Low-code ML with KNIME: Building Classification Models

KNIME 4.7+    |    Intermediate
  • 16 videos | 2h 5m 15s
  • Includes Assessment
  • Earns a Badge
Rating 4.0 of 1 users Rating 4.0 of 1 users (1)
Classification models are used to categorize data into a fixed number of discrete classes or categories. The KNIME Analytics Platform allows you to load, explore, pre-process, and use your data to train classification models with little to no code. In this course, explore classification models and the metrics used to evaluate their performance. Next, construct a KNIME workflow to load and view the data for a classification model. You will clean data, impute missing values, and cap and floor outlier values in a range. Then you will identify and filter correlated variables and you will convert categorical data to numeric values and express numeric variables. Finally, train several different classification models on the training data, evaluate them using the test data, and select the best model using hyperparameter tuning. Upon completing this course, you will have the skills and knowledge to train, clean, and process your data and to use that data to train classification models and perform hyperparameter tuning.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Identify different classification models
    Read in data for classification and view statistics on that data
    Process missing and duplicate values
    Detect and remove outliers
    Remove correlated variables for machine learning
    Perform one-hot and label encoding
    Encode and split data for machine learning
  • Train a basic logistic regression model
    Standardize data to improve model performance
    Train an ensemble classifier
    Use synthetic minority oversampling technique (smote) to oversample data to improve model performance
    Configure the search space for hyperparameter tuning
    Perform hyperparameter tuning and view the results
    Install and use an xgboost classifier
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 1m 55s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 6m 16s
    After completing this video, you will be able to identify different classification models. FREE ACCESS
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    3.  Reading and Exploring the Classification Dataset
    8m 17s
    Discover how to read in data for classification and view statistics on that data. FREE ACCESS
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    4.  Removing Missing Values and Duplicate Data
    7m 17s
    In this video, find out how to process missing and duplicate values. FREE ACCESS
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    5.  Detecting and Removing Outliers
    9m 23s
    During this video, you will learn how to detect and remove outliers. FREE ACCESS
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    6.  Removing Correlated Variables
    8m 26s
    Upon completion of this video, you will be able to remove correlated variables for machine learning. FREE ACCESS
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    7.  Converting Categorical Data to Numeric Values
    8m 49s
    In this video, discover how to perform one-hot and label encoding. FREE ACCESS
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    8.  Preparing and Partitioning Data
    6m 55s
    Learn how to encode and split data for machine learning. FREE ACCESS
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    9.  Training a Logistic Regression Model
    9m 3s
    In this video, find out how to train a basic logistic regression model. FREE ACCESS
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    10.  Improving Model Performance using Normalization
    11m 11s
    During this video, discover how to standardize data to improve model performance. FREE ACCESS
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    11.  Training a Random Forest Classification Model
    10m 54s
    After completing this video, you will be able to train an ensemble classifier. FREE ACCESS
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    12.  Oversampling Training Data using SMOTE
    7m 33s
    Learn how to use synthetic minority oversampling technique (SMOTE) to oversample data to improve model performance. FREE ACCESS
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    13.  Configuring Search Space for Hyperparameter Tuning
    12m 13s
    In this video, discover how to configure the search space for hyperparameter tuning. FREE ACCESS
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    14.  Performing Hyperparameter Tuning
    7m 45s
    Find out how to perform hyperparameter tuning and view the results. FREE ACCESS
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    15.  Training an XGBoost Classification Model
    7m
    During this video, you will learn how to install and use an XGBoost classifier. FREE ACCESS
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    16.  Course Summary
    2m 20s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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