Python-Machine_learning-for-Engineering-Tutorial---Feb 2024

Note: All comment desciption within code files written in Persian.

Welcome to Python Machine Learning for Engineering tutorial series (February 2024 edition). This repository serves as a comprehensive resource for learning Python and machine learning techniques tailored for engineering applications. Through practical sessions and detailed examples, you'll explore a wide range of Python concepts, machine learning libraries, and real-world engineering problems.

Note: The all rights belonged to Graphene and Advanced Material Laboratory (GAMLAB) under supervision of Amirkabir University of Technology (AUT)

Lecturer: Ali Pilehvar Meibody

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📚 Overview

This repository contains lecture files, additional supplementary resources, and code snippets designed for engineering students and professionals who are interested in Python programming, machine learning (ML), artificial intelligence (AI), and their engineering applications.

Note: The language used in the command for desciption in code files is Persian.

Key Sections:

• Sessions 02 to 16: Progressive Python tutorials that cover both basic and advanced topics.
• Supplementary Files: Additional Python code, AI comments, and a comprehensive list of functions from the Gamlab package.

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📝 Lecture Files

The core of the course is provided through a series of Python scripts labeled from Session02_Course24.py to Session16_FINAL_Course24.py. Each session introduces new concepts, code examples, and exercises to build your proficiency in Python and machine learning.

Session Breakdown:

• Session02_Course24.py: Introduction to Python programming, covering basic concepts like variables, data types, loops, conditionals (if, for, etc.), and functions.
• Session03_Course24.py: Introduction to Python's standard libraries and functions, such as math, random, and string manipulation.
• Session04_Course24.py: Deeper dive into Python classes, object-oriented programming (OOP), and practical examples.
• Session05_Course24.py: Working with external libraries like numpy, scipy, and pandas for numerical analysis and data manipulation.
• Session06 to Session16_FINAL_Course24.py: Progressively more complex topics, including:
  • Data visualization (matplotlib, seaborn)
  • Machine learning fundamentals (sklearn)
  • Practical engineering applications in ML
  • Final project and advanced case studies

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🛠️ Libraries and Tools

Throughout the tutorial, you'll use a variety of Python libraries that are commonly employed in engineering and machine learning. Some of the most important ones include:

• Built-in Python features: Keywords, built-in functions, control structures (if, for, etc.)
• Math Libraries:
  • math and cmath for mathematical operations
  • numpy for numerical computation
  • scipy for scientific and engineering tasks
• Data Analysis:
  • pandas for working with data structures and analysis
  • matplotlib and seaborn for plotting and data visualization
• Machine Learning:
  • scikit-learn (ML algorithms, data preprocessing, model evaluation)
  • Supplementary examples and use cases for ML in engineering contexts

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📂 Supplementary Files

Alongside the main lecture content, the repository includes a directory named supplementary_files. This directory contains:

• Additional Python code that complements the main lectures.
• Annotated examples and extended explanations on AI concepts.
• Function lists from the Gamlab package, including how to integrate it into your Python projects for specialized engineering applications.

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🌟 Why This Repository?

• Comprehensive Python Foundation: Start with the basics and build a strong understanding of Python's features, from syntax to advanced OOP.
• Engineering-focused ML: Explore machine learning through the lens of real-world engineering problems, focusing on practical applications.
• Step-by-step learning: The lectures are organized to progressively increase your knowledge, making complex topics approachable.
• Hands-on approach: Plenty of code examples, exercises, and project files to help you practice and master the concepts.

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💡 How to Use This Repository?

1. Clone the repository to your local machine:

   git clone https://github.com/APMaii/Feb2024-Python-and-ML-for-Engineering-Tutorial.git