Hello everyone and welcome to the introduction to machine learning lecture.
We've finally reached the machine learning section of this course.
And so now let's go ahead and discuss how we're going to approach machine learning.
The companion book will be using throughout the machine learning section of the course and in actual
background what machine learning is throughout this section of the course.
We're going to be using the introduction to Cisco learning like Gareth James as a companion book you'll
have optional reading assignments from this book.
It's freely available online.
So let's go ahead and see how to get it.
I'm going to jump quickly to the browser.
All right.
Here I am at my browser.
Go ahead and go to Google dot com and then search for the four letters put together.
I s l r in all capitals and that should take you to this page from USC.
The introduction to Cisco learning you can go ahead and click on this.
It'll take you to a page that looks like this.
This is the book we're going to be using an instruction to Cisco learning if you want to get the book
you can click here on the link.
Get the book and then scroll down.
This book can be purchased at Amazon or directly from Springer if you want a physical copy.
However if you want a legal free digital copy you can just click here and the book can be downloaded
as a PDA for free.
That will end up looking something like this.
Once you have the PDAF downloaded you'll see something that says from your text and statistics.
And here we have an introduction to learning and the term statistical learning is just another way to
phrase machine learning from your more mathematical background.
You should know that this book has its applications coded out in our programming language.
However we're going to be using this book as a mathematical reference.
You'll be giving optional reading assignments and you'll go ahead and read the book to get the mathematics
and the deeper mathematical background behind the actual theories and algorithms that you learn throughout
the machine learning section of the course.
So for example if we're discussing linear regression you'll get a reading assignment to read chapter
3 of this book we're going to go and scroll down here so you can see all the contents.
Here we have linear regression.
We'll go ahead and assign you the reading and then you have the option of coming over here opening this
book and then coming to linear regression.
Let's go ahead and jump to linear regression.
OK so here we are at the linear regression section and now you can optionally read this and get the
mathematical background so here you can see simple introgression formulas described estimating the coefficients
in all the topics of the mathematics behind what we're actually going to program out with Python.
Now keep in mind I will be giving you a brief theory lecture where you don't actually have to come over
here and read this.
This is optional reading if you want.
The deeper dive into mathematics behind the algorithms and you should also keep in mind that the code
for this book is an r.
However as you go along throughout this course we'll show you how to do everything in Python as well
.
Again this book is more of an optional resource for people who are interested in the mathematics of
machine learning.
Let's go ahead and jump back to the presentation.
All right.
So students who want that mathematical theory should do the reading assigned before the machine learning
lectures.
Students who just want light theory and are more interested in python applications won't have to do
that optional reading.
Now if you do want to do the reading I would recommend that you read chapters 1 and 2 right now to gain
a background understanding before continuing the rest of the machine learning lectures.
Let's discuss what is machine learning machine learning as a method of data analysis that automates
analytical model building using algorithms that heavily learn from data.
Machine learning allows computers to find hidden insights without being explicitly programs where to
look you may be wondering what does machine learning used for.
It's used in a lot of fields such as fraud detection real time ads on Web pages calculating new pricing
models financial modeling email spam filtering text sentiment analysis customer segmentation recommendation
engines and so much more.
And we're going to be exploring a lot of these cases for the machine learning portfolio projects presented
throughout this course.
Let's talk about the machine learning process in general.
First you need to acquire data.
So your data is going to have a lot of features based off whatever you're trying to study.
Then you'll probably have to clean your data to get it into a nice format and you can use your panderers
skills in order to do that then you're going to split your data into test data and training data.
You will train your machine learning model on your training data and then you will test your model on
the test data which the model hasn't seen before.
And then you can iterate through this process try to tune your model to best fit both the training data
and then test well on the test data.
Then once it's ready you can deploy your model let's talk about what is machine learning and a little
more detail.
There are three main types of machine learning algorithms supervise learning algorithms unsupervised
learning algorithms and reinforcement learning.
Let's briefly describe each type before diving in a little more detail.
Supervised learning is when you have labelled data and are trying to predict the label based off of
known features unsupervised learning is when you have unlabeled data and are trying to group maybe together
similar data points based off of other features or doing some other unsupervised Machine Learning tests
which we'll get into later on.
And then finally reinforcement learning is where your algorithm tries to learn to perform an action
based off of previous experience.
Let's discuss supervised learning in a little more detail.
Again supervised learning algorithms are trained using labeled examples such as an input or the desired
output is known.
For example maybe you have a piece of equipment that has data points labeled either for failed or are
for runs the learning algorithm receives a set of inputs along with the corresponding correct outputs
.
And then the algorithm learns by comparing its actual output with correct outputs to find errors.
It then modifies the model accordingly drew methods like classification regression prediction and gradient
boosting supervised learning uses patterns Ritwick the values of the label on additional unlabeled data
meaning you'll train your model unlabeled data and then later on you will feed your model unlabeled
data and see if it can correctly predict the label value.
Supervised learning is commonly used in applications where historical data predicts likely future events
.
For example it can anticipate when credit card transactions are likely to be fraudulent or which insurance
customer is likely to file a claim or can even attempt to predict the price of a house based off of
different features for houses for which we have historical price data unsupervised learning is used
against data that has no historical labels.
The system is not told.
The quote right answer.
The algorithm must figure out what is being shown and the goal here is to explore the data and find
some sort of structure within the data or can I also find the main attributes that separate customer
segments from each other.
Popular techniques include self organizing maps nearest neighbor mapping Kamins clustering and singular
value decomposition.
These algorithms are also used to segment text topics recommend items and identify data outliers.
This will make a lot more sense when we actually perform unsupervised learning with Python.
For now we'll just leave it at that and know that it has unlabeled data.
Reinforcement learning is often used for robotics gaming and navigation with reinforcement learning
.
The algorithm discovers through trial and error which actions yield the greatest rewards.
This type of learning has three primary components the agent which is the learner or the decision maker
the environment everything the agent interacts with and then the actions what the agent can actually
do or perform.
The objective is for the agent to choose actions that maximize the expected reward over a given amount
of time the agents will reach the goal much faster by following a good policy.
So the goal in reinforcement learning is to learn the best policy.
Now let's discuss in a little more detail how we're going to approach machine learning in this course
for each algorithm or machine learning topic.
You're going to receive an optional reading assignment from an introduction to school learning.
You'll get a light overview of theory presentation from me then we'll have a demonstration lecture with
Python and had actually perform the algorithm.
Then you'll have a machine learning portfolio project assignment which you'll have to fill out and then
you'll have an overview of the solution for that project.
So you're going to get a lot of information for each machine learning topic.
Let's go ahead and get a brief tour of the machine learning notes.
All right here I am at the downloaded folder for all the notebooks and under the machine learning sections
folder.
You should see a bunch of folders with actual either algorithms or machine learning topics.
These are going to be relevant to the section of the course and have same name.
So for example the very first machine learning topic we learn about is linear regression.
When you're on that topic you just come to the linear regression folder click on it and you should see
three notebooks.
The three no books you'll see is a lecture notebook.
Usually Ill say the machine learning algorithm name and then with Python for instance linear regression
with Python then you'll see your portfolio project exercise and then you'll see the solutions book where
you're going to go ahead and do is follow along with delight theory lecture.
Then follow along with this python notebook and then you'll attempt the exercise and then check out
the solutions to the exercise and all the data will be using for the lecture and the exercise is available
in the same folder as the algorithm folder.
OK let's go ahead and hop back to the presentation.
I want you to know that machine learning takes time to learn yourself.
You should be patiently for yourself and feel free to post the Q&A forums whenever you have questions
or a stock or even anything such as trying to find a folder or a notebook.
Keep in mind that no one course can be a reference for all machine learning topics but I'm always happy
to point you in the right direction.
In the Q&A forums.
OK let's go ahead and start using Python for machine learning.
Thanks everyone and I'll see you at the next lecture