{"id":1869,"date":"2026-08-10T08:52:30","date_gmt":"2026-08-10T08:52:30","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-the-supervised-machine-learning-bootcamp-2025-8\/"},"modified":"2026-08-10T08:52:30","modified_gmt":"2026-08-10T08:52:30","slug":"udemy-the-supervised-machine-learning-bootcamp-2025-8","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-the-supervised-machine-learning-bootcamp-2025-8\/","title":{"rendered":"Udemy \u2013 The Supervised Machine Learning Bootcamp 2025-8"},"content":{"rendered":"
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Description<\/span><\/h2>\n

The Supervised Machine Learning Bootcamp course. Why should you consider a supervised machine learning course? The supervised machine learning algorithms you\u2019ll learn here are some of the most powerful data science tools you\u2019ll need to solve regression and classification tasks. These are valuable skills that anyone who wants to work as a machine learning engineer and data scientist should have in their toolbox. Simple Bayes, KNN, Support Vector Machines, Decision Trees, Random Forests, Ridge and Lasso Regression. In this course, you\u2019ll learn the theory behind all 6 algorithms and then apply your skills to practical case studies tailored to each of them using the Python Science Kit learning library. First, we cover simple Bayes\u2014a powerful technique based on Bayesian statistics. Its strength is that it excels at performing tasks in real time. Some of the most common uses are filtering spam emails, flagging inappropriate comments on social media, or performing sentiment analysis. In this course, we have a working example of exactly how it works, so stay tuned! Next is K-nearest-neighbors \u2013 one of the most widely used machine learning algorithms. Why is that? Because of its simplicity when using distance-based criteria for accurate prediction. We follow decision tree algorithms, which will serve as the basis for our next topic \u2013 random forests. They are powerful learners that can harness the power of multiple decision trees to make accurate predictions. After that, we come across Support Vector Machines \u2013 classification and regression models that are able to use different kernels to solve a wide variety of problems. In the practical part of this section, we will build a model to classify mushrooms as poisonous or edible. Exciting! Finally, you will be introduced to Ridge and Lasso Regression \u2013 they are regularization algorithms that improve the mechanism of linear regression by limiting the strength of individual features and preventing overfitting. We will discuss the differences and similarities as well as the advantages and disadvantages of both regression techniques. Each section of the course is organized in a consistent manner for an optimal learning experience:<\/span><\/p>\n

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\u2013 We start with the fundamental theory for each algorithm. To enhance your understanding of the topic, we will guide you through a theoretical case and also introduce the mathematical formulas behind the algorithm.<\/span><\/p>\n

\u2013 Then we go to the construction of the model to solve the practical problem with it. This work is done using the famous Sklaren Python library.<\/span><\/p>\n

\u2013 We analyze the performance of our models with the help of metrics such as accuracy, precision, recall and F1 score.<\/span><\/p>\n

\u2013 We also study various techniques such as network search and cross-validation to improve model performance.<\/span><\/p>\n

To top it all off, we have a series of complementary exercises and quizzes so you can expand your skill set. Not only that, but we also provide comprehensive course materials to guide you through the course that you can refer to anytime. These lessons are created in the unique 365 teaching style that many of you are familiar with. Our goal is to present complex topics in an easy and understandable way, with a focus on practical application and visual learning. With the power of animations, quizzes, exercises and well-crafted course notes, the supervised machine learning course will meet all your learning needs.<\/span><\/p>\n<\/div>\n

What you will learn in The Supervised Machine Learning Bootcamp<\/span><\/h3>\n
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