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Machine Learning in Physics: Glass Identification Problem

Machine Learning in Physics: Glass Identification Problem

 In this course you are going to solve the glass identification problem where you are going to build and train several machine learning models

What you'll learn

  • 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.

Requirements

Description

                                   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.

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