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All Machine Learning algorithms explained in 17 min
학습 통계
CEFR 레벨
난이도
자막 (542 세그먼트)
in the next 17 minutes I will give you
an overview of the most important
machine learning algorithms to help you
decide which one is right for your
problem my name is Tim and I have been a
data scientist for over 10 years and
taught all of these algorithms to
hundreds of students in real life
machine learning boot camps there is a
simple strategy for picking the right
algorithm for your problem in 17 minutes
you will know how to pick the right one
for any problem and get a basic
intuition of each algorithm and how they
relate to each other my goal is to give
as many of you as possible an intuitive
understanding of the major machine
learning algorithms to make you stop
feeling overwhelmed according to
Wikipedia machine learning is a field of
study in artificial intelligence
concerned with the development and study
of statistical algorithms that can learn
from data and generalize to unseen data
and thus perform tasks without explicit
instructions much of the recent
advancements in AI are driven by neural
networks which I hope to give you an
intuitive understanding of by the end of
this video Let's divide machine learning
into its subfields generally machine
learning is divided into two areas
supervised learning and unsupervised
learning supervised learning is when we
have a data set with any number of
independent variables also called
features or input variables and a
dependent variable also called Target or
output variable that is supposed to be
predicted we have a so-called training
data set where we know the True Values
for the output variable also called
labels that we can train our algorithm
on to later predict the output variable
for new unknown data examples could be
predicting the price of a house the
output variable based on features of the
house say square footage location year
of construction Etc categorizing an
object as a cat or a dog the output
variable or label based on features of
the object say height weight size of the
ears color of the eyes Etc unsupervised
learning is basically any learning
problem that is not supervised so where
no truth about the data is known so
where a supervised algorithm would be
like showing a little kid what a typical
cat looks like and what a typical dog
looks like and then giving it a new
picture and asking it what animal it
sees an unsupervised algorithm would be
giving a kid with no idea of what cats
and dogs are a pile of pictures of
animals and asking it to group by
similarity without any further
instructions examples of unsupervised
problems might be to sort all of your
emails into three unspecified categories
which you can then later inspect and
name as you wish the algorithm will
decide on its own how it will create
those categories also called clusters
let's start with supervised learning
arguably the bigger and more important
branch of machine learning there are
broadly two subcategories in regression
we want to predict a continuous numeric
Target variable for a given input
variable using the example from before
it could be predicting the price of a
house given any number features of a
house and determining their relationship
to the final price of the house we might
for example find out that square footage
is directly proportional to the price
linear dependence but that the age of
the house has no influence on the price
of the house in classification we try to
assign a discrete categorical label also
called a class to a data point for
example we may want to assign the label
spam or no spam to an email based on its
content sender and so on but we could
also have more than two classes for
example junk primary social promotions
and updates as Gmail does by default now
let's dive into the actual algorithms
starting with the mother of all machine
learning algorithms linear regression in
general supervised learning algorithms
try to determine the relationship
between two variables we try to find the
function that Maps one to the other
linear regression in its simplest form
is trying to determine a linear
relationship between two variables
namely the input and the output we want
to fit a linear equation to the data by
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