Machine Learning in Physics: Glass Identification Problem

Machine Learning in Physics: Glass Identification Problem, available at $19.99, has an average rating of 4, with 16 lectures, based on 1 reviews, and has 21 subscribers.

You will learn about 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. This course is ideal for individuals who are Machine Learning students who want to excel their skills in machine learning with real world problems in physics. or Any machine learning learner who wants to go from theory to practice machine learning in different industries such as physics. It is particularly useful for Machine Learning students who want to excel their skills in machine learning with real world problems in physics. or Any machine learning learner who wants to go from theory to practice machine learning in different industries such as physics.

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Summary

Title: Machine Learning in Physics: Glass Identification Problem

Price: $19.99

Average Rating: 4

Number of Lectures: 16

Number of Published Lectures: 16

Number of Curriculum Items: 16

Number of Published Curriculum Objects: 16

Original Price: $19.99

Quality Status: approved

Status: Live

What You Will 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.

Who Should Attend

• Machine Learning students who want to excel their skills in machine learning with real world problems in physics.
• Any machine learning learner who wants to go from theory to practice machine learning in different industries such as physics.

Target Audiences

• Machine Learning students who want to excel their skills in machine learning with real world problems in physics.
• Any machine learning learner who wants to go from theory to practice machine learning in different industries such as physics.