This is my repository for learning Python from scratch.
I started learning Python because I want to understand the basics of programming and improve my logical thinking skills. I don’t have any prior programming background, so this is my first step into the world of coding. However, i have an interest into AI, Machine Learning, Coding & LLM.
So far, I’ve learned some basic Python concepts, such as:
- Variables and Data Types: How to store data in variables.
- User Input: Creating programs that can interact with users.
- Conditionals (
if-else): Making decisions based on certain conditions. - Loops (
for,while): Automating repetitive tasks. - Functions: Organizing code into reusable blocks.
- Attention Mechanism: Understanding how models focus on specific parts of input data.
- Heatmap Visualization : Creating heatmaps to visualize data distributions and model attention.
- Export GIF/MP4 : Saving visual outputs as GIF or MP4 for better presentation and analysis.
- Reinforcement Learning Animation : Random visualization to look agent behaviour.
- Sankey Diagram : Visualizes token interactions using flow diagram.
- Nested Loop : Loop in Loop, use for make 2D structure diagram.
- Simple pattern 2D : Make a simple design with star, cross pattern, multi cross, hollow pyramid diagram
- Uses the classic Iris dataset (150 samples, 4 features)
- Implements K-Nearest Neighbors (KNN) algorithm
- Includes train/test split and model evaluation
- visualization techniques: exploration using matplotlib, seaborn, and animation
- To-Do List: An app to add, view, and delete tasks.
- Calculator: A program to perform basic math operations.
- Number Guessing Game: A simple interactive game to guess a random number.
- Calculator for Math : Make a simple calculator to work simple math operation.
- Diamond Diagram : Create diamond pattern with plot pattern & colour combination.
- Cube gif & Rotation : Implementation Matplotlib for Cube rotation with gif format.
- Basic Python & Machine learning Theory: Learned about libraries like NumPy and Pandas for Data Analysis & Machine Learning
- Analysis of product sales using the Panda library: Created a simple program to analyze product sales data, calculate revenue, and identify best-selling product.
- Exploring Attention Mechanism: Learned how attention weights work in Transformers and visualized them using heatmaps.
- Heatmap Animation : Created a simple animation to display attention weights using Matplotlib.
- Export as GIF/MP4 : Saved the visualization as a GIF and MP4 file to share.
- Animated Attention Heatmap : Color intensity represents attention strength, Animated layer transitions, Generates GIF output
- Sankey Diagram : Visualizes token interactions using flow diagram with Arrow, Nodes represent tokens/attention, Customizable colors/labels
- Reinforcement Learning Animation : Random action selection, Real-time pole balancing visualization, Outputs GIF of agent behavior
- Loops (
for,while): Automating repetitive tasks (remind). - Functions: Organizing code into reusable blocks (remind).
- Nested Loop : Loop in Loop, use for make 2D structure diagram.
- Simple pattern 2D : Make a simple design with star, cross pattern, multi cross, hollow pyramid diagram
-
Loops (
for,while): Automating repetitive tasks (remind). -
Functions: Organizing code into reusable blocks (remind).
-
Nested Loop : Loop in Loop, use for make 2D structure diagram.
-
Simple pattern 2D : Make a simple design with star, cross pattern, multi cross, hollow pyramid diagram.
-
Calculator for Math : Make a simple calculator to work simple math operation.
-
Diamond Diagram : Create diamond pattern with plot pattern & colour combination.
-
Cube gif & Rotation : Implementation Matplotlib for Cube rotation with gif format.
-
Uses the classic Iris dataset (150 samples, 4 features)
-
Implements K-Nearest Neighbors (KNN) algorithm
-
Includes train/test split and model evaluation
- Today's learning focused on data visualization techniques using matplotlib, seaborn, and animation functions. We explored the Iris dataset visualization, created simple animations with matplotlib FuncAnimation, and analyzed complex patterns using seaborn's statistical plots. Key points included understanding figure setup, animation frame management, and interpreting seaborn's statistical representations.
- We encountered common errors such as
AttributeError(when methods weren't properly imported) andNameError(when variables weren't defined in the correct scope), which taught us the importance of proper library imports and variable management. These errors highlighted the learning process of debugging and understanding Python's error messaging system.
- Error File GeoJSON not included: DataSourceError: tangsel.geojson: No such file or directory
- Matrix & Vector application with theory
- Error Dataset Natural Earth not included: Changed structure dataset in new Data from Naturalearth
- Check the code, File reconditioning and alternative visualization for better graphic
- Setting up the 3D cube coordinates
- Applying a color gradient using Matplotlib's 'winter' colormap
- Implementing a rotation animation with horizontal movement
- Resolving deprecation warnings related to colormap access
- Saving the animation as a GIF using PillowWriter
- Simple visualization with dummy data for election in Jabodetabek & Tangsel
Programming ammateur, starting with Python, Machine Learning, AI, and LLMs.
This my first repository will be updated regularly as I continue learning Python, Machine Learning, AI & LLM 😊
If you have suggestions or ideas for simple projects I could try, feel free to let me know!