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Introduction to Machine Learning for Data Science

A primer on Machine Learning for Data Science. Revealed for everyday people, by the Backyard Data Scientist.
(9,039 reviews)
46,200 students
Created by David Valentine


CourseMarks Score®







Platform: Udemy
Price: $11.99
Video: 5h 33m
Language: English
Next start: On Demand

Top Machine Learning courses:

Detailed Analysis

CourseMarks Score®

9.3 / 10

CourseMarks Score® helps students to find the best classes. We aggregate 18 factors, including freshness, student feedback and content diversity.

Freshness Score

9.1 / 10
This course was last updated on 7/2020.

Course content can become outdated quite quickly. After analysing 71,530 courses, we found that the highest rated courses are updated every year. If a course has not been updated for more than 2 years, you should carefully evaluate the course before enrolling.

Student Feedback

9.0 / 10
We analyzed factors such as the rating (4.4/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.

New courses are hard to evaluate because there are no or just a few student ratings, but Student Feedback Score helps you find great courses even with fewer reviews.

Content Score

9.3 / 10
Video Score: 8.4 / 10
The course includes 5h 33m video content. Courses with more videos usually have a higher average rating. We have found that the sweet spot is 16 hours of video, which is long enough to teach a topic comprehensively, but not overwhelming. Courses over 16 hours of video gets the maximum score.
The average video length is 6 hours 15 minutes of 454 Machine Learning courses on Udemy.
Detail Score: 10.0 / 10

The top online course contains a detailed description of the course, what you will learn and also a detailed description about the instructor.

Extra Content Score: 9.5 / 10

Tests, exercises, articles and other resources help students to better understand and deepen their understanding of the topic.

This course contains:

0 article.
11 resources.
0 exercise.
0 test.

Table of contents


Course Most Recently Updated Nov/2018! 
Thank you all for the huge response to this emerging course!  We are delighted to have over 20,000 students in over 160 different countries.  I’m genuinely touched by the overwhelmingly positive and thoughtful reviews.  It’s such a privilege to share and introduce this important topic with everyday people in a clear and understandable way. 
I’m also excited to announce that I have created real closed captions for all course material, so weather you need them due to a hearing impairment, or find it easier to follow long (great for ESL students!)… I’ve got you covered. 
Most importantly:
To make this course “real”, we’ve expanded.  In November of 2018, the course went from 41 lectures and 8 sections, to 62 lectures and 15 sections!  We hope you enjoy the new content!  

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.
Bonus Course!  To make this “really real”, I’ve included a bonus course!
Most importantly in the bonus course I’ll include information at the end of every section titled “Further Magic to Explore” which will help you to continue your learning experience. 
In this bonus course we’ll explore:
•Creating a real live Machine Learning Example of Titanic proportions.  That’s right – we are going to predict survivability onboard the Titanic!
•Use Anaconda Jupyter and python 3.x
•A crash course in python – covering all the core concepts of Python you need to make sense of code examples that follow. See the included free cheat sheet!
•Hands on running Python! (Interactively, with scripts, and with Jupyter)
•Basics of how to use Jupyter Notebooks
•Reviewing and reinforcing core concepts of Machine Learning (that we’ll soon apply!)
•Foundations of essential Machine Learning and Data Science modules:
•NumPy – An Array Implementation
•Pandas – The Python Data Analysis Library
•Matplotlib – A plotting library which produces quality figures in a variety of formats
•SciPy – The fundamental Package for scientific computing in Python
•Scikit-Learn – Simple and efficient tools data mining, data analysis, and Machine Learning
•In the titanic hands on example we’ll follow all the steps of the Machine Learning workflow throughout:
•1. Asking the right question.
•2. Identifying, obtaining, and preparing the right data
•3. Identifying and applying a Machine Learning algorithm
•4. Evaluating the performance of the model and adjusting
•5. Using and presenting the model
•We’ll also see a real world example of problems in Machine learning, including underfit and overfit.
The bonus course finishes with a conclusion and further resources to continue your Machine Learning journey. 
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!


• 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.

You will 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!

This course is for

• 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.

How much does the Introduction to Machine Learning for Data Science course cost? Is it worth it?

The course costs $11.99. And currently there is a 87% discount on the original price of the course, which was $94.99. So you save $83 if you enroll the course now.
The average price is $17.2 of 454 Machine Learning courses. So this course is 30% cheaper than the average Machine Learning course on Udemy.

Does the Introduction to Machine Learning for Data Science course have a money back guarantee or refund policy?

YES, Introduction to Machine Learning for Data Science has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.

Are there any SCHOLARSHIPS for this course?

Currently we could not find a scholarship for the Introduction to Machine Learning for Data Science course, but there is a $83 discount from the original price ($94.99). So the current price is just $11.99.

Who is the instructor? Is David Valentine a SCAM or a TRUSTED instructor?

David Valentine has created 3 courses that got 9,212 reviews which are generally positive. David Valentine has taught 59 students and received a 4.4 average review out of 9,212 reviews. Depending on the information available, David Valentine is a TRUSTED instructor.

More info about the instructor, David Valentine

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”


CourseMarks Score®







Platform: Udemy
Price: $11.99
Video: 5h 33m
Language: English
Next start: On Demand

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