Master Supervised Machine Learning & AI: Regression, Classification, Model Evaluation, and Ensemble Methods
Sub Category
- Other IT & Software
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Objectives
- Understand the fundamental principles and workflow of supervised machine learning.
- Implement various linear and non-linear regression algorithms for predictive modeling.
- Apply a diverse set of classification techniques including Logistic Regression and SVMs.
- Master data preprocessing steps such as feature scaling, encoding, and handling missing values.
Pre Requisites
- Basic Python programming skills (variables, loops, functions).
- Familiarity with fundamental statistics and probability concepts (mean, median, standard deviation).
FAQ
- Q. How long do I have access to the course materials?
- A. You can view and review the lecture materials indefinitely, like an on-demand channel.
- Q. Can I take my courses with me wherever I go?
- A. Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don't have an internet connection, some instructors also let their students download course lectures. That's up to the instructor though, so make sure you get on their good side!
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