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 with the Scikit-learn library in Python during 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, discover how to integrate it with other tools, and leave equipped to perform common data science tasks.

Course Content
  • Scikit-learn’s key features
  • Scikit-learn integration with pandas and Matplotlib
  • Hands-on data preparation with pandas
  • Machine learning models implementation
  • Visualization of data and results with Matplotlib
  • Comparison of different models
  • Explaination of machine learning model outcomes
  • Scikit-learn’s functionalities identification
  • Preprocessing of data for use in Scikit-learn models
  • Machine learning algorithms applications
  • Model performance analysis using evaluation metrics
  • Interpretation and explanation of model implications
Requirements & Materials

Requirements

A Windows or Mac computer with a webcam is required to participate in the course. Tablets or other devices are not supported. Recommended: Additional monitor.  

Familiarity with using a computer and adequate typing ability. 

Participants are expected to have their cameras on during the interactive sessions and must attend 80% of the sessions to pass the course. 

Prerequisites

Materials

All course materials.

Session Details

Who Should Attend

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

A person sitting at a desk looking at a computer

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
A group of people around a table

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.

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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