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

 

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

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Step by step guide in mastering Scikit-Learn (2021) Data science & Machine learning - Scikit learn (SKLearn)- Supervised Learning explained

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

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