Supervised Machine Learning using Python – Basic
Description
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.
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
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
About Me
Course
Contact Me