Prompt Engineering for Machine Learning

Generative AI    |    Beginner
  • 13 videos | 1h 43m 24s
  • Includes Assessment
  • Earns a Badge
Rating 5.0 of 1 users Rating 5.0 of 1 users (1)
Machine learning involves creating models that dynamically change based on the data from which they are created. Within machine learning, three fundamental problems-regression, classification, and clustering-are the focus of a variety of solution techniques. Begin this course by conducting regression analysis. You will analyze and visualize data to get a sense of the variables with predictive power, split data into training and test sets, and train a model. Then you will interpret the R-squared metric to evaluate how well the regression model has performed. Next, you will create a classification model for predicting categorical targets and split your data into test and training data to train a logistic regression model. You will also explore the impact of training a model on imbalanced data, and with generative artificial intelligence (AI) assistance, see how you can mitigate this by leveraging oversampling and undersampling techniques. Finally, you will perform clustering, train a k-means clustering model, and evaluate it using the silhouette and Davies-Bouldin scores. At course completion, you will have a good understanding of key concepts of machine learning and how to perform regression analysis, classification of data, and clustering.

WHAT YOU WILL LEARN

  • Discover the key concepts covered in this course
    Provide an overview of machine learning models
    Use regression models and view data
    Interpret relationships in data
    Train a regression model
    Analyze data for classification
    Split data and train a classification model
  • Evaluate the performance of classification models
    Train a classification model on an imbalanced dataset
    Balance out imbalanced data
    Train a clustering model
    Evaluate the performance of clustering
    Summarize the key concepts covered in this course

IN THIS COURSE

  • 2m 14s
    In this video, we will discover the key concepts covered in this course. FREE ACCESS
  • 6m 2s
    After completing this video, you will be able to provide an overview of machine learning models. FREE ACCESS
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    3.  Using Regression Analysis
    6m 10s
    During this video, you will learn how to use regression models and view data. FREE ACCESS
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    4.  Interpreting Relationships in Data with GPT-4
    9m 15s
    In this video, find out how to interpret relationships in data. FREE ACCESS
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    5.  Training a Regression Model with Google Bard's Help
    8m 17s
    During this video, discover how to train a regression model. FREE ACCESS
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    6.  Analyzing Data for Classification
    8m 19s
    Learn how to analyze data for classification. FREE ACCESS
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    7.  Preprocessing Data and Training Models
    9m 49s
    In this video, discover how to split data and train a classification model. FREE ACCESS
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    8.  Evaluating Classification Models with Prompt Engineering
    11m 10s
    Find out how to evaluate the performance of classification models. FREE ACCESS
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    9.  Training a Classification Model with an Imbalanced Dataset
    9m 46s
    During this video, you will learn how to train a classification model on an imbalanced dataset. FREE ACCESS
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    10.  Oversampling and Undersampling Data with Bard's Help
    9m 49s
    Discover how to balance out imbalanced data. FREE ACCESS
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    11.  Training a Clustering Model with Prompt Engineering
    10m 33s
    Learn how to train a clustering model. FREE ACCESS
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    12.  Evaluating Clustering Models with Generative AI Help
    9m 29s
    In this video, find out how to evaluate the performance of clustering. FREE ACCESS
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    13.  Course Summary
    2m 31s
    In this video, we will summarize the key concepts covered in this course. FREE ACCESS

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