Building Classification Models with scikit-learn

Building Classification Models with scikit-learn
.MP4, AVC, 1280×720, 30 fps | English, AAC, 2 Ch | 2h 33m | 303 MB
Instructor: Janani Ravi

This course covers several important techniques used to implement classification in scikit-learn, starting with logistic regression, moving on to Discriminant Analysis, Naive Bayes and the use of Decision Trees, and then even more advanced techniques such as Support Vector Classification and Stochastic Gradient Descent Classification.
Perhaps the most ground-breaking advances in machine learning have come from applying machine learning to classification problems. In this course, Building Classification Models with scikit-learn you will gain the ability to enumerate the different types of classification algorithms and correctly implement them in scikit-learn. First, you will learn what classification seeks to achieve, and how to evaluate classifiers using accuracy, precision, recall, and ROC curves. Next, you will discover how to implement various classification techniques such as logistic regression, and Naive Bayes classification. You will then understand other more advanced forms of classification, including those using Support Vector Machines, Decision Trees and Stochastic Gradient Descent. Finally, you will round out the course by understanding the hyperparameters that these various classification models possess, and how these can be optimized. When you’re finished with this course, you will have the skills and knowledge to select the correct classification algorithm based on the problem you are trying to solve, and also implement it correctly using scikit-learn.

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