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Machine Learning with Scikit-Learn

via YouTube

Overview

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This course on Machine Learning with Scikit-Learn aims to teach learners the fundamentals of machine learning using the Scikit-Learn library and Python. By the end of the course, students will be able to understand and implement various machine learning algorithms such as K-Nearest Neighbors, Logistic Regression, Decision Trees, and Neural Networks. The course covers topics like data preprocessing techniques including scaling, normalization, and one-hot encoding, as well as automatic feature selection. The teaching method involves a hands-on approach with practical examples and exercises. This course is intended for individuals interested in learning machine learning concepts and applying them using the Scikit-Learn library in Python.

Syllabus

Hands-On Machine Learning with Scikit-Learn and Python - Course Preview.
Machine Learning with Scikit-Learn - The Dataset - 1.
Machine Learning with Scikit-Learn - Part 2.
Machine Learning with Scikit-Learn - Part 3.
Machine Learning with Scikit-Learn - Part 5 - KNN 1.
Machine Learning with Scikit-Learn - Part 8 - Logistic Regression 1.
Machine Learning with Scikit-Learn - Part 11 - Decision Trees 1.
Machine Learning with Scikit-Learn - Part 19 - Neural Networks 1.
Machine Learning with Scikit-Learn - Part 37 - Preprocessing - Scaling.
Machine Learning with Scikit-Learn - Part 38 - Preprocessing - Normalization.
Machine Learning with Scikit-Learn - Part 40 - One Hot Encoding 1.
Machine Learning with Scikit-Learn - Part 42 - Automatic Feature Selection 1.

Taught by

Cristi Vlad

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