FlexStack: Python Data Modeling and Visualization 3 – Model Behavior

  • Overview
  • Course Content
  • Requirements & Materials
Overview

FlexStack: Python Data Modeling and Visualization 3 – Model Behavior

Course Description

Harness the power of AI by implementing machine learning models with the Scikit-learn library in Python in the third course of the FlexStack: Python Data Modeling and Visualization Certificate. Course activities focus on the end-to-end process of data preparation, model implementation, and visualization of results. Participants also learn to accurately measure and validate predictive models and identify which algorithms best fit the available data and problem. Through hands-on exercises, learners gain valuable skills with Scikit-learn’s core functionalities, disover how to integrate it with other tools, and leave equipped to perform common data science tasks.

Course Content
  • Master Scikit-learn’s key features
  • Integrate Scikit-learn with pandas and Matplotlib
  • Hands-on data preparation with pandas
  • Implement various machine learning models
  • Visualize data and results with Matplotlib
  • Compare different models for best results
  • Explain outcomes of machine learning models
  • Identify Scikit-learn’s functionalities
  • Preprocess data for use in Scikit-learn models
  • Apply machine learning algorithms
  • Analyze model performance using evaluation metrics
  • Interpret and explain model implications
Requirements & Materials

Prerequisites

  • FlexStack: Python Data Modeling and Visualization 1 – Playing with Pandas,
  • FlexStack: Python Data Modeling and Visualization 2 – The Matplot Thickens, 
  • FlexStack: Python Data Modeling and Visualization 3 – Model Behavior


 

Materials

Computer, webcam, microphone, and internet access

Students will receive: All course materials

Who Should Attend

This course is ideal for experienced Python programmers, data analysts, and other professionals looking to develop their machine learning skills. It is ideal for those looking to better understand how to implement, interpret, and effectively utilize machine learning models.

What You Will Learn

  • Scikit-learn’s main features
  • Combination of Scikit-learn with Pandas and Matplotlib
  • Data preparation with Pandas
  • Machine learning models implementation
  • Visualization of data and results with Matplotlib
  • Comparison of different models for best results
  • Explanation of the outcomes of machine learning models
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How You Will Benefit

  • Identify the key features and functionalities of the Scikit-learn library.
  • Integrate Scikit-learn with pandas and matplotlib to create comprehensive data analysis workflows.
  • Perform preprocessing of data using pandas for use in Scikit-learn models.
  • Apply various machine learning algorithms from Scikit-learn to data sets.
  • Analyze the performance of machine learning models using evaluation metrics.
  • Visualize model results and data distributions using Matplotlib.
  • Compare different machine learning models to determine the best fit for specific problems.
  • Interpret the results of machine learning models and explain their implications.
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  • Taught by Experts in the Field icon
    Taught by Experts in the Field

The course schedule was well-structured with a mix of lectures, class discussions, and hands-on exercises led by knowledgeable and engaging instructors.

- Abe Kani
President

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