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: Practical data science using Python
  name:
    name part: Dasgupta, Manas
    role:
      role term: presenter.
  name:
    name part: Packt Publishing
    role:
      role term: publisher.
  type of resource: moving image
  genre: videorecording
  genre: Instructional films.
  genre: Internet videos.
  genre: Nonfiction films.
  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 (29 hr., 48 min.)) : sound, color.
  abstract: Explore data science using Python, statistical techniques, EDA, NumPy, Pandas, Scikit Learn, and Statsmodel libraries and take your first step toward becoming a data scientist or a machine learning engineer. About This Video Detailed coverage of Python for data science and machine learning Learn about model optimization using hyperparameter tuning Learn about unsupervised learning using K-Means clustering In Detail In this course, you will learn about core concepts of data science, exploratory data analysis, statistical methods, role of data, Python language, challenges of bias, variance and overfitting, choosing the right performance metrics, model evaluation techniques, model optimization using hyperparameter tuning and grid search cross validation techniques, and more. You will learn how to perform detailed data analysis using Python, statistical techniques, and exploratory data analysis, using various predictive modeling techniques such as a range of classification algorithms, regression models, and clustering models. You will learn the scenarios and use cases of deploying predictive models. This course also covers classification using decision trees, which include the Gini index and entropy measures and hyperparameter tuning. It covers the use of NumPy and Pandas libraries extensively for teaching exploratory data analysis. In addition, you will also explore advanced classification techniques and support vector machine predictions. There is also an introductory lesson included on Deep Neural Networks with a worked-out example on image classification using TensorFlow and Keras. By the end of the course, you will learn some basic foundations of data science using Python. Audience This course is for Python, machine learning developers, data scientists, data analysts, and business analysts. This course will also be beneficial for aspiring data science professionals and machine learning engineers. Exposure to programming languages will be useful.
  note: "Updated in August 2022."
  note: Manas Dasgupta, presenter.
  subject:
    topic: Quantitative research
  subject:
    topic: Python (Computer program language)
  subject:
    topic: Python (Computer program language)
  subject:
    topic: Quantitative research
  classification: QA76.9.Q36
  classification: 004.4/22
  identifier: 9781804611814
  identifier: 1804611816
  identifier: 9781804611814 O'Reilly Media
  identifier: https://proxy.library.tamu.edu/login
  record info:
    record content source: ORMDA
    record creation date: 220913
    record change date: 20260128202549.0
    record identifier: in00004672327
    language of cataloging:
      language term: eng