A Gentle Introduction to Machine Learning Using SciKit-Learn
4.2 (533 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
12,419 students enrolled

A Gentle Introduction to Machine Learning Using SciKit-Learn

How to use Scikit-Learn to buid a supervised learning model
4.2 (533 ratings)
Course Ratings are calculated from individual students’ ratings and a variety of other signals, like age of rating and reliability, to ensure that they reflect course quality fairly and accurately.
12,419 students enrolled
Created by Mike West
Last updated 4/2017
English
English [Auto-generated]
Price: Free
This course includes
  • 1 hour on-demand video
  • 2 articles
  • 1 downloadable resource
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What you'll learn
  • At the end of the course you'll understand how to create an end to end model using Python's SciKit_Learn.
  • You'll understand the nomenclature and process when creating a solution in SciKit_Learn.

  • You will also have a Jupyter Notebook that's annotated with all the important points in the course.

  • You will also receive a completed Jupyter Notebook filled with models and references.
Requirements
  • You'll need to understand the basics of Python.
Description

Welcome to A Gentle Introduction to Machine Learning Using SciKit-Learn

In this course, we going to build an end-to-end Python machine learning project.  You’ll learn how to use Scikit-Learn to build and tune a supervised learning model. 

Scikit-learn was initially developed by David Cournapeau as a Google summer of code project in 2007 and since then has become the de facto library used for machine learning in Python.                               

Python is one of the most popular languages for machine learning and in the course we’ll gently introduce you to SciKit-Learn, a library designed for working with machine learning projects.

Scikit-Learn, also known as sklearn, is Python's premier general-purpose machine learning library.  Scikit-Learn's versatility makes it the best starting place for most ML problems.

Scikit-Learn is great for beginners it offers a high-level interface for many tasks. This allows you to better practice the entire machine learning workflow and understand the big picture.

We will also gently introduce you to the vernacular of machine learning. For example, a target variable is simply that thing we are trying to predict. A feature is often no more than a column in at table.

You’ll get hands on experience with the process of machine learning. The process involves importing data, cleaning the data, training and testing, pre-processing and feature engineering.

We are going to define new terms but we will skip the math and theory for now.

Thanks for your interest in A Gentle Introduction to Machine Learning Using SciKit-Learn. 

See you in the course!!!!

Who this course is for:
  • One of the most prominent machine learning languages is Python. You are going to learn how to craft an end to end machine learning model using SciKit_Learn.
Course content
Expand all 17 lectures 58:00
+ Introduction
9 lectures 24:44

What are we going to learn in this course? 

What is SciKit-Learn?

Introduction
01:34

What exactly are we going to learn in this class? 

In this lesson let's get granular on what the course is about. 

What is our Goal?
01:47

Are we data scientists? 

Let's find out about this exciting sub-field of machine learning. 

Predictive Modeling
03:22

This library is becoming the most popular tool for real world predictive analytics.

Why use scikit-learn?
02:59

There's a new easy button way to install Python and it's libraries. 

Let's learn how in this lesson. 

Installing Python and SciKit Learn
04:12

Rows are called rows in machine learning. 

Let's learn what they are called. 

Terminology
01:37

Let's learn how to use our IDE. 

The Jupyter Notebook is one of the easiest environments to learn Python in and it's what most machine learning practitioners use on a daily basis to create their models. 

Jupyter Notebook Anatomy
06:56
Course Downloads
01:36
Summary
00:41
Quiz
10 questions
+ Building Our Model
8 lectures 33:16

In this lecture and the next let's walk through an entire model. 

An End to End Model Walk through - Part 1
06:10

A quick overview of the entire code set will provide us with a very high level overview of what we are trying to accomplish. 

An End to End Model Walk through - Part 2
05:20

It's nice to work with all the clean data sets we use while we are learning predictive modeling. 

However, that's not what happens in the real world. 

All Canned Data is Clean
02:44

In this lecture let's start building out our model. 

Building the Model - Part 1
07:56

Let's continue building out the model. 

What's an array and why is a balanced target variable important if we are going to use accuracy as our evaluation metric. 

Building the Model - Part 2
04:04

We are working our way through the model building process. 

Let's continue to learn the process in this lesson. 

Building the Model - Part 3
02:55

The final step in our model building process is training of fitting our model. 

In the final lesson on building our model let's learn how to do that. 

Building the Model - Part 4
03:04
Summary
01:03
Quiz
10 questions