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The Top 5 Machine Learning Libraries in Python
Rating: 4.7 out of 5(4,588 ratings)
113,326 students

The Top 5 Machine Learning Libraries in Python

A Gentle Introduction to the Top Python Libraries used in Applied Machine Learning
Created byMike West
Last updated 4/2025
English

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.

Course content

6 sections • 32 lectures • 1h 40m total length

  • What's the Course About? What will I Learn?3:38

    Let's learn what the libraries in Python are. 

    How can we use these libraries to build machine learning models? 

    Let's find out. 

  • Instructor Q & A4:54

    Here are a few questions that might help you. 

    These are questions I've seen asked often on Quora and other data science boards. 

    I try not to sugar coat any of my answers. 

  • Machine Learning Vernacular4:16

    All fields have their own vernacular. 

    In order to start learning the basics there are a few terms we must know. 

    Let's learn them in this lesson. 

  • Must Know Terms Quiz0:12

    These are core terms you have to know. 

  • The Machine Modeling Process4:33

    In this lesson let's walk through the supervised machine learning process. 

  • Installing Python 3.X4:15

    There are two main version of Python. 

    There is 2.X and 3.X. 

    We will use 3.X in the course. 

  • Jupyter Notebook Anatomy6:56

    Our IDE in Python will be a Jupyter Notebook. 

    Let's find out how to work with the gui. 

    It's really easy to use. 

  • Course Downloads1:25

    This is where you will download the content that comes with the course. 

  • Summary0:57

    Let's wrap up what we covered in this section. 

  • Quiz

Requirements

  • 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.

Description

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.

A 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.

A 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.

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