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Applied Machine Learning: Feature Engineering

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Matt Harrison

1:41:44

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  • 01 - Applied ML Feature engineering.mp4
    00:49
  • 02 - What you should know.mp4
    01:10
  • 01 - Imputation.mp4
    04:58
  • 02 - Filling in missing values.mp4
    05:22
  • 03 - Binning.mp4
    06:08
  • 04 - Log transform.mp4
    06:14
  • 05 - Scaling.mp4
    02:13
  • 06 - Challenge Basic techniques.mp4
    01:33
  • 07 - Solution Basic techniques.mp4
    05:51
  • 01 - One hot encoding.mp4
    06:47
  • 02 - Hashing encoder.mp4
    02:29
  • 03 - Mean target encoding.mp4
    02:24
  • 04 - Challenge Categorical.mp4
    00:23
  • 05 - Solution Categorical.mp4
    06:11
  • 01 - PCA.mp4
    02:30
  • 02 - Feature aggregation.mp4
    02:07
  • 03 - TFIDF.mp4
    05:01
  • 04 - Text embeddings.mp4
    03:57
  • 05 - Challenge Feature extraction.mp4
    00:15
  • 06 - Solution Feature extraction.mp4
    02:12
  • 01 - Extracting date components.mp4
    00:50
  • 02 - Seasonality and trend decomposition.mp4
    06:15
  • 03 - Challenge Temporal features.mp4
    01:31
  • 04 - Solution Temporal features.mp4
    08:31
  • 01 - Importance and weights.mp4
    08:07
  • 02 - Recursive feature elimination.mp4
    02:15
  • 03 - Adding a random column.mp4
    01:29
  • 04 - Challenge Feature selection.mp4
    00:18
  • 05 - Solution Feature selection.mp4
    03:23
  • 01 - Next steps.mp4
    00:31

    • Description

    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.

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    Category

    Machine Learning

    Matt Harrison
    Matt Harrison
    Instructor's Courses
    Linkedin Learning
    Linkedin Learning
    View courses Linkedin Learning
    LinkedIn Learning is an American online learning provider. It provides video courses taught by industry experts in software, creative, and business skills. It is a subsidiary of LinkedIn. All the courses on LinkedIn fall into four categories: Business, Creative, Technology and Certifications. It was founded in 1995 by Lynda Weinman as Lynda.com before being acquired by LinkedIn in 2015. Microsoft acquired LinkedIn in December 2016.
    • language english
    • Training sessions 30
    • duration 1:41:44
    • English subtitles has
    • Release Date 2024/06/19

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