Applied Machine Learning : Feature Engineering / with Matt Harrison.

Format:

Video

Author/Creator:

Harrison, Matthew, speaker.

Contributor:

linkedin.com (Firm)

Language:

English

Genre:

Instructional films.

Educational films.

Physical Description:

1 online resource

Place of Publication:

Carpenteria, CA : linkedin.com, 2024.

System Details:

Latest version of the following browsers: Chrome, Safari, Firefox, or Internet Explorer. Adobe Flash Player Plugin. JavaScript and cookies must be enabled. A broadband Internet connection.

Summary:

Extract the maximum value from your data using feature engineering. Learn how to clean, normalize, and create features to improve the performance of your machine learning models.

Machine learning is not magic. The quality of the predictions coming out of your model is a direct reflection of the data you feed it during training. This course with instructor Matt Harrison guides you through the nuances of feature engineering techniques for numeric data so you can take a dataset, tease out the signal, and throw out the noise in order to optimize your machine learning model. Matt teaches you techniques like imputation, binning, log transformations, and scaling for numeric data. He covers methods for other types of data, like as one hot encoding, mean targeting coding, principal component analysis, feature aggregation, and text processing techniques like TFIDF and embeddings. The tools you learn in this course will generalize to nearly any kind of machine learning algorithm/problem, so join Matt in this course to learn how you can extract the maximum value from your data using feature engineering.

Participant:

Presenter: Matt Harrison

Notes:

4/16/202412:00:00AM

Access Restriction:

Restricted for use by site license.