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QuickRef.ME
QuickRef.ME - Quick Reference Cheat Sheet
Share quick reference and cheat sheet for developers
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Sharing NumPy Notes Today! React to this post to let me know you're interested! β¨π
π5π₯3π1
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π5β€1
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96%
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Free Notes.zip
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9. ML Cheatsheet
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13. ML Handwritten Notes
1. DATA SCIENCE INTERVIEW PREPARATION - PANDAS
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3. 100 Questions to Master DSA β¨
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5. Data Structures and Algorithms
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7. DSA_Book
8. Java_Complete_Notes
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β€3π2
Supercharge Your Resume & Land Your dream job with these Python projects:
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Thank you for supporting us! π€π«£
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1οΈβ£ Scraping Stock Prices from Yahoo Finance πΉ: Learn to scrape stock prices from Yahoo Finance using Python. β
Link: https://www.youtube.com/watch?v=c09sDpzlB2k
2οΈβ£ Credit Card Approval Prediction π³: Predict credit card approval using machine learning techniques. β
Link: https://www.youtube.com/watch?v=KZqP25FX8w8
3οΈβ£ Building a Chat Bot from Scratch π¬: Develop a chatbot from scratch with Python programming. β
Link: https://www.youtube.com/watch?v=JzPgeRJfNo4
4οΈβ£ Detecting Fake News with Python π°: Build a model to detect fake news articles using Python. β
Link: https://www.youtube.com/watch?v=U6ieiJAhXQ4
5οΈβ£ Music Recommendation System π΅: Create a system to recommend music based on user preferences. β
Link: https://www.youtube.com/watch?v=FV3IvHeuH_k
6οΈβ£ Email Spam Filtering π§: A project to classify emails as spam or not spam using Python. β
Link: https://www.youtube.com/watch?v=YncZ0WwxyzU
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β€7π1π1
30-Day Python Learning Roadmap for Data Analysis ππ
Days 1-5: Get Started with Python
Day 1: Install Python and a code editor (try Anaconda or Jupyter Notebook).
Days 2-5: Learn the basics of Python:
π’ Understand variables and data types.
π΅ Practice basic operations (addition, subtraction, etc.).
Days 6-10: Control Flow and Functions
Days 6-8: Learn about control flow:
βͺοΈ Use if statements to make decisions.
π Understand loops (for and while) to repeat tasks.
Days 9-10: Discover functions:
π‘ Learn how to create and use functions.
π£ Understand modules and how to import them.
Days 11-15: Data Structures
Days 11-12: Explore these data structures:
π€ Lists: ordered collections.
π΄ Tuples: immutable ordered collections.
π‘ Dictionaries: key-value pairs.
Days 13-15: Study:
π’ Sets: unique collections of items.
π΅ String manipulation: working with text data.
Days 16-20: Libraries for Data Analysis
Days 16-17: Get to know NumPy:
π Perform numerical operations efficiently.
Days 18-19: Dive into Pandas:
π Learn to manipulate and analyze data.
Day 20: Start with basic data visualization using Matplotlib:
π Create simple charts and plots.
Days 21-25: Data Cleaning and Analysis
Days 21-22: Practice data cleaning:
π§Ή Use Pandas to prepare your data for analysis.
Days 23-25: Explore data analysis techniques:
π Learn about exploratory data analysis (EDA).
Days 26-30: Advanced Topics
Days 26-27: Learn about data visualization with Seaborn:
π¨ Create more advanced visualizations.
Days 28-29: Get an introduction to machine learning with Scikit-Learn:
π€ Understand basic concepts and algorithms.
Day 30: Work on a small data analysis project:
π» Apply everything youβve learned!
Additional Resources
π Use Kaggle to find datasets for your projects.
π§βπ» Check out GeeksforGeeks to practice coding problems.
ENJOY LEARNING πβοΈ
Give your Feedback regarding this post, by Giving Reactions to This post!! βοΈπ€©
Days 1-5: Get Started with Python
Day 1: Install Python and a code editor (try Anaconda or Jupyter Notebook).
Days 2-5: Learn the basics of Python:
π’ Understand variables and data types.
π΅ Practice basic operations (addition, subtraction, etc.).
Days 6-10: Control Flow and Functions
Days 6-8: Learn about control flow:
βͺοΈ Use if statements to make decisions.
π Understand loops (for and while) to repeat tasks.
Days 9-10: Discover functions:
π‘ Learn how to create and use functions.
π£ Understand modules and how to import them.
Days 11-15: Data Structures
Days 11-12: Explore these data structures:
π€ Lists: ordered collections.
π΄ Tuples: immutable ordered collections.
π‘ Dictionaries: key-value pairs.
Days 13-15: Study:
π’ Sets: unique collections of items.
π΅ String manipulation: working with text data.
Days 16-20: Libraries for Data Analysis
Days 16-17: Get to know NumPy:
π Perform numerical operations efficiently.
Days 18-19: Dive into Pandas:
π Learn to manipulate and analyze data.
Day 20: Start with basic data visualization using Matplotlib:
π Create simple charts and plots.
Days 21-25: Data Cleaning and Analysis
Days 21-22: Practice data cleaning:
π§Ή Use Pandas to prepare your data for analysis.
Days 23-25: Explore data analysis techniques:
π Learn about exploratory data analysis (EDA).
Days 26-30: Advanced Topics
Days 26-27: Learn about data visualization with Seaborn:
π¨ Create more advanced visualizations.
Days 28-29: Get an introduction to machine learning with Scikit-Learn:
π€ Understand basic concepts and algorithms.
Day 30: Work on a small data analysis project:
π» Apply everything youβve learned!
Additional Resources
π Use Kaggle to find datasets for your projects.
π§βπ» Check out GeeksforGeeks to practice coding problems.
ENJOY LEARNING πβοΈ
Give your Feedback regarding this post, by Giving Reactions to This post!! βοΈπ€©
π8β€2π₯2
Forwarded from Career.Hubb πΌβ¨
Company: RiseUpp
Position: Data Science Intern
Location: Work From Home
Duration: 3 months
Stipend: βΉ30,000 per month
Internship Timing: Full Time
Working Days: 5 Days a week
Apply Here: Click Here to Apply!
Position: Data Science Intern
Location: Work From Home
Duration: 3 months
Stipend: βΉ30,000 per month
Internship Timing: Full Time
Working Days: 5 Days a week
Apply Here: Click Here to Apply!
π Top 5 Websites to Learn Coding 10x Faster in 2024!
Mimo: Fun, bite-sized coding exercises to learn programming.
URL: https://mimo.org
Sololearn: Interactive lessons for learning various programming languages.
URL: https://www.sololearn.com
Codecademy: Hands-on coding practice and real projects for faster learning.
URL: https://www.codecademy.com
Daily.dev: Stay updated with the latest coding news and resources.
URL: https://daily.dev
Khan Academy: Learn computer science fundamentals through expert-led courses.
URL: https://www.khanacademy.org
For more coding tips and resources, follow @pycode_hubb! π
Mimo: Fun, bite-sized coding exercises to learn programming.
URL: https://mimo.org
Sololearn: Interactive lessons for learning various programming languages.
URL: https://www.sololearn.com
Codecademy: Hands-on coding practice and real projects for faster learning.
URL: https://www.codecademy.com
Daily.dev: Stay updated with the latest coding news and resources.
URL: https://daily.dev
Khan Academy: Learn computer science fundamentals through expert-led courses.
URL: https://www.khanacademy.org
For more coding tips and resources, follow @pycode_hubb! π
β€2π1