Websites and platforms where you can practice Python projects, do hands-on coding, and gain valuable experience
1. w3schools
(https://www.w3schools.com/python): Offers interactive Python courses and coding exercises. Great for beginners.
2. Learn Python (https://learnpython.org/): Good resource for beginners
3. Freecodecamp (https://www.freecodecamp.org/learn/data-analysis-with-python/#data-analysis-with-python-course): Offers Python courses, including those from top universities. You can earn certificates upon completion.
4. Hackerrank (https://www.hackerrank.com/domains/tutorials/10-days-of-python): Offers Python tutorials and coding challenges to practice your skills.
5. Google (https://developers.google.com/edu/python): Free resource to learn python from Google.
6. Project Euler (https://projecteuler.net/): Provides mathematical and computational problems that can be solved with Python. Great for honing your programming skills.
7. Python.org (https://docs.python.org/3/tutorial/index.html): The official Python website has a tutorial section that includes exercises and examples to practice Python concepts.
8. GitHub (https://github.com/): Explore Python repositories, contribute to open-source projects, or start your own Python project to build a portfolio.
9. Kaggle (https://www.kaggle.com/): Offers Python datasets, competitions, and notebooks for data science and machine learning projects.
10. Udemy (https://bit.ly/45q7pxh): Amazing course to master Python in 100 days with coding challenges with certificate. Learn data science, automation, build websites, games and apps!
11. Real Python (https://realpython.com/): Offers tutorials and articles on various Python topics, including practical projects.
Remember that practice is key to mastering Python. Choose projects and exercises that align with your interests and goals, and don't hesitate to explore multiple platforms to diversify your learning experience.
ENJOY LEARNING ๐๐
1. w3schools
(https://www.w3schools.com/python): Offers interactive Python courses and coding exercises. Great for beginners.
2. Learn Python (https://learnpython.org/): Good resource for beginners
3. Freecodecamp (https://www.freecodecamp.org/learn/data-analysis-with-python/#data-analysis-with-python-course): Offers Python courses, including those from top universities. You can earn certificates upon completion.
4. Hackerrank (https://www.hackerrank.com/domains/tutorials/10-days-of-python): Offers Python tutorials and coding challenges to practice your skills.
5. Google (https://developers.google.com/edu/python): Free resource to learn python from Google.
6. Project Euler (https://projecteuler.net/): Provides mathematical and computational problems that can be solved with Python. Great for honing your programming skills.
7. Python.org (https://docs.python.org/3/tutorial/index.html): The official Python website has a tutorial section that includes exercises and examples to practice Python concepts.
8. GitHub (https://github.com/): Explore Python repositories, contribute to open-source projects, or start your own Python project to build a portfolio.
9. Kaggle (https://www.kaggle.com/): Offers Python datasets, competitions, and notebooks for data science and machine learning projects.
10. Udemy (https://bit.ly/45q7pxh): Amazing course to master Python in 100 days with coding challenges with certificate. Learn data science, automation, build websites, games and apps!
11. Real Python (https://realpython.com/): Offers tutorials and articles on various Python topics, including practical projects.
Remember that practice is key to mastering Python. Choose projects and exercises that align with your interests and goals, and don't hesitate to explore multiple platforms to diversify your learning experience.
ENJOY LEARNING ๐๐
๐8
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๐5
๐จโ๐ปWebsites to Practice Python
Python Basics ๐:
1. http://codingbat.com/python
2. https://www.hackerrank.com/
3. https://www.hackerearth.com/practice/
Practice Problems set :
4. https://projecteuler.net/archives
5. http://www.codeabbey.com/index/task_list
6. http://www.pythonchallenge.com/
Python Basics ๐:
1. http://codingbat.com/python
2. https://www.hackerrank.com/
3. https://www.hackerearth.com/practice/
Practice Problems set :
4. https://projecteuler.net/archives
5. http://www.codeabbey.com/index/task_list
6. http://www.pythonchallenge.com/
7 Baby steps to learn Python:
1. Learn the basics: Start with the fundamentals of Python programming language, such as data types, variables, operators, control structures, and functions.
2. Write simple programs: Start writing simple programs to practice what you have learned. Start with small programs that solve basic problems, such as calculating the factorial of a number, checking whether a number is prime or not, or finding the sum of a sequence of numbers.
3. Work on small projects: Start working on small projects that interest you. These can be simple projects, such as creating a calculator, building a basic game, or automating a task. By working on small projects, you can develop your programming skills and gain confidence.
4. Learn from other people's code: Look at other people's code and try to understand how it works. You can find many open-source projects on platforms like GitHub. Analyze the code, see how it's structured, and try to figure out how the program works.
5. Read Python documentation: Python has extensive documentation, which is very helpful for beginners. Read the documentation to learn more about Python libraries, modules, and functions.
6. Participate in online communities: Participate in online communities like StackOverflow, Reddit, or Python forums. These communities have experienced programmers who can help you with your doubts and questions.
7. Keep practicing: Practice is the key to becoming a good programmer. Keep working on projects, practicing coding problems, and experimenting with different techniques. The more you practice, the better you'll get.
Best Resource to learn Python
Freecodecamp Python ML Course with FREE Certificate
Python for Data Analysis
Python course for beginners by Microsoft
Scientific Computing with Python
Python course by Google
Python Free Resources
Please give us credits while sharing: -> https://t.me/free4unow_backup
ENJOY LEARNING ๐๐
1. Learn the basics: Start with the fundamentals of Python programming language, such as data types, variables, operators, control structures, and functions.
2. Write simple programs: Start writing simple programs to practice what you have learned. Start with small programs that solve basic problems, such as calculating the factorial of a number, checking whether a number is prime or not, or finding the sum of a sequence of numbers.
3. Work on small projects: Start working on small projects that interest you. These can be simple projects, such as creating a calculator, building a basic game, or automating a task. By working on small projects, you can develop your programming skills and gain confidence.
4. Learn from other people's code: Look at other people's code and try to understand how it works. You can find many open-source projects on platforms like GitHub. Analyze the code, see how it's structured, and try to figure out how the program works.
5. Read Python documentation: Python has extensive documentation, which is very helpful for beginners. Read the documentation to learn more about Python libraries, modules, and functions.
6. Participate in online communities: Participate in online communities like StackOverflow, Reddit, or Python forums. These communities have experienced programmers who can help you with your doubts and questions.
7. Keep practicing: Practice is the key to becoming a good programmer. Keep working on projects, practicing coding problems, and experimenting with different techniques. The more you practice, the better you'll get.
Best Resource to learn Python
Freecodecamp Python ML Course with FREE Certificate
Python for Data Analysis
Python course for beginners by Microsoft
Scientific Computing with Python
Python course by Google
Python Free Resources
Please give us credits while sharing: -> https://t.me/free4unow_backup
ENJOY LEARNING ๐๐
๐5
Python from scratch
by University of Waterloo
0. Introduction
1. First steps
2. Built-in functions
3. Storing and using information
4. Creating functions
5. Booleans
6. Branching
7. Building better programs
8. Iteration using while
9. Storing elements in a sequence
10. Iteration using for
11. Bundling information into objects
12. Structuring data
13. Recursion
https://open.cs.uwaterloo.ca/python-from-scratch/
#python
by University of Waterloo
0. Introduction
1. First steps
2. Built-in functions
3. Storing and using information
4. Creating functions
5. Booleans
6. Branching
7. Building better programs
8. Iteration using while
9. Storing elements in a sequence
10. Iteration using for
11. Bundling information into objects
12. Structuring data
13. Recursion
https://open.cs.uwaterloo.ca/python-from-scratch/
#python
๐1
Guide to Building an AI Agent
1๏ธโฃ ๐๐ต๐ผ๐ผ๐๐ฒ ๐๐ต๐ฒ ๐ฅ๐ถ๐ด๐ต๐ ๐๐๐
Not all LLMs are equal. Pick one that:
- Excels in reasoning benchmarks
- Supports chain-of-thought (CoT) prompting
- Delivers consistent responses
๐ Tip: Experiment with models & fine-tune prompts to enhance reasoning.
2๏ธโฃ ๐๐ฒ๐ณ๐ถ๐ป๐ฒ ๐๐ต๐ฒ ๐๐ด๐ฒ๐ป๐โ๐ ๐๐ผ๐ป๐๐ฟ๐ผ๐น ๐๐ผ๐ด๐ถ๐ฐ
Your agent needs a strategy:
- Tool Use: Call tools when needed; otherwise, respond directly.
- Basic Reflection: Generate, critique, and refine responses.
- ReAct: Plan, execute, observe, and iterate.
- Plan-then-Execute: Outline all steps first, then execute.
๐ Choosing the right approach improves reasoning & reliability.
3๏ธโฃ ๐๐ฒ๐ณ๐ถ๐ป๐ฒ ๐๐ผ๐ฟ๐ฒ ๐๐ป๐๐๐ฟ๐๐ฐ๐๐ถ๐ผ๐ป๐ & ๐๐ฒ๐ฎ๐๐๐ฟ๐ฒ๐
Set operational rules:
- How to handle unclear queries? (Ask clarifying questions)
- When to use external tools?
- Formatting rules? (Markdown, JSON, etc.)
- Interaction style?
๐ Clear system prompts shape agent behavior.
4๏ธโฃ ๐๐บ๐ฝ๐น๐ฒ๐บ๐ฒ๐ป๐ ๐ฎ ๐ ๐ฒ๐บ๐ผ๐ฟ๐ ๐ฆ๐๐ฟ๐ฎ๐๐ฒ๐ด๐
LLMs forget past interactions. Memory strategies:
- Sliding Window: Retain recent turns, discard old ones.
- Summarized Memory: Condense key points for recall.
- Long-Term Memory: Store user preferences for personalization.
๐ Example: A financial AI recalls risk tolerance from past chats.
5๏ธโฃ ๐๐พ๐๐ถ๐ฝ ๐๐ต๐ฒ ๐๐ด๐ฒ๐ป๐ ๐๐ถ๐๐ต ๐ง๐ผ๐ผ๐น๐ & ๐๐ฃ๐๐
Extend capabilities with external tools:
- Name: Clear, intuitive (e.g., "StockPriceRetriever")
- Description: What does it do?
- Schemas: Define input/output formats
- Error Handling: How to manage failures?
๐ Example: A support AI retrieves order details via CRM API.
6๏ธโฃ ๐๐ฒ๐ณ๐ถ๐ป๐ฒ ๐๐ต๐ฒ ๐๐ด๐ฒ๐ป๐โ๐ ๐ฅ๐ผ๐น๐ฒ & ๐๐ฒ๐ ๐ง๐ฎ๐๐ธ๐
Narrowly defined agents perform better. Clarify:
- Mission: (e.g., "I analyze datasets for insights.")
- Key Tasks: (Summarizing, visualizing, analyzing)
- Limitations: ("I donโt offer legal advice.")
๐ Example: A financial AI focuses on finance, not general knowledge.
7๏ธโฃ ๐๐ฎ๐ป๐ฑ๐น๐ถ๐ป๐ด ๐ฅ๐ฎ๐ ๐๐๐ ๐ข๐๐๐ฝ๐๐๐
Post-process responses for structure & accuracy:
- Convert AI output to structured formats (JSON, tables)
- Validate correctness before user delivery
- Ensure correct tool execution
๐ Example: A financial AI converts extracted data into JSON.
8๏ธโฃ ๐ฆ๐ฐ๐ฎ๐น๐ถ๐ป๐ด ๐๐ผ ๐ ๐๐น๐๐ถ-๐๐ด๐ฒ๐ป๐ ๐ฆ๐๐๐๐ฒ๐บ๐ (๐๐ฑ๐๐ฎ๐ป๐ฐ๐ฒ๐ฑ)
For complex workflows:
- Info Sharing: What context is passed between agents?
- Error Handling: What if one agent fails?
- State Management: How to pause/resume tasks?
๐ Example:
1๏ธโฃ One agent fetches data
2๏ธโฃ Another summarizes
3๏ธโฃ A third generates a report
Master the fundamentals, experiment, and refine and.. now go build something amazing!
1๏ธโฃ ๐๐ต๐ผ๐ผ๐๐ฒ ๐๐ต๐ฒ ๐ฅ๐ถ๐ด๐ต๐ ๐๐๐
Not all LLMs are equal. Pick one that:
- Excels in reasoning benchmarks
- Supports chain-of-thought (CoT) prompting
- Delivers consistent responses
๐ Tip: Experiment with models & fine-tune prompts to enhance reasoning.
2๏ธโฃ ๐๐ฒ๐ณ๐ถ๐ป๐ฒ ๐๐ต๐ฒ ๐๐ด๐ฒ๐ป๐โ๐ ๐๐ผ๐ป๐๐ฟ๐ผ๐น ๐๐ผ๐ด๐ถ๐ฐ
Your agent needs a strategy:
- Tool Use: Call tools when needed; otherwise, respond directly.
- Basic Reflection: Generate, critique, and refine responses.
- ReAct: Plan, execute, observe, and iterate.
- Plan-then-Execute: Outline all steps first, then execute.
๐ Choosing the right approach improves reasoning & reliability.
3๏ธโฃ ๐๐ฒ๐ณ๐ถ๐ป๐ฒ ๐๐ผ๐ฟ๐ฒ ๐๐ป๐๐๐ฟ๐๐ฐ๐๐ถ๐ผ๐ป๐ & ๐๐ฒ๐ฎ๐๐๐ฟ๐ฒ๐
Set operational rules:
- How to handle unclear queries? (Ask clarifying questions)
- When to use external tools?
- Formatting rules? (Markdown, JSON, etc.)
- Interaction style?
๐ Clear system prompts shape agent behavior.
4๏ธโฃ ๐๐บ๐ฝ๐น๐ฒ๐บ๐ฒ๐ป๐ ๐ฎ ๐ ๐ฒ๐บ๐ผ๐ฟ๐ ๐ฆ๐๐ฟ๐ฎ๐๐ฒ๐ด๐
LLMs forget past interactions. Memory strategies:
- Sliding Window: Retain recent turns, discard old ones.
- Summarized Memory: Condense key points for recall.
- Long-Term Memory: Store user preferences for personalization.
๐ Example: A financial AI recalls risk tolerance from past chats.
5๏ธโฃ ๐๐พ๐๐ถ๐ฝ ๐๐ต๐ฒ ๐๐ด๐ฒ๐ป๐ ๐๐ถ๐๐ต ๐ง๐ผ๐ผ๐น๐ & ๐๐ฃ๐๐
Extend capabilities with external tools:
- Name: Clear, intuitive (e.g., "StockPriceRetriever")
- Description: What does it do?
- Schemas: Define input/output formats
- Error Handling: How to manage failures?
๐ Example: A support AI retrieves order details via CRM API.
6๏ธโฃ ๐๐ฒ๐ณ๐ถ๐ป๐ฒ ๐๐ต๐ฒ ๐๐ด๐ฒ๐ป๐โ๐ ๐ฅ๐ผ๐น๐ฒ & ๐๐ฒ๐ ๐ง๐ฎ๐๐ธ๐
Narrowly defined agents perform better. Clarify:
- Mission: (e.g., "I analyze datasets for insights.")
- Key Tasks: (Summarizing, visualizing, analyzing)
- Limitations: ("I donโt offer legal advice.")
๐ Example: A financial AI focuses on finance, not general knowledge.
7๏ธโฃ ๐๐ฎ๐ป๐ฑ๐น๐ถ๐ป๐ด ๐ฅ๐ฎ๐ ๐๐๐ ๐ข๐๐๐ฝ๐๐๐
Post-process responses for structure & accuracy:
- Convert AI output to structured formats (JSON, tables)
- Validate correctness before user delivery
- Ensure correct tool execution
๐ Example: A financial AI converts extracted data into JSON.
8๏ธโฃ ๐ฆ๐ฐ๐ฎ๐น๐ถ๐ป๐ด ๐๐ผ ๐ ๐๐น๐๐ถ-๐๐ด๐ฒ๐ป๐ ๐ฆ๐๐๐๐ฒ๐บ๐ (๐๐ฑ๐๐ฎ๐ป๐ฐ๐ฒ๐ฑ)
For complex workflows:
- Info Sharing: What context is passed between agents?
- Error Handling: What if one agent fails?
- State Management: How to pause/resume tasks?
๐ Example:
1๏ธโฃ One agent fetches data
2๏ธโฃ Another summarizes
3๏ธโฃ A third generates a report
Master the fundamentals, experiment, and refine and.. now go build something amazing!
๐2
Complete Roadmap to Learn Python Programming in 2025
Beginner Level
1. Basics of Python
- Understanding syntax and basic concepts
- Variables and data types
- Basic operators and expressions
- Input and output functions
- Conditional statements (if, elif, else)
- Loops (for, while)
2. Data Structures
- Lists
- Tuples
- Sets
- Dictionaries
3. Functions and Modules
- Defining and calling functions
- Arguments and return values
- Lambda functions
- Built-in modules and importing external modules
Intermediate Level
4. File Handling
- Reading from and writing to files
- Working with CSV, JSON, and other file formats
5. Object-Oriented Programming (OOP)
- Classes and objects
- Methods and constructors
- Inheritance and polymorphism
- Encapsulation and abstraction
6. Error Handling and Exceptions
- Try, except, finally blocks
- Raising exceptions
- Custom exceptions
7. Libraries and Frameworks
- Understanding and using popular libraries (NumPy, Pandas, Matplotlib)
- Introduction to web frameworks (Flask, Django)
Advanced Level
8. Advanced Concepts
- Decorators
- Generators
- Context managers
9. Working with Databases
- SQL and NoSQL databases
- ORM (Object-Relational Mapping) with SQLAlchemy or Django ORM
10. Web Development
- Full-stack development with Django or Flask
- RESTful APIs and backend services
11. Data Science and Machine Learning
- Data analysis with Pandas
- Data visualization with Matplotlib and Seaborn
- Machine learning with Scikit-Learn and TensorFlow
Tools and Best Practices
12. Version Control
- Using Git and GitHub for version control
- Collaboration and branching strategies
13. Testing and Debugging
- Unit testing with Unittest or PyTest
- Debugging techniques and tools
14. Development Environment
- Setting up IDEs (PyCharm, VS Code)
- Virtual environments and dependency management
15. Code Quality
- Writing clean and efficient code
- Adhering to PEP 8 standards
- Code reviews and refactoring
Best Resource to learn Python
Python Interview Questions with Answers
Freecodecamp Python ML Course with FREE Certificate
Python for Data Analysis
Python course for beginners by Microsoft
Scientific Computing with Python
Python course by Google
Python Free Resources
Please give us credits while sharing: -> https://t.me/free4unow_backup
ENJOY LEARNING ๐๐
Beginner Level
1. Basics of Python
- Understanding syntax and basic concepts
- Variables and data types
- Basic operators and expressions
- Input and output functions
- Conditional statements (if, elif, else)
- Loops (for, while)
2. Data Structures
- Lists
- Tuples
- Sets
- Dictionaries
3. Functions and Modules
- Defining and calling functions
- Arguments and return values
- Lambda functions
- Built-in modules and importing external modules
Intermediate Level
4. File Handling
- Reading from and writing to files
- Working with CSV, JSON, and other file formats
5. Object-Oriented Programming (OOP)
- Classes and objects
- Methods and constructors
- Inheritance and polymorphism
- Encapsulation and abstraction
6. Error Handling and Exceptions
- Try, except, finally blocks
- Raising exceptions
- Custom exceptions
7. Libraries and Frameworks
- Understanding and using popular libraries (NumPy, Pandas, Matplotlib)
- Introduction to web frameworks (Flask, Django)
Advanced Level
8. Advanced Concepts
- Decorators
- Generators
- Context managers
9. Working with Databases
- SQL and NoSQL databases
- ORM (Object-Relational Mapping) with SQLAlchemy or Django ORM
10. Web Development
- Full-stack development with Django or Flask
- RESTful APIs and backend services
11. Data Science and Machine Learning
- Data analysis with Pandas
- Data visualization with Matplotlib and Seaborn
- Machine learning with Scikit-Learn and TensorFlow
Tools and Best Practices
12. Version Control
- Using Git and GitHub for version control
- Collaboration and branching strategies
13. Testing and Debugging
- Unit testing with Unittest or PyTest
- Debugging techniques and tools
14. Development Environment
- Setting up IDEs (PyCharm, VS Code)
- Virtual environments and dependency management
15. Code Quality
- Writing clean and efficient code
- Adhering to PEP 8 standards
- Code reviews and refactoring
Best Resource to learn Python
Python Interview Questions with Answers
Freecodecamp Python ML Course with FREE Certificate
Python for Data Analysis
Python course for beginners by Microsoft
Scientific Computing with Python
Python course by Google
Python Free Resources
Please give us credits while sharing: -> https://t.me/free4unow_backup
ENJOY LEARNING ๐๐
๐4