This is a very basic 10-15 min Basics of Machine Learning deck that I used to give a talk to the Oklahoma City Data Science/Big Data user group in Feb 2015.

2. Objective
Entice you to want to
go learn more about
machine learning.

3. Data is huge.
People are expensive.
Computation is cheap.

4. Use applied statistics
to let the computers
program themselves.

5. Types

6. Types
Supervised
"I have a set of
examples with the right
answers, I want to learn
a pattern and use it on
examples where I don't
have the answers."

7. Types
Unsupervised
"I have data with no
answers, but I want
to find a pattern
that might lead me
to an answer."

8. Types
Reinforcement
"I want to start with
what I know now,
and be able to
learn new things as
they come along."

9. Types
Supervised
"I have a set of
examples with the right
answers, I want to learn
a pattern and use it on
examples where I don't
have the answers."
Unsupervised
"I have data with no
answers, but I want
to find a pattern
that might lead me
to an answer."
Reinforcement
"I want to start with
what I know now,
and be able to
learn new things as
they come along."

10. Types
Supervised
"I have a set of
examples with the right
answers, I want to learn
a pattern and use it on
examples where I don't
have the answers."
Unsupervised
"I have data with no
answers, but I want
to find a pattern
that might lead me
to an answer."
Reinforcement
"I want to start with
what I know now,
and be able to
learn new things as
they come along."

11. Regression vs Classification
Supervised Learning

12. Regression
Use continuous data to
make a model that
predicts where new
data will fit.

13. Classification
Label data into
"buckets", and make
predictions on which
bucket a new data
point will fall into.

16. Where to learn more
Books
Podcasts
Interactive

17. Where to learn more
Books
Podcasts
Interactive

18. Where to learn more
Books
Podcasts
Interactive

19. Where to learn more
Books
Podcasts
Interactive

20. Where to learn more
frankdevans