Welcome to my Repo: ML-Learning-Repository

This Repo is a personal collection of small learning projects, reference notes, and example code used while learning and practicing the langiages and tools needed to become a Machine Learning Enginner. The materials are organized loosely by topic and are intended as lightweight, runnable examples and notes you can read and adapt.

Contents (high-level)

• General things to Note.txt — Short notes and reminders about ML python development in general.
• README.md — (this file) overview of the repo and guidance.
• learning_git_and_numpy/ — Primary learning projects and example scripts for NumPy and related topics.
  • numpy-learning-project/ — Series of short example scripts (01..17 and extras) that demonstrate NumPy array creation, indexing, broadcasting, ufuncs, and small image-processing examples. Each file is a self-contained script intended for exploration and learning.
    • Example files: 01_first_array.py, 02_array_creation.py, 07_array_indexing.py, 09_image_processor.py, 12_vectorization.py, etc.
  • things to note.txt — Misc notes related to the examples in this folder and git markdown formatting procedure.
• Text Files/ — A small set of text documents (how to write commit message.txt, assignment.txt).
• SQL_and_pandas/ — Examples and small utilities showing simple database generation and pandas workflows.
  • database_generator/ — Scripts to programmatically build example SQL files and demonstrate building a small test database.
    • build_database.py, run_database_build.py, and helper modules. The database where the tables are generated in is located at sQL_CoDe/my_database.db.
  • pandas_codE/ — Short pandas scripts and notes demonstrating data manipulation patterns (Gemini_Code.py, script_1.py, script_2.py).
  • sQL_CoDe/ — SQL snippets and an example notebook (learn_sql.ipynb) containing basic SQL learning material.

Purpose and intended usage

This folder acts as a personal learning library. Use it to:

• Read the example scripts to learn idiomatic NumPy/pandas patterns.
• Run the small scripts directly (they are written as simple Python scripts — open them in an editor and run with your local Python interpreter).
• Use database_generator to produce sample SQL if you want to practice loading data into a local database.

Typical quick-start steps (assumes you have Python 3.8+ installed):

1. Open a console in this folder (for example: the numpy-learning-project folder).
2. Create/activate a virtual environment if you like.
3. Run an example script with python 01_first_array.py or open an example in VS Code.

Notes:

• These examples intentionally avoid heavy, pinned dependencies. If you need pandas or NumPy, install them in your environment:

pip install numpy pandas jupyter

or use the requirements.txt file:

pip install -r requirements.txt

• Some files (the notebook learn_sql.ipynb) require Jupyter to view/run.

File highlights and short descriptions

• Most directories contain one or more of the following files: script_1.{file extension}, script_2.{file extension}, or Gemini_Code.{file extension}. These files serve distinct purposes within the learning workflow:
  • Gemini_Code: Stores the code provided for instructional purposes.
  • script_1: Used for practicing the concepts currently being taught.
  • script_2: Serves as a comprehensive repository for all demonstrated code, optimized for future reference.
• learning_git_and_numpy/numpy-learning-project/01_first_array.py — Intro to creating NumPy arrays.
• .../09_image_processor.py — Simple image-processing code demonstrating array shapes and indexing.
• SQL_and_pandas/database_generator/build_database.py — Generates SQL table creation and sample-data insert files (useful to seed a test database).
• SQL_and_pandas/pandas_codE/Gemini_Code.py — Short pandas examples and notes.

Conventions and organization suggestions

• Filenames are prefixed with numbers in the numpy-learning-project folder to indicate a suggested reading order. They are small, self-contained learning scripts.
• The SQL_and_pandas area is split into code that generates SQL (database_generator) and example pandas scripts (pandas_codE).

Recommended next steps / improvements

1. Add a requirements.txt or environment.yml listing the minimal packages used (NumPy, pandas, jupyter).
2. Convert a couple of example scripts into small unit tests or notebooks to demonstrate reproducibility.
3. Standardize naming and capitalization (pandas_codE vs pandas_code) for consistency.
4. Add short README files inside large subfolders (numpy-learning-project, database_generator) with direct run instructions and expected outputs.

License & attribution

This folder appears to be personal learning material. If you want to publish or share it, add a LICENSE file (MIT/Apache/BSD as appropriate) and ensure any third-party code snippets include attributions.

Contact Me

• Phone Number: +234 9133982877
• Whatsapp: Link to my Whatsapp
• Email: jesfusionprox@gmail.com

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This README was last edited on 2025-11-17