Machine Learning in Physics: Glass Identification Problem

Apply machine learning techniques to solve physics problems

  Haithem Gasmi

   Machine Learning, Physics, Data Science and AI ML, Teaching & Academics

  Language - English

   Published on 02/2022



Move your ML skills from theory to practice in one of the most interesting fields " Physics"?

In this course you are going to solve the glass identification problem where you are going to build and train several machine learning models in order to  classify 7 types of glass( 1- Building windows float-processed glass / 2- Building windows non-float-processed glass / 3- Vehicle windows float-processed glass / 4- Vehicle windows non-float-processed-glass / 5- Containers glass / 6- Tableware glass / 7- Headlamps glass).

Through this course, you will learn how to deal with a machine learning problem from start to end: 

  1. You will learn how to import, explore, analyse and visualize your data.
  2. You will learn the different techniques of data preprocessing like : data cleaning, data scaling and data splitting in order to feed the  most convenient format of data to your models. 
  3. You will learn how to build and train a set of machine learning models such as : Logistic Regression, Support Vector Machine (SVM), Decision Trees and Random Forest Classifiers.
  4. You will learn how to evaluate and measure the performance of your models with different metrics like: accuracy-score and confusion matrix.
  5. You will learn how to compare between the results of your models.
  6. You will learn how to fine-tune your models to boost their performance.

After completing this course, you will gain a bunch of skillset that allows you to deal with any machine learning problem from the very first step to getting a fully trained performent model.

What Will I Get ?

  • Learn how to use and manipulate different machine learning libraries and tools to classify the different types of glass.

  • Visualize you data features with several types of plots such as : Bar plots and Scatter plots with the help of data Viz tools like: Matplotlib and Seaborn.

  • Build a good sense of exploring and analysing your data from the plotted graphs.

  • Get insights from data analysis that will help you solve the problem with the most convenient way.

  • Understand the different steps of Data Preprocessing like : checking the missing data, standardization and scaling, spliting the dataset).

  • Build and Train multiple State-of- the-art classification models like : Logistic Regression, KNN, Decision Tree and Random Forest Classifiers

  • Learn how to evalute your models/classifiers with different metrics.

  • Fine-tune different parameters to boost the performance of your models.

  • Learn how to set and read a confusion matrix in order to make comparisons between the actual values and the predicted values.


  • Familiar with foundational python programming concepts.

  • A very basic background of machine learning will help

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