Description
Course Machine Learning with Python for Everyone Part 3: Fundamental Toolbox. In this course you will learn how to code introductory machine learning concepts using Python, scikit-learn. You will learn about the principles and criteria of measuring regression models and types of machine learning models such as decision tree classifiers, regressors, support vector classifiers, regression, logistic regression, penalized regression, discriminant analysis. You will see techniques for feature engineering, including scaling, discretization, 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 understanding of machine learning concepts and become familiar with or code used in the ML domain.
Students should have a basic understanding of programming in Python (variables, control flow, simple scripts). They should also have basic knowledge of machine learning vocabulary (dataset, training set, test set, model). Students interested in this field must install a version of Python that allows them to use scikit-learn, pandas, matplotlib and seaborn in order to run the codes of this course.
What you will learn in the course
- From classification methods including decision trees, support vector classifiers, logistic regression, discriminant analysis
- Identify bias in classifier models
- Comparison of different models of Classifiers
- Learn about regression methods including penalized regression and regression trees
- Identify bias in regression models
- Manually engineer features through the methods of feature scaling, discretization, categorical coding, analysis of interactions, target manipulations
- Setting hyperparameters
- Use nested cross-validation
- Develop pipelines
Course specifications Machine Learning with Python for Everyone Part 3: Fundamental Toolbox
- Publisher: Oreilly
- teacher: Mark Fenner
- Training level: beginner to advanced
- Training duration: 4 hours 36 minutes
Course headings
Course images
Sample video of the course
Installation guide
After Extract, view with your favorite Player.
English subtitle
Quality: 720p
download link
File(s) password: www.downloadly.ir
Size
1.2 GB
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