📊 Supervised Learning

Table of Contents

Course Objectives

  • Fundamental Python programming skills
  • Statistical concepts and how to apply them in practice
  • Gain experience with the Scikit, including data visualization with Plotly and data wrangling with Pandas

Program overview

The demand for skilled data science practitioners is rapidly growing. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi.

Subjects covered in this course

  • Preprocessing

    Learn about different preprocessing techniques, and apply them to prepare your data for modelling.

  • Regression Models

    Learn about regression models, how they work, and how to apply them.

  • Classification Models

    Learn about different classification models, how they work, and how to apply them.

  • Model Tuning

    Learn about how to get the most out of your model by tuning the hyperparameters of it.

  • Model Evaluation

    Learn about how to evaluate your model and compare its performance against other models.

  • Model Tuning

    Table of Contents Module Objectives Contents Module Objectives Why tuning the model? What are the hyperparameters? How to tune a model? Contents Grid Search Learn about grid search approach.



Meet your instructor

Mohammad Fili

FAQs

What are the prerequisites?

  • Understanding of linear algebra
  • Programming background in python

How to succeed in this course?

Last updated on Jun 1, 2022