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!!!!
What exactly are we going to learn in this class?
In this lesson let's get granular on what the course is about.
Are we data scientists?
Let's find out about this exciting sub-field of machine learning.
This library is becoming the most popular tool for real world predictive analytics.
There's a new easy button way to install Python and it's libraries.
Let's learn how in this lesson.
Rows are called rows in machine learning.
Let's learn what they are called.
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.
In this lecture and the next let's walk through an entire model.
A quick overview of the entire code set will provide us with a very high level overview of what we are trying to accomplish.
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.
In this lecture let's start building out our model.
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.
We are working our way through the model building process.
Let's continue to learn the process in this lesson.
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.
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 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).
Born in Ohio, raised and educated in Pennsylvania, I currently reside in Atlanta with my wife and two children.