonts.google.com/specimen/Lato
5. Inter – Loved by Tech Giants :- https://fonts.google.com/specimen/Inter
6. Poppins – Fresh & Trendy :- https://fonts.google.com/specimen/Poppins
7. Nunito – Clean & Universal :- https://fonts.google.com/specimen/Nunito
Python Cheat_Sheet -cheatography.pdf
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Python Cheat Sheet 🥂
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🔅 HTML Form Input Types
The <input> HTML element is used to create interactive controls for web-based forms to accept data from the user. A wide variety of input data types and control widgets are available, depending on the device and user agent. The <input> element is one of the most powerful and complex in all of HTML due to the sheer number of combinations of input types and attributes.
The <input> HTML element is used to create interactive controls for web-based forms to accept data from the user. A wide variety of input data types and control widgets are available, depending on the device and user agent. The <input> element is one of the most powerful and complex in all of HTML due to the sheer number of combinations of input types and attributes.
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Here are some interesting facts about Python:
1. *Created in the late 1980s*: Python was first conceived in the late 1980s by Guido van Rossum, a Dutch computer programmer.
2. *Named after Monty Python*: Guido van Rossum was a fan of the British comedy group Monty Python's Flying Circus, and he chose the name "Python" for his new language.
3. *First released in 1991*: The first version of Python, version 0.9.1, was released in 1991.
4. *High-level language*: Python is a high-level language, meaning it abstracts away many low-level details, allowing developers to focus on the logic of their program.
5. *Object-oriented*: Python is an object-oriented language, which means it organizes code into objects that contain data and functions that operate on that data.
6. *Dynamic typing*: Python is dynamically typed, which means you don't need to declare the type of a variable before using it.
7. *Large standard library*: Python has a vast and comprehensive standard library that includes modules for various tasks, such as file I/O, networking, and data structures.
8. *Cross-platform*: Python can run on multiple operating systems, including Windows, macOS, and Linux.
9. *Extensive use in data science and AI*: Python is widely used in data science, machine learning, and artificial intelligence due to its simplicity, flexibility, and extensive libraries, including NumPy, pandas, and scikit-learn.
10. *Large and active community*: Python has a massive and active community, with numerous conferences, meetups, and online forums.
1. *Created in the late 1980s*: Python was first conceived in the late 1980s by Guido van Rossum, a Dutch computer programmer.
2. *Named after Monty Python*: Guido van Rossum was a fan of the British comedy group Monty Python's Flying Circus, and he chose the name "Python" for his new language.
3. *First released in 1991*: The first version of Python, version 0.9.1, was released in 1991.
4. *High-level language*: Python is a high-level language, meaning it abstracts away many low-level details, allowing developers to focus on the logic of their program.
5. *Object-oriented*: Python is an object-oriented language, which means it organizes code into objects that contain data and functions that operate on that data.
6. *Dynamic typing*: Python is dynamically typed, which means you don't need to declare the type of a variable before using it.
7. *Large standard library*: Python has a vast and comprehensive standard library that includes modules for various tasks, such as file I/O, networking, and data structures.
8. *Cross-platform*: Python can run on multiple operating systems, including Windows, macOS, and Linux.
9. *Extensive use in data science and AI*: Python is widely used in data science, machine learning, and artificial intelligence due to its simplicity, flexibility, and extensive libraries, including NumPy, pandas, and scikit-learn.
10. *Large and active community*: Python has a massive and active community, with numerous conferences, meetups, and online forums.
*Python Detailed Roadmap* 🚀
📌 1. Basics
◼ Data Types & Variables
◼ Operators & Expressions
◼ Control Flow (if, loops)
📌 2. Functions & Modules
◼ Defining Functions
◼ Lambda Functions
◼ Importing & Creating Modules
📌 3. File Handling
◼ Reading & Writing Files
◼ Working with CSV & JSON
📌 4. Object-Oriented Programming (OOP)
◼ Classes & Objects
◼ Inheritance & Polymorphism
◼ Encapsulation
📌 5. Exception Handling
◼ Try-Except Blocks
◼ Custom Exceptions
📌 6. Advanced Python Concepts
◼ List & Dictionary Comprehensions
◼ Generators & Iterators
◼ Decorators
📌 7. Essential Libraries
◼ NumPy (Arrays & Computations)
◼ Pandas (Data Analysis)
◼ Matplotlib & Seaborn (Visualization)
📌 8. Web Development & APIs
◼ Web Scraping (BeautifulSoup, Scrapy)
◼ API Integration (Requests)
◼ Flask & Django (Backend Development)
📌 9. Automation & Scripting
◼ Automating Tasks with Python
◼ Working with Selenium & PyAutoGUI
📌 10. Data Science & Machine Learning
◼ Data Cleaning & Preprocessing
◼ Scikit-Learn (ML Algorithms)
◼ TensorFlow & PyTorch (Deep Learning)
📌 11. Projects
◼ Build Real-World Applications
◼ Showcase on GitHub
📌 12. ✅ Apply for Jobs
◼ Strengthen Resume & Portfolio
◼ Prepare for Technical Interviews
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📌 1. Basics
◼ Data Types & Variables
◼ Operators & Expressions
◼ Control Flow (if, loops)
📌 2. Functions & Modules
◼ Defining Functions
◼ Lambda Functions
◼ Importing & Creating Modules
📌 3. File Handling
◼ Reading & Writing Files
◼ Working with CSV & JSON
📌 4. Object-Oriented Programming (OOP)
◼ Classes & Objects
◼ Inheritance & Polymorphism
◼ Encapsulation
📌 5. Exception Handling
◼ Try-Except Blocks
◼ Custom Exceptions
📌 6. Advanced Python Concepts
◼ List & Dictionary Comprehensions
◼ Generators & Iterators
◼ Decorators
📌 7. Essential Libraries
◼ NumPy (Arrays & Computations)
◼ Pandas (Data Analysis)
◼ Matplotlib & Seaborn (Visualization)
📌 8. Web Development & APIs
◼ Web Scraping (BeautifulSoup, Scrapy)
◼ API Integration (Requests)
◼ Flask & Django (Backend Development)
📌 9. Automation & Scripting
◼ Automating Tasks with Python
◼ Working with Selenium & PyAutoGUI
📌 10. Data Science & Machine Learning
◼ Data Cleaning & Preprocessing
◼ Scikit-Learn (ML Algorithms)
◼ TensorFlow & PyTorch (Deep Learning)
📌 11. Projects
◼ Build Real-World Applications
◼ Showcase on GitHub
📌 12. ✅ Apply for Jobs
◼ Strengthen Resume & Portfolio
◼ Prepare for Technical Interviews
Like for more ❤️💪
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*15 Best Project Ideas for Data Science*📊
🚀 *Beginner Level:*
1. Exploratory Data Analysis (EDA) on Titanic Dataset
2. Netflix Movies/TV Shows Data Analysis
3. COVID-19 Data Visualization Dashboard
4. Sales Data Analysis (CSV/Excel)
5. Student Performance Analysis
🌟 *Intermediate Level:*
6. Sentiment Analysis on Tweets
7. Customer Segmentation using K-Means
8. Credit Score Classification
9. House Price Prediction
10. Market Basket Analysis (Apriori Algorithm)
🌌 *Advanced Level:*
11. Time Series Forecasting (Stock/Weather Data)
12. Fake News Detection using NLP
13. Image Classification with CNN
14. Resume Parser using NLP
15. Customer Churn Prediction
*React ❤️ for more*
🚀 *Beginner Level:*
1. Exploratory Data Analysis (EDA) on Titanic Dataset
2. Netflix Movies/TV Shows Data Analysis
3. COVID-19 Data Visualization Dashboard
4. Sales Data Analysis (CSV/Excel)
5. Student Performance Analysis
🌟 *Intermediate Level:*
6. Sentiment Analysis on Tweets
7. Customer Segmentation using K-Means
8. Credit Score Classification
9. House Price Prediction
10. Market Basket Analysis (Apriori Algorithm)
🌌 *Advanced Level:*
11. Time Series Forecasting (Stock/Weather Data)
12. Fake News Detection using NLP
13. Image Classification with CNN
14. Resume Parser using NLP
15. Customer Churn Prediction
*React ❤️ for more*
I’ve seen so many freshers and freelancers struggling to build a good portfolio.
They waste hours on resumes, websites, and still feel stuck.
That’s why I recommend checking out Peerlist — it’s simple, clean, and made for devs, designers, and builders.
You can link your GitHub, blogs, projects, and everything in one place.
*Check it here: 👉peerlist.io*
They waste hours on resumes, websites, and still feel stuck.
That’s why I recommend checking out Peerlist — it’s simple, clean, and made for devs, designers, and builders.
You can link your GitHub, blogs, projects, and everything in one place.
*Check it here: 👉peerlist.io*