Step by step guide in mastering Scikit-Learn (2021)

Data science & Machine learning - Scikit learn (SKLearn)- Supervised Learning explained step by step for beginners

What you'll learn:
Basics of Data science and Machine learning
Create their own Data model and prediction modelling
Classification and Regression Model prediction

Requirements
Basic Python knowledge
Willing to learn new tools

Description
End to end Implementation of Data science and Machine Learning model using Scikit-Learn(SKLearn)

From Data analysis and gathering to creating your own modelling will be covered as part of this course.

This course covers the entire workflow of Scikit-Learn to create a model solving the real-life problem.

Also explained Pandas, Numpy, Matplotlib, Seaborn function used along with this course.

Covered in detail on creating model for Classification and regression helping users to solve supervised learning problems in detail.

Used 6+ Datasets for creating model and contains detailed explanation on how to choose estimators based on data available.

Explained the option of improving the results by changing parameters and Hyper-parameter in a model.

Covers in detail about:

Getting data ready

Choosing estimators

Fitting the data

Predicting values

Evaluation of results

Improving the results of the model

Saving the model.

Who this course is for
Beginners of programming
Willingness in learning to create their own modelling