Description
The teachings of this course make it easier to mastering the usage of resolution timber and random forests in your information evaluation tasks. The course focuses on resolution tree classifiers and random forest classifiers as a result of a lot of the profitable machine studying purposes seem like classification issues. The teachings clarify:
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Decision timber for classification issues.
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Parts of rising resolution timber.
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The sklearn parameters to outline resolution tree classifiers.
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Prediction with resolution timber utilizing Scikit-learn (becoming, pruning/tuning, investigating).
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The sklearn parameters to outline random forest classifiers.
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Prediction with random forests utilizing Scikit-learn (becoming, tuning, investigating).
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The concepts behind random forests for prediction.
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Traits of fitted resolution timber and random forests.
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Significance of information and understanding prediction efficiency.
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How one can perform a prediction challenge utilizing resolution timber and random forests.
Specializing in classification issues, the course makes use of the DecisionTreeClassifier and RandomForestClassifier strategies of Python’s Scikit-learn library. It prepares you for utilizing resolution timber and random forests to make predictions and understanding the predictive construction of information units.
That is what’s inside the teachings:
This course is for individuals who need to use resolution timber or random forests for prediction with Scikit-learn. This requires sensible expertise and the course facilitates you with Jupyter notebooks to overview and follow the teachings’ subjects.
Every lesson is a brief video to observe. Many of the classes clarify one thing about resolution timber or random forests with an instance in a Jupyter pocket book. The course supplies embody greater than 50 Jupyter notebooks and the corresponding Python code. You’ll be able to obtain the notebooks of the teachings for overview. You can even use the notebooks to strive different definitions of resolution timber and random forests or different information for additional follow.
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