Python for Students

@LearnPython3

🔅 Building the Classic Snake Game with Python

🔊 Author: Robin Andrews
🔸 Date: 2021-10-21
⏰ Duration: 1h 3m

🌀 Learn how to integrate and deepen your existing knowledge of Python by programming a Snake game.

📗 Topics: Game Programming, Python

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🔸 Full description 🔸

Are you looking for a fun, meaningful way to level up your Python programming skills? In this course, instructor Robin Andrews shows you how to put together what you need to know to build the Python version of a classic Snake game. Robin introduces you to turtle graphics and how you can use and control animation using Python turtle graphics. He explains global variables and shows you how to draw with turtle graphics by using stamps. With these pieces in place, its time to work on the game itself! Robin walks you through how to represent the snake, move it around the screen, and control the snakes direction. He discusses the game loop that is used to control the game and also goes over how to add snake food to the game, implement a scoring system, and reset the game. Robin finishes up with advice on how to use Lambda expressions to avoid repetition in your game and some fun ways to personalize your game.

Building the Classic Snake Game with Python

@LearnPython3

🔅 Python vs. JavaScript for Development

🔊 Author: Julie Nisbet
🔸 Date: 2021-10-04
⏰ Duration: 33m

🌀 Learn about the benefits and drawbacks of using Python and JavaScript, two common programming languages, when working on data science projects.

📗 Topics: JavaScript, Python

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🔸 Full description 🔸

Python and JavaScript are two of the most popular programming languages used by developers, analysts, and data scientists alike. But how do you know which language to use for specific projects or tasks? In this course, longtime instructor and software engineer Julie Nisbet answers this question and provides you with the tools you need to decide between these languages when approaching different projects. Julie starts by exploring the history of each language and how they operate. She then discusses key considerations when picking between each language for a project or task. Julie continues by reviewing key differences between the two programming languages, and closes by exploring various use cases for Python and JavaScript. Upon completing this course, you'll feel prepared to determine whether Python or JavaScript is best for you based on the project at hand.This course was created by Madecraft. We are pleased to host this training in our library.

Python vs. JavaScript for Development

@LearnPython3

Here's a list of 50+ Python libraries for data science👇

1. NumPy - "Handles arrays and math operations efficiently."
2. pandas - "Data manipulation made easy with data frames."
3. Matplotlib - "Plots and charts for data visualization."
4. Seaborn - "Creates attractive statistical plots."
5. SciPy - "Scientific and technical computing toolkit."
6. scikit-learn - "Machine learning at your fingertips."
7. TensorFlow - "For deep learning and neural networks."
8. Keras - "High-level deep learning API."
9. PyTorch - "Deep learning framework for researchers."
10. Statsmodels - "Statistical models and tests."
11. NLTK - "Natural language processing toolkit."
12. Gensim - "Topic modeling and document similarity."
13. XGBoost - "Gradient boosting for better predictions."
14. LightGBM - "Efficient gradient boosting framework."
15. CatBoost - "Optimized gradient boosting for categories."
16. NetworkX - "Build and analyze networks and graphs."
17. Beautiful Soup - "HTML and XML parsing made simple."
18. Requests - "Effortless HTTP requests."
19. SQLAlchemy - "Relational database interactions."
20. Pandas Profiling - "Generate data reports quickly."
21. Featuretools - "Automated feature engineering."
22. H2O - "Open-source machine learning platform."
23. Yellowbrick - "Visualize machine learning results."
24. Plotly - "Interactive and shareable plots."
25. Dash - "Build web apps for data visualization."
26. Flask - "Lightweight web app framework."
27. Streamlit - "Create apps with minimal code."
28. Bokeh - "Interactive web-based visualization."
29. GeoPandas - "Geospatial data analysis made easy."
30. Altair - "Declarative statistical visualization."
31. Prophet - "Time series forecasting with ease."
32. Feature-engine - "Feature engineering for ML."
33. Dask - "Parallel computing for big data."
34. Vaex - "Efficient dataframes for big data."
35. Optuna - "Automated hyperparameter tuning."
36. imbalanced-learn - "Handling imbalanced datasets."
37. Eli5 - "Interpret machine learning models."
38. SHAP - "Explainability for ML models."
39. scikit-image - "Image processing in Python."
40. TextBlob - "Text processing and sentiment analysis."
41. Polars - "Fast DataFrame library."
42. Cufflinks - "Combines Plotly with pandas."
43. TA-Lib - "Technical analysis for financial data."
44. OpenCV - "Computer vision and image processing."
45. Pymc3 - "Probabilistic programming for Bayesian analysis."
46. Scrapy - "Web scraping toolkit."
47. PySpark - "Apache Spark for big data processing."
48. PyArrow - "Columnar data format for analytics."
49. OptimalFlow - "AutoML for data scientists."
50. Pycaret - "Automated machine learning toolkit."

These libraries cover a wide range of data science tasks, from data manipulation and visualisation to machine learning and deep learning, making them essential tools for any data scientist or Python programmer.

🔰 Python Programming: The Complete Python Bootcamp 2023

🌟 4.4 - 1838 votes 💰 Original Price: $74.99

Python from Scratch. Learn Data Science and Visualization, Automation, Excel, SQL and Scraping with Python.100% Hands-On

Taught By: Andrei Dumitrescu, Crystal Mind Academy

Download Full Course: https://t.me/LearnPython3/511
Download All Courses: https://t.me/zero_to_mastery

#Development #Python

01 - Course Introduction

02 - Setup the Programming Environment

03 - Python Basics

04 - Hands-On Challenges Python Basics

05 - Strings in Python

06 - Hands-On Challenges Python Strings