Description
Machine Learning with Python for Everyone Course, Part 3: Fundamental Toolbox. To code machine learning problems, you need to go beyond the following discussions. Machine learning with Python for everyone. Part 3: Essential Toolbox shows how to turn introductory machine learning concepts into concrete code using Python, scikit-learn, and other tools. You will learn about fundamental classification and regression metrics such as decision tree classifiers and regressions, support vector machine classifiers and regression, logistic regression, penalized regression, and discriminant analysis. You’ll see feature design techniques including scaling, sampling, and interaction. You’ll 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 new to data science, a data analyst transitioning to using machine learning models, a research scientist looking to add machine learning techniques to your classic statistical training, or a manager looking to add data science/machine learning to your team.
Students should have a basic understanding of Python programming (variables, basic control flow, simple scripting). They should also have a basic knowledge of machine learning terminology (data set, training set, test set, model). They must have a Python installation that will allow them to use scikit-learn, pandas, matplotlib and seaborn.
What will you learn on the course?
- Use fundamental classification methods including decision trees, support vector classifiers, logistic regression, and discriminant analysis.
- Detecting bias and variability in classifiers
- Comparison of classifiers
- Use basic regression techniques, including penalized regression and regression trees.
- Determine bias and variability of regressors
- Manual feature engineering through feature scaling, discretization, categorical coding, interaction analysis, and target manipulations.
- Tuning Hyperparameters
- Use nested cross validation
- Pipeline development
Course Specifications: Machine Learning with Python for Everyone. Part 3. Basic set of tools
- Publisher: Oreily
- Instructor: Mark Fenner
- Level of training: from beginner to advanced
- Duration of training: 4 hours 36 minutes
Course headings
course images
Example video course
installation instructions
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English subtitles
Quality: 720p
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Password for file(s): www.downloadly.ir
size
1.2 GB