rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Python machine learning bootcamp
  name:
    name part: Schallwig, Maximilian
    role:
      role term: presenter.
  name:
    name part: Packt Publishing
    role:
      role term: publisher.
  type of resource: moving image
  genre: videorecording
  genre: Instructional films.
  genre: Nonfiction films.
  genre: Internet videos.
  origin info:
    place:
      place term: xx
    date issued: 2022
    edition: [First edition].
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: remote
    extent: 1 online resource (1 video file (24 hr.)) : sound, color.
  abstract: In this course, we will cover many different types of machine learning aspects. We will start by going through a sample machine learning project from an idea to developing a final working model. You will learn many important techniques around data preparation, cleaning, feature engineering, optimization and learning techniques, and much more. Once we have gone through the whole machine learning project, we will then dive deeper into several different areas of machine learning, to better understand each task, and how each of the models we can use to solve these tasks work, and then also using each model and understanding how we can tune all the parameters we learned about in the theory components. We will dive deeper into classification, regression, ensembles, dimensionality reduction, and unsupervised learning. At the end of this course, you should have a solid foundation of machine learning knowledge. You will be able to build machine learning solutions to different types of problems you will come across and be ready to start applying machine learning on the job or in technical interviews. What You Will Learn Learn how to take an ML idea and flush it out into a fully functioning project Learn the different types of ML approaches and the models within each section Get a theoretical and intuitive understanding of how each model works See the practical application and implementation for each model we cover Learn how to optimize models Learn the common pitfalls and how to overcome them Audience This course is designed for beginner Python programmers and data scientists who want to understand ML (Machine Learning) models in depth and be able to use them in practice. Basic Python knowledge is required and some previous experience with the Pandas and Matplotlib libraries will be helpful. About The Author Maximilian Schallwig: Maximilian Schallwig is a data engineer and a proficient Python programmer. He holds a bachelor's degree in physics and a master's degree in astroph…
  note: "Published in December 2022."
  note: Maximilian Schallwig, presenter.
  subject:
    topic: Machine learning
  classification: Q325.5
  classification: 006.3/1
  identifier: 9781804619049
  identifier: 1804619043
  identifier: 9781804619049 O'Reilly Media
  identifier: https://proxy.library.tamu.edu/login
  record info:
    record content source: ORMDA
    record creation date: 230110
    record change date: 20260128225205.9
    record identifier: in00004673544
    language of cataloging:
      language term: eng