Learning Repository

This repository is a collection of various learning projects, notes, and explorations across different domains, primarily focusing on AI, data science, and financial markets.

Folder Structure and Purpose

Here's a breakdown of the repository's folder structure and the purpose of each directory:

Root Level:

• Learning: The root folder, encompassing all the learning materials and projects.

AI Related Folders:

• Learning/AI: Contains various AI-related projects and learning resources.

  • Learning/AI/Browser-Use: Focuses on using AI for browser automation and data extraction. Includes project files and virtual environment (venv) for managing dependencies (anthropic, playwright, langchain).
  • Learning/AI/DeepSeek/RAG: Implementations of Retrieval Augmented Generation using DeepSeek models.
  • Learning/AI/GoogleAI: Explorations and projects related to Google AI services and models.
    • Learning/AI/GoogleAI/Audio: Projects related to Audio AI on Google.
    • Learning/AI/GoogleAI/Gemini: Projects related to Gemini AI on Google.
    • Learning/AI/GoogleAI/GenAI: Projects related to GenAI on Google.
  • Learning/AI/LangChain: Contains projects utilizing the LangChain framework for building language model applications.
  • Learning/AI/ML: General machine learning projects and concepts.
  • Learning/AI/Milvus: Projects using the Milvus vector database.
  • Learning/AI/OpenAI: Contains projects using OpenAI's APIs.
    • Learning/AI/OpenAI/Generate_Audio: Audio generation projects using OpenAI.
    • Learning/AI/OpenAI/Generate_Text: Text generation projects using OpenAI.
  • Learning/AI/OpenCV: Projects focused on Computer Vision tasks using OpenCV. Includes a virtual environment (opencv-env) with necessary packages.
  • Learning/AI/PyCaret: Machine learning models with PyCaret
  • Learning/AI/ScikitLearn: Machine learning models with scikit-learn.
  • Learning/AI/TensorFlow: Projects related to building and training models with TensorFlow, including a TensorFlow environment folder with necessary packages.
  • Learning/AI/Yolo/image-detection/pigs/images/test: Yolo model training examples, images test set, for pig detection.

General Learning Folders:

• Learning/Alura: Learning projects from Alura courses.

  • Learning/Alura/LlamaIndex/Pandas: RAG (Retrival Augmented Generation) model with Pandas

• Learning/Asimov: A collection of projects and learning experiences related to the Asimov Academy.

  • Learning/Asimov/Car_Rental: A project of a Car Rental.
  • Learning/Asimov/Excel: Excel Projects.
  • Learning/Asimov/Master_Class: Materials and exercises from a masterclass.
    • Learning/Asimov/Master_Class/pages: Aplication pages
  • Learning/Asimov/OS: Projects and experiences related to Operating Systems.
  • Learning/Asimov/Object_Oriented: Python code OOP projects.
    • Learning/Asimov/Object_Oriented/Chaos_Simulator: A model of a Chaos Simulator
  • Learning/Asimov/PDB: Presumably related to protein database/biology projects (unclear without further context).
  • Learning/Asimov/Pandas: Pandas data analysis exercises and projects.
    • Learning/Asimov/Pandas/Analisando Dados com Pandas & SQL: Data analyses models with pandas and SQL
      • Learning/Asimov/Pandas/Analisando Dados com Pandas & SQL/SQL & Pandas: SQL & Pandas Data analysis
    • Learning/Asimov/Pandas/Oracle: Using Pandas with Oracle databases.
    • Learning/Asimov/Pandas/Séries Temporais: Time series Analyses.
  • Learning/Asimov/RAG: RAG related projects.
    • Learning/Asimov/RAG/data: Storing .txt data
  • Learning/Asimov/Rock_Paper_Scissors: A simple implementation of the Rock-Paper-Scissors game.
  • Learning/Asimov/SQL: SQL code of exercises from the Alura SQL
  • Learning/Asimov/Statistics: Statistics concepts.
  • Learning/Asimov/Tic_Tac_Toe: Implementation of the Tic-Tac-Toe game.
  • Learning/Asimov/Trading/Data_Sources: Data sources of trading projects.

• Learning/Camera: Experiments using camera and computer vision.

• Learning/FARM: Information from FARM

• Learning/FIAP: Learning Materials of FIAP courses.

• Learning/Kaggle: Notebooks and submissions for Kaggle competitions.

• Learning/Mesop: Experiments related to Mesop

• Learning/Oracle: Exercises connecting python with an Oracle Database

• Learning/Python_Language: Python Language exercises.

Financial Market Related Folders:

• Learning/QuantConnect: Algorithmic trading experiments using QuantConnect's Lean engine. * Learning/QuantConnect/Lean-Tutorial/Buy-Close-Sell-Open: A basic algorithmic trading strategy with Lean. * Learning/QuantConnect/Lean-Tutorial/Buy-Close-Sell-Open/backtests: Storing backtests files

• Learning/Seaborn: Graph building models using seaborn

• Learning/Stock_Market: Projects and data related to stock market analysis and trading.

  • Learning/Stock_Market/AI: AI models based on stock market data.
  • Learning/Stock_Market/Alpaca: Projects utilizing the Alpaca trading API.
    • Learning/Stock_Market/Alpaca/Generate_Indicators: A strategy to generate indicators.
  • Learning/Stock_Market/Bovespa: Analysis of stocks listed on the Bovespa stock exchange.
  • Learning/Stock_Market/IBKR: Projects related to Interactive Brokers.
    • Learning/Stock_Market/IBKR/ClientPortal Gateway: Using the client portal gateway of interactive brokers.
    • Learning/Stock_Market/IBKR/HackingTheMarkets/interactive-brokers-web-api: Web API interaction with Interactive Brokers.
      • Learning/Stock_Market/IBKR/HackingTheMarkets/interactive-brokers-web-api/scripts: Scripts code to implement.
      • Learning/Stock_Market/IBKR/HackingTheMarkets/interactive-brokers-web-api/webapp: Webapp Code.
  • Learning/Stock_Market/Kraken: Models trading on Kraken
  • Learning/Stock_Market/Prediction/NDX_v1: A model based on NDX predictions v1.
    • Learning/Stock_Market/Prediction/NDX_v1/Database: Database files.
  • Learning/Stock_Market/Prediction/SPX_v4: SPX prediction model, version 4.
  • Learning/Stock_Market/Put_Spread: Projects about Put Spread
  • Learning/Stock_Market/TastyTrade: Related to TastyTrade
  • Learning/Stock_Market/Trading: General code for stock market trading models.
  • Learning/Stock_Market/Yahoo: Data scraped from Yahoo finance, using selenium.

Utility Folders:

• Learning/Send_Email: Basic examples of sending emails with python.
• Learning/Snowflake: Testing connection with snowflake database.
• Learning/Streamlit: Projects built with the Streamlit framework for interactive web applications.
  • Learning/Streamlit/Farmtech: Farmtech apis
  • Learning/Streamlit/Games: Games using Streamlit.
• Learning/Threads: Studies about multithreading
• Learning/Weaviate: Weaviate vector database
• Learning/Web_Scraping: Code for extracting data from various websites using scraping techniques.

This repository is a work in progress and may contain unfinished projects, experiments, and notes. It serves as a personal learning hub and may not be suitable for production use without further development and testing.