Machine Learning Fundamentals

In this 3-hour course, you will dive into the fundamentals of machine learning using the scikit-learn library. From understanding data representation and applying clustering algorithms to implementing supervised learning techniques like Naïve Bayes, Decision Trees, and SVM, this course will give you the tools to develop and analyze machine learning models effectively.

What I will be able to do after this course

• Understand data representation and its importance in machine learning.
• Differentiate between supervised and unsupervised machine learning models.
• Explore and visualize data using the Matplotlib library.
• Apply clustering algorithms such as K-means, Gaussian Mixture, and Birch.
• Implement a confusion matrix and analyze results using scikit-learn.
• Use supervised learning algorithms like Naïve Bayes, Decision Trees, and SVM.
• Visualize model errors to improve the performance of machine learning algorithms.

Course Instructor(s)

Samik Sen, an expert in machine learning and data science, holds a PhD in Theoretical Physics. With extensive experience teaching high-performance computing and applying machine learning in various industries, Samik specializes in using R and Python for machine learning applications.

Who is it for?

This course is perfect for developers who are new to machine learning and want to learn how to implement machine learning algorithms using scikit-learn. Basic knowledge of Python programming is required, but no prior knowledge of machine learning is necessary.