Prompt Engineering for Machine Learning
Generative AI
| Beginner
- 13 videos | 1h 43m 24s
- Includes Assessment
- Earns a Badge
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 14sIn this video, we will discover the key concepts covered in this course. FREE ACCESS
-
6m 2sAfter completing this video, you will be able to provide an overview of machine learning models. FREE ACCESS
-
3. Using Regression Analysis6m 10sDuring this video, you will learn how to use regression models and view data. FREE ACCESS
-
4. Interpreting Relationships in Data with GPT-49m 15sIn this video, find out how to interpret relationships in data. FREE ACCESS
-
5. Training a Regression Model with Google Bard's Help8m 17sDuring this video, discover how to train a regression model. FREE ACCESS
-
6. Analyzing Data for Classification8m 19sLearn how to analyze data for classification. FREE ACCESS
-
7. Preprocessing Data and Training Models9m 49sIn this video, discover how to split data and train a classification model. FREE ACCESS
-
8. Evaluating Classification Models with Prompt Engineering11m 10sFind out how to evaluate the performance of classification models. FREE ACCESS
-
9. Training a Classification Model with an Imbalanced Dataset9m 46sDuring this video, you will learn how to train a classification model on an imbalanced dataset. FREE ACCESS
-
10. Oversampling and Undersampling Data with Bard's Help9m 49sDiscover how to balance out imbalanced data. FREE ACCESS
-
11. Training a Clustering Model with Prompt Engineering10m 33sLearn how to train a clustering model. FREE ACCESS
-
12. Evaluating Clustering Models with Generative AI Help9m 29sIn this video, find out how to evaluate the performance of clustering. FREE ACCESS
-
13. Course Summary2m 31sIn this video, we will summarize the key concepts covered in this course. FREE ACCESS
EARN A DIGITAL BADGE WHEN YOU COMPLETE THIS COURSE
Skillsoft is providing you the opportunity to earn a digital badge upon successful completion on some of our courses, which can be shared on any social network or business platform.
Digital badges are yours to keep, forever.
