The Supervised Machine Learning Bootcamp

About this course

Do you want to master supervised machine learning and land a job as a machine learning engineer or data scientist?This Supervised Machine Learning course is designed to equip you with the essential tools to tackle real-world challenges. You'll dive into powerful algorithms like Naïve Bayes, KNNs, Support Vector Machines, Decision Trees, Random Forests, and Ridge and Lasso Regression—skills every top-tier data professional needs.By the end of this course, you'll not only understand the theory behind these six algorithms, but also gain hands-on experience through practical case studies using Python’s sci-kit learn library. Whether you're looking to break into the industry or level up your expertise, this course gives you the knowledge and confidence to stand out in the field.First, we cover naïve Bayes – a powerful technique based on Bayesian statistics. Its strong point is that it’s great at performing tasks in real-time. Some of the most common use cases are filtering spam e-mails, flagging inappropriate comments on social media, or performing sentiment analysis. In the course, we have a practical example of how exactly that works, so stay tuned!Next up is K-nearest-neighbors – one of the most widely used machine learning algorithms. Why is that? Because of its simplicity when using distance-based metrics to make accurate predictions.We’ll follow up with decision tree algorithms, which will serve as the basis for our next topic – namely random forests. They are powerful ensemble learners, capable of harnessing the power of multiple decision trees to make accurate predictions.After that, we’ll meet Support Vector Machines – classification and regression models, capable of utilizing different kernels to solve a wide variety of problems. In the practical part of this section, we’ll build a model for classifying mushrooms as either poisonous or edible. Exciting!Finally, you’ll learn about Ridge and Lasso Regression

Course objectives

• equip students with essential machine learning tools
• prepare students for roles in data science or machine learning engineering
• provide practical experience with real-world challenges