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:
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.
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