Are you interested in the field of machine learning? Then you have come to the right place and this course is exactly what you need!
In this course, you will learn the basics of various popular machine learning approaches through several practical examples. Various machine learning algorithms such as K-NN, Linear Regression, SVM, K-Means Clustering, and Decision Tree will be explained and implemented in Python. In this course, I try to share my knowledge and teach you the basics of the theories, algorithms, and programming libraries in a simple way. I will guide you step by step on your journey into the world of machine learning.
Each concept is introduced in plain English, avoiding confusing mathematical notation and jargon. It’s then demonstrated using Python code you can experiment with and build upon, along with notes you can keep for future reference. You won't find academic, deeply mathematical coverage of these algorithms in this course - the focus is on practical understanding and application of them. This course will teach you the basic techniques used by real-world industry data scientists. I'll cover the fundamentals of machine learning techniques that are essential for real-world problems, including:
Support Vector Machines
Basic knowledge of Python programming
You'll need a desktop computer (Windows, Mac, or Linux) capable of running Anaconda 3 or newer. The course will walk you through installing the necessary free software.