The Top 5 Machine Learning Libraries in Python
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The Top 5 Machine Learning Libraries in Python

A Gentle Introduction to the Top Python Libraries used in Applied Machine Learning
4.2 (279 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
9,115 students enrolled
Created by Mike West
Last updated 5/2017
English
English
Price: Free
Includes:
  • 1.5 hours on-demand video
  • 11 Articles
  • 1 Supplemental Resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I 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.
View Curriculum
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 is the target audience?
  • If you're looking to learn machine learning then this course is for you.
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Curriculum For This Course
33 Lectures
01:41:15
+
Introduction
10 Lectures 33:17

Let's high level what this course is about. 

Course Introduction
01:59

Let's learn what the libraries in Python are. 

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

Let's find out. 

What's the Course About? What will I Learn?
03:38

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. 

Instructor Q & A
05:02

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. 

Machine Learning Vernacular
04:16

These are core terms you have to know. 

Must Know Terms Quiz
00:15

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

The Machine Modeling Process
04:33

There are two main version of Python. 

There is 2.X and 3.X. 

We will use 3.X in the course. 

Installing Python 3.X
04:15

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. 

Jupyter Notebook Anatomy
06:56

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

Course Downloads
01:25

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

Summary
00:58

Quiz
10 questions
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Pandas
5 Lectures 19:06

Let's walk through some pandas code. 

Before we can massage our data we need to import it. 

Let's learn how to do that in this lecture. 

Import Pandas and Manipulate Data
06:04

Let's begin working with tabular data. 

Think excel spreadsheet when you hear tabular data. 


Importing a CSV in Pandas
05:08

Let's continue massaging our data in Pandas. 

In this lesson we will drop some columns and sort some data. 

Remove Columns and Sort Some Data
05:03

Here's a tip that helps me cement what I've learned in Python courses. 

Learning Tip
01:54

Let's wrap up what we've learned in this section. 

Summary
00:57

Quiz
5 questions
+
NumPy
4 Lectures 12:30

What is an array. 

Let's define what an array is in very simple terms. 

Anatomy of an Array
04:21

Let's crate a simple array and inspect it. 

Inspecting simply means what do we know about our array after it's been created. 

Creating Arrays
03:52

Let's access the elements of our array. 

It's not difficult but there are some nuances we should know about. 

Accessing Elements in Our Array
03:26

Let's wrap up what we've learned in this section. 

Summary
00:51

Quiz
8 questions
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SciKit-Learn
6 Lectures 14:11

What is SciKit-Learn and why should we use it. 

What is SciKit-Learn?
03:23

In our example we are going to work with the Iris dataset. 

It's the hello world for machine learning. 

Data Sets
00:45

In this lesson let's walk through the entire process of building an end to end model. 

We will build a highly accurate model in less than 15 lines of code. 

An End to End Model
05:06

Let's walk through every line of code in this lesson. 

Anatomy of an End to End Model
03:40

Let's define the 4 core metrics seen in our model. 

What Does Accuracy Mean?
00:26

Let's wrap up what we've learned in this section. 

Summary
00:50

Quiz
5 questions
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matplotlib
3 Lectures 09:26

In this lecture let's work through two very simple examples of a line and scatter plot. 

The line and Scatter Plot
04:45

In this brief lesson let's craft a histogram. 

The Histogram
03:32

Let's wrap up what we've learned in this section. 

Summary
01:09

Quiz
6 questions
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NLTK
5 Lectures 13:03

Let's define what NLP is and how we can use NLTK to create models for text classification. 

What is NLP and NTLK?
04:39

This is from my course on tokenization but I think it will help you visualize the process. 

What is Tokenization?
02:44

Word and Sentence Tokenization
04:20

Summary
00:45

Quiz
5 questions

Bonus Lecture "Deep Learning"
00:34
About the Instructor
Mike West
4.1 Average rating
2,606 Reviews
43,190 Students
40 Courses
SQL Server and Machine Learning Evangelist

I've been a production SQL Server DBA most of my career.

I've worked with databases for over two decades. I've worked for or consulted with over 50 different companies as a full time employee or consultant. Fortune 500 as well as several small to mid-size companies. Some include: Georgia Pacific, SunTrust, Reed Construction Data, Building Systems Design, NetCertainty, The Home Shopping Network, SwingVote, Atlanta Gas and Light and Northrup Grumman.

Experience, education and passion

I learn something almost every day. I work with insanely smart people. I'm a voracious learner of all things SQL Server and I'm passionate about sharing what I've learned. My area of concentration is performance tuning. SQL Server is like an exotic sports car, it will run just fine in anyone's hands but put it in the hands of skilled tuner and it will perform like a race car.

Certifications

Certifications are like college degrees, they are a great starting points to begin learning. I'm a Microsoft Certified Database Administrator (MCDBA), Microsoft Certified System Engineer (MCSE) and Microsoft Certified Trainer (MCT).

Personal

Born in Ohio, raised and educated in Pennsylvania, I currently reside in Atlanta with my wife and two children.