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