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 focus on building a predictive & recommendation based engine 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.

Topics for this course

24 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

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With a pure blend of Mathematics, Statistics, Data Analysis and Interpretation,we deliver training in numerous technologies ranging from basic report authoring, building large scale data integration, data quality, data visualization, data mining and the new generation distributed computing using big data.
17 Courses
2 students


Course Details

  • Level: Intermediate
  • Categories: Data Science
  • Total Lessons: 24
  • Total Enrolled: 0
  • Last Update: April 28, 2020