Supervised Machine Learning using Python – Basic

Learn to create Machine Learning model to detect a food item category based on its title.


Supervised Machine Learning is a field and study of data where some part of it is known to us before we use it for predictive applications. Most common example is a labeled data where the previous values of a target or a dependent variable is known.
This program is focused to build predictive & recommendation based engines which returns a recommended output given certain inputs. For this to work, we collect labeled training data, split the data, derive equations and measure their accuracy. Powerful metrics are used like accuracy_score, _coef, intercept and confusion matrix to check the overall fitment.
This program will give a jump start to machine learning to create recommendation based systems in software engineering and data science.


TECHNOLOGY USED: Python core & Machine Learning Library

What Will I Learn?

  • Learn the handle labeled data
  • Collect output based on the previous experience
  • Learn to create any ML model from start
  • You will be able to identify features required for ML

Topics for this course

25 Lessons

What is Supervised Machine Learning

Finding the labeled data to use
Checking the reliability of the data in use
Data exploration and isolating outliers
Data sanitization and data frame creations

How Supervised Machine Learning works?

Understand Supervised Algorithm

Challenges in Supervised Machine Learning

Advantages & Disadvantages of Supervised Machine Learning

Best practices for Supervised Machine Learning

About the instructors

Pedagogy Trainings

4.67 ratings (6 )
23 Courses
83 students



Course Details


  • Knowledge of Python core

Target Audience

  • Executives and Managers
  • Students and Working Professionals in the IT as well as Non-IT sectors
  • Anyone who is an ML Enthusiast