Machine Learning Project on Student grade Prediction
- Offered byGreat Learning
Machine Learning Project on Student grade Prediction at Great Learning Overview
Machine Learning Project on Student grade Prediction
at Great Learning
Duration | 1 hour |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Beginner |
Official Website | Explore Free Course |
Credential | Certificate |
Machine Learning Project on Student grade Prediction at Great Learning Highlights
Machine Learning Project on Student grade Prediction
at Great Learning
- Earn a certificate of completion from Great Learning
Machine Learning Project on Student grade Prediction at Great Learning Course details
Machine Learning Project on Student grade Prediction
at Great Learning
More about this course
- In this session, you will be working on an end-to-end case study to understand the different stages of Model building using the Machine Learning concept
- This will deal with "data manipulation" with pandas and Numpy, and "data visualization" with Matplotlib and Seaborn libraries with the Student grade dataset
- After Data manipulation and Data visualization, Exploratory data analysis will be performed on the Student grade and then a ML model will be built on the student grade dataset to create a model/system which can predict the grade according to the different attributes
- You will also learn about the basics of the sci-kit-learn library to implement the machine learning algorithm
Machine Learning Project on Student grade Prediction at Great Learning Curriculum
Machine Learning Project on Student grade Prediction
at Great Learning
Syllabus
Array
Machine Learning Project on Student grade Prediction at Great Learning Faculty details
Machine Learning Project on Student grade Prediction
at Great Learning
Ms. Sampriti Chatterjee
Designation : Instructor
Description : Sampriti has been working in the field of data science and has expertise in languages such as Python, SQL, and Java. She also has expertise in the field of deep learning and has worked with deep learning frameworks such as Keras and TensorFlow 2.0. She is been in the technical content side for the last few years.
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