A primer on Machine Learning for Data Science. Revealed for everyday people, by the Backyard Data Scientist.
What you'll 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.
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To really understand computer technology has changed the world,
with an appreciation of scale.
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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!
Requirements
·
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.
Description
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!
Who 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.
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