Oreilly – Machine Learning with Python for Everyone, Part 3: Fundamental Toolbox 2022-8

Oreilly – Machine Learning with Python for Everyone, Part 3: Fundamental Toolbox 2022-8 Downloadly IRSpace

Super Admin Super Admin
Apr 17, 2025 - 19:20 Updated: Apr 21, 2025 - 09:23
0 0
Oreilly – Machine Learning with Python for Everyone, Part 3: Fundamental Toolbox 2022-8
Oreilly – Machine Learning with Python for Everyone, Part 3: Fundamental Toolbox 2022-8

Course Machine Learning with Python for Everyone Part 3: Fundamental Toolbox. To code machine learning tasks you need to go beyond following discussions. Machine Learning with Python for Everyone Part 3: The Essential Toolbox shows you how to turn introductory machine learning concepts into concrete code using Python, scikit-learn, and friends. You will learn about fundamental classification and regression metrics such as decision tree classifiers and regressions, support vector classifiers and regression, logistic regression, penalized regression, and discriminant analysis. You’ll see techniques for engineering features, including scaling, discretization, and interactions. You will learn how to implement pipelines for more complex processing and nested cross-validation for setting hyperparameters. This course is suitable for anyone who needs to improve their basic understanding of machine learning concepts and become familiar with basic machine learning code. You may be a newer data scientist, a data analyst transitioning to using machine learning models, an R&D scientist looking to add machine learning techniques to your classic statistical training, or a manager who needs Adds Data Science/Machine Learning to your team.

Students should have a basic understanding of programming in Python (variables, basic control flow, simple scripts). They should also have basic familiarity with machine learning vocabulary (dataset, training set, test set, model). They should be running a Python installation that allows them to use scikit-learn, pandas, matplotlib, and seaborn.

What you will learn in the course

  • Use fundamental classification methods including decision trees, support vector classifiers, logistic regression and discriminant analysis.
  • Detect bias and variability in classifiers
  • Comparison of classifiers
  • Use basic regression methods including penalized regression and regression trees
  • Identify bias and variability in regressors
  • Manual engineering of features through feature scaling, discretization, categorical coding, interaction analysis, and target manipulations.
  • Setting hyperparameters
  • Use nested cross-validation
  • Develop pipelines

Course specifications Machine Learning with Python for Everyone Part 3: Fundamental Toolbox

  • Publisher: Oreilly
  • Instructor: Mark Fenner
  • Training level: beginner to advanced
  • Training duration: 4 hours 36 minutes

Course headings

Machine Learning with Python for Everyone Part 3: Fundamental Toolbox

Course images

Machine Learning with Python for Everyone Part 3: Fundamental Toolbox

Sample video of the course

Installation guide

After Extract, view with your favorite Player.

English subtitle

Quality: 720p

download link

Download part 1 – 1 GB

Download part 2 – 200 MB

File(s) password: www.downloadly.ir

Size

1.2 GB

Tags: