Machine Learning for Data Science
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Machine Learning for Data Science

A primer on Machine Learning for Data Science. Revealed for everyday people, by the Backyard Data Scientist.
4.5 (102 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.
3,464 students enrolled
Created by David Valentine
Last updated 1/2017
English [Auto-generated]
Current price: $12 Original price: $95 Discount: 87% off
3 days left at this price!
30-Day Money-Back Guarantee
  • 3 hours on-demand video
  • 2 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion

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What Will I Learn?
  • Genuinely understand what Computer Science, Algorithms, Programming, Data, Big Data, Artificial Intelligence, Machine Learning, and Data Science is.
  • To understand how these different domains fit together, how they are different, and how to avoid the marketing fluff.
  • The Impacts Machine Learning and Data Science is having on society.
  • To really understand computer technology has changed the world, with an appreciation of scale.
  • To know what problems Machine Learning can solve, and how the Machine Learning Process works.
  • How to avoid problems with Machine Learning, to successfully implement it without losing your mind!
View Curriculum
  • A passion to learn, and basic computer skills!
  • Students should understand basic high-school level mathematics, but Statistics is not required to understand this course.

Course Launched On Nov 11/2016! 

Thank you all for the huge response to this emerging course!  We are delighted to have over 2300 students in over 102 different countries and for the overwhelmingly positive and thoughtful reviews.  It's such a privilege to share this important topic with everyday people in a clear and understandable way. 

Unlock the secrets of understanding Machine Learning for Data Science!

In this introductory course, the “Backyard Data Scientist” will guide you through wilderness of Machine Learning for Data Science.  Accessible to everyone, this introductory course not only explains Machine Learning, but where it fits in the “techno sphere around us”, why it’s important now, and how it will dramatically change our world today and for days to come.

Our exotic journey will include the core concepts of:

  • The train wreck definition of computer science and one that will actually instead make sense. 
  • An explanation of data that will have you seeing data everywhere that you look!
  • One of the “greatest lies” ever sold about the future computer science.
  • A genuine explanation of Big Data, and how to avoid falling into the marketing hype.
  • What is Artificial intelligence?  Can a computer actually think?  How do computers do things like navigate like a GPS or play games anyway?
  • What is Machine Learning?  And if a computer can think – can it learn? 
  • What is Data Science, and how it relates to magical unicorns!
  • How Computer Science, Artificial Intelligence, Machine Learning, Big Data and Data Science interrelate to one another. 

We’ll then explore the past and the future while touching on the importance, impacts and examples of Machine Learning for Data Science:

  • How a perfect storm of data, computer and Machine Learning algorithms have combined together to make this important right now.
  • We’ll actually make sense of how computer technology has changed over time while covering off a journey from 1956 to 2014.  Do you have a super computer in your home?  You might be surprised to learn the truth.
  • We’ll discuss the kinds of problems Machine Learning solves, and visually explain regression, clustering and classification in a way that will intuitively make sense.
  • Most importantly we’ll show how this is changing our lives.  Not just the lives of business leaders, but most importantly…you too!

To make sense of the Machine part of Machine Learning, we’ll explore the Machine Learning process:

  • How do you solve problems with Machine Learning and what are five things you must do to be successful?
  • How to ask the right question, to be solved by Machine Learning.
  • Identifying, obtaining and preparing the right data … and dealing with dirty data!
  • How every mess is “unique” but that tidy data is like families! 
  • How to identify and apply Machine Learning algorithms, with exotic names like “Decision Trees”, “Neural Networks” “K’s Nearest Neighbors” and “Naive Bayesian Classifiers”
  • And the biggest pitfalls to avoid and how to tune your Machine Learning models to help ensure a successful result for Data Science.

Our final section of the course will prepare you to begin your future journey into Machine Learning for Data Science after the course is complete.  We’ll explore:

  • How to start applying Machine Learning without losing your mind.
  • What equipment Data Scientists use, (the answer might surprise you!)
  • The top five tools Used for data science, including some surprising ones. 
  • And for each of the top five tools – we’ll explain what they are, and how to get started using them. 
  • And we’ll close off with some cautionary tales, so you can be the most successful you can be in applying Machine Learning to Data Science problems.

So I invite you to join me, the Backyard Data Scientist on an exquisite journey into unlocking the secrets of Machine Learning for Data Science.... for you know - everyday people... like you!

Sign up right now, and we'll see you – on the other side!

Who is the target audience?
  • Before you load Python, Before you start R - you need this course. This introductory course will introduce you to the Fundamentals, that you need before you start getting "Hands on".
  • Anyone interested in understanding how Machine Learning is used for Data Science.
  • Including business leaders, managers, app developers, consumers - you!
  • Adventurous folks, whom are ready to strap themselves into the exotic world of Data Science and Machine Learning.
Compare to Other Data Science Courses
Curriculum For This Course
38 Lectures
4 Lectures 11:28

Why should you buy this course?  Begin here to see what we'll cover and what this course will bring to you!

Preview 02:00

My personal thank you, for entrusting me with your time.  It's a privilege to share this amazing topic with you.

Preview 00:50

A taste of what's to come - the course overview outlines what we'll be discussing, in each section of this course.

Preview 03:13

SECRET SAUCE!: Top tips on how to get the most out of this course!  Don't skip this lecture - it's worth your time!

Preview 05:25
Core Concepts
11 Lectures 01:06:58

What will we discover with core concepts?  Here I'll give you a brief overview of all the exciting lectures contained in this section.

Core Concepts Overview

The current definition of computer science is an incomprehensible train wreck!  Find out why in this lecture! 

Computer Science - the `Train Wreck' Definition

In order to better understand what computer science is, it's useful to understand what DATA is.  By the end of this lecture you'll be able to see DATA EVERYWHERE you look! 

What's Data / "I can see data everywhere!"

There are two different kinds of data - Structured and Unstructured.  This is a key concept, that we are going to come back to time and time again later on.  Important, and delivered in under 3 minutes! 

Structured vs Unstructured Data

Test your understanding of structured vs. unstructured data in this quick quiz!

Structured and Unstructured Data
2 questions

Here we revisit the definition of what Computer Science is, with something that's actually comprehensible.  Wondering what an algorithm is?  We've got that covered to?  And while we're at it - we'll even dive into programming.

Finally, we'll touch on what I call "One of the greatest lies - Ever SOLD".

Computer Science - Definition Revisited & The Greatest "lie" ever SOLD....

So what is Big Data?  Learn the three V's of big data, what it is... and what it isn't!  

This lecture will educate you so you don't fall for the "marketing hype" often associated with Big Data. 

What's big data?

A quiz on the ideas of big data.

Big Data - Quiz
2 questions

This is a longer lecture, however within 12 minutes we'll cover off the most fundamental parts of Artificial Intelligence. 

Do you how a computer plans a route in a GPS?  Or how it would play a game like Tic-Tac-Toe?  The answers might surprise you!  This lecture has several animations to help illustrate the concepts and importantly - the challenges of AI in search. 

And!  We'll also cover off one of the most interesting questions - "Can a computer Really Think"?

What is Artificial Intelligence (AI)

Alas!  We are discussing Machine Learning!  In this lecture, we'll clearly define Machine Learning.  We'll give a simplified overview of the Machine Learning Process, which we'll expand later on in section 4.  We'll discuss some applications of Machine Learning, as well as what Machine Learning gives AI.

By the end of this lecture, you'll have an idea of what Machine Learning can be used for.

What is Machine Learning? - Part 1 - The ideas

In this Animated Example, we'll show a simple Machine Learning application.  While it's a very simple example, it will show how data can be looked at, examined for patterns, and will discuss the difference between sensitivity and specificity.  These are key concepts to Machine Learning and important to understand when applying it.

What is Machine Learning? - Part 2 - An Example

What is Data Science?  Magical Unicorns?  (Yes really!).  Battling Venn Diagrams (I'm not kidding!)

In this lecture, we'll define what Data Science is and what a Data Scientist does. 

What is data science?

Big Data!  AI!  Machine Learning!  Computer Science!  Data Science!  

How does this all fit together?  Where does one "start" and the other "stop?"  In this lecture, we'll use an animated diagram to explain how all these different domains interrelate.  Confusion stops here! 

Recap & How do these relate to each other?
Impacts, Importance and examples
5 Lectures 35:07

What will we discover with "Impacts, Importance and Examples"?  Here I'll give you a brief overview of all the exciting lectures contained in this section.

Impacts, Importance and examples - Overview

Why are we talking about this?  Why is this important now! 

In this lecture we'll uncover the convergence of events that have come together in a perfect storm of digital change. 

Why is this important now?

Computers exploding?!  Every one always gives lip service to "how much technology has actually changed".   But what does it really mean?  In this longer lecture, we'll take a journey from 1956 to 2014, and really explain how the world has changed.

Do you have a super computer in your house?  You might be surprised to find out the truth.....!

Computers exploding! - The explosive growth of computer power explained.

In this brief lecture, we'll cover the three different problems Machine Learning solves really well. 

  • Classification
  • Clustering
  • Regression

Pictures will help make sense of every concept, and it will be the bedrock for later seeing how different problems can be solved by Machine Learning.  While watching this lecture, be sure to look at how a problem can be solved in different ways, using different approaches to Machine Learning.

What problems does Machine Learning Solve?

We've covered off - what it is.  How it works.  What it provides....

Now the question is How is this changing our lives?

In this lecture we'll talk about what we'll likely see.  What happens when Machine Learning goes wrong.  And we'll touch on ethics - which is not just a case of banning killer robots, but much more subtle as well.

Where it's transforming our lives
The Machine Learning Process
7 Lectures 34:29

What will we discover with "The Machine Learning Process"?  Here I'll give you a brief overview of all the exciting lectures contained in this section.

The Machine Learning Process - Overview

In this lecture we'll cover off each of the five step of the Machine Learning Process, sometimes called a "pipeline" or "workflow".  Any problem being solved by Machine Learning will have to touch all of these fives steps - sometimes more than once. 

This key lecture will discuss how the parts of the process work together.  Not to be missed! 

5 Step Machine Learning Process Overview

What question are you asking?  What are your goals?
What does done look like? How good must our prediction be?

All these things are key parts of 1 - Asking the right question in the first place....

1 - Asking the right question

In this tell all lecture:

  • Domain expertise reigns supreme! 
  • Where will you get your data from?  Surprising secret sources of data you might not have considered.
  • Dirty data....  dirty,dirty data!  Anticipating the largest effort in any Machine Learning  project realistically.. as well as discussing tidy data.

What are waiting for!  Go to your the lecture (room) and clean that (data) up!  All messes are not created equal. 

2 - Identifying, obtaining, and preparing the right data

It's science and it's art.  In this lecture we'll discuss how Machine Learning algorithms interact with data to model answers to your problems.  We'll discuss and illustrate four common Machine Learning algorithms.  For each, we'll cover off how they work, and what workloads work best for them.  You'll become a master of the digitally arcane, with powers over:

  • Decision Trees
  • Naïve Bayesian Classifiers
  • Neural Networks
  • kNN - K's Nearest Neighbours.
3 - Identifying and applying a ML Algorithm

How do you evaluate the performance of your Machine Learning algorithm anyway?  And if it's not working they way you expected - how do you fix it?  In this tell all lecture, we'll discuss common problems of Machine Learning - and how address them.

4 - Evaluating the performance of the model and adjusting

Finally!  We've reached the end goal!  Or have we?

In this brief lecture, we'll cover off four important things to keep in mind to use your Machine Learning Model. 

5 - Using and presenting the model

A quiz on the process of Machine Learning

Machine Learning - Process
6 questions
How to apply Machine Learning for Data Science
9 Lectures 14:20

How do you get started in your journey to applying Machine Learning for Data Science?  In this brief overview, we'll describe the tell-all lectures, that will give you a place to start to apply Machine Learning and Data Science. 

How to apply Machine Learning for Data Science - Overview


Really.  This lecture is a important one, because it will give you guidance on how to get started in your journey without loosing your mind along the way. 

Where to begin your journey

What do you need to do Machine Learning?  Is it expensive?  Out of reach?

In this surprising lecture, we'll pull back the curtain on what Data Scientists are actually using.  We'll also list the top five tools for Data Science, that we will deep dive into, in the following lectures.

Common platforms and tools for Data Science

The number one tool for Data Science, is "R" and is a power house for Machine Learning applications.  We'll describe the tool, as well as provide links an important tips on using it.

Data Science using - R

The second most popular tool for Data Science, is "Python".  Python is a general programming language with incredible power, versatility and flexibility.  It's gaining on R year by year, and has powerful Data Science and Machine Learning Capabilities. 

We'll describe the python, as well as provide links an important tips on using it.

Data Science using - Python

The third most common tool for Data Science is SQL.  Pronounced SEA-QUEL, this is a Database language.  In this lecture we'll describe what SQL is, and why it has shown up in the third place for data science tools. 

Data Science using SQL

The fourth most common tool for Data Science is Microsoft Excel?  Yes - really!  In this lecture we'll describe Microsoft Excel and it's value as a Data Science tool. 

Finally, we'll give you the "real deal", when it comes to doing Machine Learning in excel.  The answer, will surprise you!

Data Science using Excel

The final top five tool for Data Science is rapid miner.  In this lecture we'll discuss using Software as a Service, and some things to think about when using Rapid Miner. 

Data Science using RapidMiner

You made it!  In this final lecture of section 5, we'll talk about things to watch out for when doing Machine Learning.  This lecture will give you key information on how to avoid obstacles on your way to success!

Cautionary Tales
1 Lecture 00:40

Congratulations on your journey into Machine Learning and Data Science.  We sincerely hope you enjoyed it - and we hope to see you again... in our next course! 

All done! What's next?
Retired Lectures
1 Lecture 04:48
#4 - Secret sauce inside!: How to get the most out of this course
About the Instructor
David Valentine
4.3 Average rating
424 Reviews
18,549 Students
3 Courses
The Backyard Data Scientist

Mr. David Valentine is an decorated Enterprise Architect with over seventeen years of experience in enterprise computing environments.  He currently works for the Province of Manitoba, in Canada where he is a responsible for the architecture of the Server and Mainframe compute environment. 

Mr. Valentine has a passion for Data Science, Computer Science, Machine Learning and Data Science.  As the "Backyard Data Scientist", he bring his experience and ability to simplify challenging technical topics to Data Science.  He's delighted to offer the world his first course on the Udemy platform, "Machine Learning for Data Science"