Linear Classifiers in Python faq

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Python is a great language for machine learning, and linear classifiers are a great way to get started. Logistic regression and support vector machines (SVMs) are two of the most popular linear classifiers used in Python. In this course, you'll learn the fundamentals of using these models to solve classification problems. You'll gain an understanding of the conceptual framework that underpins logistic regression and SVMs, and learn about logistic regression in depth. You'll also learn everything there is to know about support vector machines, including tuning hyperparameters and using kernels to fit non-linear decision boundaries.

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Course Overview

❗The content presented here is sourced directly from Datacamp platform. For comprehensive course details, including enrollment information, simply click on the 'Go to class' link on our website.

Updated in [June 30th, 2023]

This course provides an introduction to linear classifiers in Python. Participants will learn the fundamentals of using logistic regression and support vector machines (SVMs) to solve classification problems. The conceptual framework that underpins logistic regression and SVMs will be discussed, as well as an in-depth look at logistic regression. Participants will also learn about support vector machines, including tuning hyperparameters and using kernels to fit non-linear decision boundaries. By the end of the course, participants will have a comprehensive understanding of linear classifiers in Python.

[Applications]
After completing this course, learners can apply the concepts of linear classifiers to solve classification problems. They can use logistic regression and support vector machines (SVMs) to classify data and tune hyperparameters for these models. Learners can also use kernels to fit non-linear decision boundaries. Additionally, learners can use the conceptual framework that underpins logistic regression and SVMs to gain a better understanding of the models.

[Career Path]
A career path recommended to learners of this course is a Machine Learning Engineer. Machine Learning Engineers are responsible for developing and deploying machine learning models to solve real-world problems. They must have a strong understanding of the fundamentals of machine learning, such as logistic regression and SVMs, as well as the ability to tune hyperparameters and use kernels to fit non-linear decision boundaries. Machine Learning Engineers must also be able to work with data scientists and software engineers to develop and deploy machine learning models.

The development trend for Machine Learning Engineers is very positive. As more and more companies are recognizing the value of machine learning, the demand for Machine Learning Engineers is increasing. Companies are looking for Machine Learning Engineers who can develop and deploy machine learning models quickly and efficiently. Additionally, Machine Learning Engineers must be able to work with data scientists and software engineers to ensure that the models are deployed correctly and are able to meet the company's needs.

[Education Path]
The recommended educational path for learners interested in Python and the Bottle Web Framework is to pursue a Bachelor's degree in Computer Science. This degree will provide learners with a comprehensive understanding of computer science fundamentals, such as algorithms, data structures, and software engineering. Learners will also gain an understanding of the principles of web development, including web architecture, web security, and web technologies.

In addition to the core computer science courses, learners should also take courses that focus on Python and the Bottle Web Framework. These courses will provide learners with the skills and knowledge necessary to create webapps in Python using the Bottle framework. Learners will learn how to design and develop web applications, create user interfaces, and integrate databases.

The development trend for this educational path is to focus on the latest technologies and frameworks. As the web development landscape continues to evolve, learners should stay up-to-date on the latest trends and technologies. This will ensure that they are able to create webapps that are secure, efficient, and user-friendly. Additionally, learners should also focus on developing their problem-solving and critical thinking skills, as these are essential for success in the field of computer science.

Course Syllabus

Applying logistic regression and SVM

Loss functions

Logistic regression

Support Vector Machines

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