Beginners Guide to Machine Learning – Python, Keras, SKLearn

Gain a foundational understanding of machine learning

Implement both supervised and unsupervised machine learning models

Measure the performances of different machine learning models using the suitable metrics

Understand which machine learning model to use in which situation

Reduce data of higher dimensions to data of lower dimensions using principal component analysis


  • A windows machine, and a willingness to learn


In this course, we will cover the foundations of machine learning. The course is designed to not beat around the bush, and cover exactly what is needed concisely and engagingly. Based on a university level machine learning syllabus, this course aims to effectively teach, what can sometimes be dry content, through the use of entertaining stories, professionally edited videos, and clever scriptwriting. This allows one effectively absorb the complex material, without experiencing the usual boredom that can be experienced when trying to study machine learning content.   

The course first goes into a very general explanation of machine learning. It does this by telling a story that involves an angry farmer and his missing donuts. This video sets the foundation for what is to come.

After a general understanding is obtained, the course moves into supervised classification. It is here that we are introduced to neural networks through the use of a plumbing system on a flower farm.

Thereafter, we delve into supervised regression, by exploring how we can figure out whether certain properties are value for money or not.

We then cover unsupervised classification and regression by using other farm-based examples.

This course is probably the best foundational machine learning course out there, and you will definitely benefit greatly from it.

Who this course is for:

  • Beginners to machine learning. College students looking to improve their capability. Professionals looking to implement machine learning in their day to day business.

Course content

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