A Gentle Introduction to the Top Python Libraries used in Applied Machine Learning


A Gentle Introduction to the Top Python Libraries used in Applied Machine Learning” offers an overview of key Python libraries essential for building real-world predictive models. The course includes an annotated Jupyter Notebook and covers the supervised predictive modeling process. While no prerequisites are required, familiarity with Python and basic machine learning concepts is beneficial. The course emphasizes the role of data scientists in extracting actionable insights from vast amounts of data using predictive models. It also highlights the importance of machine learning engineers who implement these models. The course is ideal for those looking to enter the field of machine learning and data science.


What You’ll Learn

  • You’ll receive the completely annotated Jupyter Notebook used in the course.
  • You’ll be able to define and give examples of the top libraries in Python used to build real world predictive models.
  • You will be able to create models with the most powerful language for machine learning there is.
  • You’ll understand the supervised predictive modeling process and learn the core vernacular at a high level.



  • There are no prerequisites however knowledge of Python will be helpful.
  • A familiarity with the concepts of machine learning would be helpful but aren’t necessary.


Recent Review from Similar Course:

“This was one of the most useful classes I have taken in a long time. Very specific, real-world examples. It covered several instances of ‘what is happening’, ‘what it means’ and ‘how you fix it’. I was impressed.”  Steve

Welcome to The Top 5 Machine Learning Libraries in Python.  This is an introductory course on the process of building supervised machine learning models and then using libraries in a computer programming language called Python.

What’s the top career in the world? Doctor? Lawyer? Teacher? Nope. None of those.

The top career in the world is the data scientist. Great. What’s a data scientist?

The area of study which involves extracting knowledge from data is called Data Science and people practicing in this field are called as Data Scientists.

Business generate a huge amount of data.  The data has tremendous value but there so much of it where do you begin to look for value that is actionable? That’s where the data scientist comes in.  The job of the data scientist is to create predictive models that can find hidden patterns in data that will give the business a competitive advantage in their space.

Don’t I need a PhD?  Nope. Some data scientists do have PhDs but it’s not a requirement.  A similar career to that of the data scientist is the machine learning engineer.

machine learning engineer is a person who builds predictive models, scores them and then puts them into production so that others in the company can consume or use their model.  They are usually skilled programmers that have a solid background in data mining or other data related professions and they have learned predictive modeling.

In the course we are going to take a look at what machine learning engineers do. We are going to learn about the process of building supervised predictive models and build several using the most widely used programming language for machine learning. Python. There are literally hundreds of libraries we can import into Python that are machine learning related.

library is simply a group of code that lives outside the core language. We “import it” into our work space when we need to use its functionality. We can mix and match these libraries like Lego blocks.

Thanks for your interest in the The Top 5 Machine Learning Libraries in Python and we will see you in the course.


Who this course is for:

  • If you’re looking to learn machine learning then this course is for you.


Enroll For Free Now