Machine & Deep Learning Algorithms: Data Preparation in Pandas ML

Machine Learning
  • 10 Videos | 1h 7m 41s
  • Includes Assessment
  • Earns a Badge
Likes 62 Likes 62
Classification, regression, and clustering are some of the most commonly used machine learning (ML) techniques and there are various algorithms available for these tasks. In this 10-video course, learners can explore their application in Pandas ML. First, examine how to load data from a CSV (comma-separated values) file into a Pandas data frame and prepare the data for training a classification model. Then use the scikit-learn library to build and train a LinearSVC classification model and evaluate its performance with available model evaluation functions. You will explore how to install Pandas ML and define and configure a ModelFrame, then compare training and evaluation in Pandas ML with equivalent tasks in scikit-learn. Learn how to build a linear regression model by using Pandas ML. Then evaluate a regression model by using metrics such as r-square and mean squared error, and visualize its performance with Matplotlib. Work with ModelFrames for feature extraction and encoding, and configure and build a clustering model with the K-Means algorithm, analyzing data clusters to determine unique characteristics. Finally, complete an exercise on regression, classification, and clustering.

WHAT YOU WILL LEARN

  • load data from a CSV file into a Pandas dataframe and prepare the data for training a classification model
    use the scikit-learn library to build and train a LinearSVC classification model and then evaluate its performance using the available model evaluation functions
    install Pandas ML and then define and configure a ModelFrame
    compare training and evaluation in Pandas ML with the equivalent tasks in scikit-learn
    use Pandas for feature extraction and one-hot encoding, load its contents into a ModelFrame, and initialize and train a linear regression model
  • evaluate a regression model using metrics such as r-square and mean squared error and visualize its performance using Matplotlib
    work with ModelFrames for feature extraction and label encoding
    configure and build a clustering model using the K-Means algorithm and analyze data clusters to determine characteristics that are unique to them
    distinguish between the use of scikit-learn and Pandas ML when training a model and identify some of the metrics used to evaluate a model

IN THIS COURSE

  • Playable
    1. 
    Course Overview
    2m 16s
    UP NEXT
  • Playable
    2. 
    Data Preparation in scikit-learn
    6m 26s
  • Locked
    3. 
    Training and Evaluating Models in scikit-learn
    6m 56s
  • Locked
    4. 
    Introducing the Pandas ML ModelFrame
    5m 48s
  • Locked
    5. 
    Training and Evaluating Models in Pandas ML
    7m 28s
  • Locked
    6. 
    Preparing Data for Regression
    7m 32s
  • Locked
    7. 
    Evaluating Regression Models
    8m 21s
  • Locked
    8. 
    Preparing Data for Clustering
    4m 41s
  • Locked
    9. 
    The K-Means Clustering Algorithm
    7m 16s
  • Locked
    10. 
    Exercise: Regression, Classification, and Clustering
    6m 57s

EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE

Skillsoft is providing you the opportunity to earn a digital badge upon successful completion of this course, which can be shared on any social network or business platform

Digital badges are yours to keep, forever.

PEOPLE WHO VIEWED THIS ALSO VIEWED THESE

Likes 262 Likes 262  
COURSE SOLID & GRASP
Likes 3218 Likes 3218  
Likes 45 Likes 45