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Fundamentals of Machine Learning: A Pythonic Introduction

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level

Beginner

Duration

14h

Lessons

65 Lessons

Category:

Learn the key concepts of machine learning and implement algorithms in Python for practical applications.

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This course focuses on core concepts, algorithms, and machine learning techniques. It explores the fundamentals, implements algorithms from scratch, and compares the results with scikit-learn, the Python machine learning library. This course contains examples, theoretical knowledge, and codes for various ML algorithms.

You’ll start by learning the essentials of machine learning and its applications. Then, you’ll learn about supervised learning, clustering, and constructing a bag of visual words project, followed by generalized linear regression, support vector machines, logistic regression, ensemble learning, and principal component analysis. You’ll also learn about autoencoders and variational autoencoders and end with three exciting projects.

By the end, you’ll have a solid understanding of machine learning and its algorithms, hands-on experience implementing such algorithms and applying them to different problems, and an understanding of how each algorithm works with the provided examples.

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Fundamentals of Machine Learning: A Pythonic Introduction
Fundamentals of Machine Learning: A Pythonic Introduction
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