#10 Machine Learning Specialization [Course 1, Week 1, Lesson 3]
Summary
TLDRThis video script explains the fundamentals of supervised learning, focusing on how algorithms process input data sets to predict outcomes. It introduces the concept of a training set comprising input features and output targets, which the model learns from. The model, represented as a function 'f', estimates predictions (Y hat) based on input features (X). The script delves into linear regression, a basic form of supervised learning where 'f' is a straight line, and discusses the importance of the cost function in machine learning. It concludes by encouraging viewers to explore an optional lab for hands-on experience with linear regression in Python.
Takeaways
- 📊 Supervised learning algorithms use input data sets to produce a function (f) that can predict output targets.
- 🏠 The training set in supervised learning includes both input features (e.g., size of a house) and output targets (e.g., price of a house).
- 🔢 The model's prediction (y hat) is an estimate of the actual target value (Y), which may differ until confirmed.
- 📉 The function f, often represented as a straight line, is used to make predictions based on the input feature X.
- 📈 The choice of a linear function (F_w,b(x) = Wx + b) simplifies the model and serves as a foundation for more complex models.
- 📚 Linear regression is the specific name for the model when the function f is a straight line, especially with one input variable.
- 🔑 Parameters W and B in the function f are crucial as they determine the slope and intercept of the line, respectively.
- 📝 The script suggests an optional lab for practicing defining a straight line function in Python and fitting it to training data.
- 🤖 Cost function is a critical component in machine learning, used to evaluate and improve the model's performance.
- 🔍 The next step after understanding the basics of linear regression is to learn how to construct a cost function for model optimization.
Q & A
What is the primary goal of a supervised learning algorithm?
-The primary goal of a supervised learning algorithm is to learn a function, denoted as 'f', that can take new input data (features) and predict an output (target) based on the training data provided.
What are the two components of a training set in supervised learning?
-The two components of a training set in supervised learning are the input features (such as the size of a house) and the output targets (such as the price of the house).
What does the output target represent in the context of supervised learning?
-The output target represents the correct answer or the actual true value that the model will learn from during training.
How is the function 'f' in supervised learning typically represented?
-In the context of the script, the function 'f' is represented as a straight line equation, which can be mathematically expressed as f(x) = wx + b, where 'w' and 'b' are parameters that need to be learned.
What is the difference between 'y' and 'y-hat' in the script?
-'y' represents the actual true value of the target in the training set, while 'y-hat' (notated as ŷ) is the estimated or predicted value of 'y' made by the model.
Why might a linear function be chosen as the initial model in supervised learning?
-A linear function is chosen as the initial model because it is relatively simple and easy to work with, which serves as a good foundation for understanding more complex models, including non-linear ones.
What is the term for a linear model with a single input variable?
-A linear model with a single input variable is called 'univariate linear regression', where 'univariate' indicates that there is only one variable involved.
What is the purpose of the cost function in the context of supervised learning?
-The cost function in supervised learning is used to measure the performance of the model by quantifying the difference between the model's predictions and the actual values in the training set.
Why is the cost function considered an important concept in machine learning?
-The cost function is considered an important concept in machine learning because it is a universal tool used to evaluate and improve the model's predictions, and it is fundamental to training many advanced AI models.
What is the next step after defining the model function in supervised learning?
-After defining the model function, the next step is to construct a cost function and use it to optimize the parameters of the model to minimize the cost and improve the model's predictions.
Outlines
📊 Introduction to Supervised Learning
This paragraph introduces the concept of supervised learning, explaining how an algorithm takes a dataset with input features and output targets to learn a function. The function, denoted as 'f', is used to make predictions on new data. The training set includes features like house size and targets like house price. The algorithm's goal is to find a function that can predict the target value (Y hat) based on the input feature (X). The paragraph also touches on the historical term 'hypothesis' for the function 'f' and discusses the importance of the true value (Y) versus the estimated value (Y hat). It ends by hinting at the choice of a linear function to represent 'f', which is a simple yet foundational approach to building more complex models.
🔍 Linear Regression: The Foundation
The second paragraph delves into the specifics of linear regression, a type of supervised learning where the function 'f' is represented as a straight line. It discusses the use of the linear model with one variable, also known as univariate linear regression, focusing on a single feature such as the size of a house. The paragraph explains the notation for the linear function, f(x) = wx + b, where 'w' and 'b' are parameters that determine the line's slope and intercept, respectively. It also mentions the potential for more complex, non-linear models but emphasizes the simplicity and educational value of starting with linear functions. The paragraph concludes by suggesting an optional lab for viewers to explore defining and fitting a straight line function in Python, which helps in understanding how to adjust 'w' and 'b' to best fit the training data.
Mindmap
Keywords
💡Supervised Learning
💡Training Set
💡Input Features
💡Output Targets
💡Function f
💡Prediction \( \hat{y} \)
💡Linear Regression
💡Univariate Linear Regression
💡Cost Function
💡Model Parameters (w and b)
Highlights
Supervised learning algorithms input a dataset and output a function to make predictions.
A training set includes input features and output targets, which are the right answers for the model to learn from.
The supervised learning algorithm produces a function, historically called a hypothesis, to make predictions.
The function f takes a new input X and outputs an estimate or prediction, denoted as Y hat.
In machine learning, Y hat represents the estimate or prediction for the true value Y.
The model's prediction is an estimated value of Y, which may differ from the actual true value.
The function f, representing the model, is chosen to be a straight line for simplicity in this context.
The linear function f is represented as F_W,B(X) = W*X + B, where W and B are parameters to be determined.
The values of W and B in the function determine the prediction Y hat based on the input feature X.
Linear regression is used as a foundational model, which can be extended to more complex non-linear models.
The model is called linear regression with one variable, indicating a single input feature.
The term 'univariate linear regression' is used to describe a linear model with a single input variable.
An optional lab is available to practice defining a straight line function in Python and fitting the training data.
Constructing a cost function is crucial for making the model work effectively.
The cost function is a universal concept in machine learning, used in linear regression and advanced AI models.
The next video will explore how to construct a cost function for the learning algorithm.
Transcripts
let's look in this video at the process
of how supervised Learning Works
supervised learning algorithm will input
the data set and then what exactly does
it do and what does it output let's find
out in this video
recall that a training set in supervised
learning includes both the input
features such as the size of the house
and also the output targets such as the
price of the house the output targets
are the right answers to the model we'll
learn from
to train the model you feed the trading
set both the input features and the
output targets to your learning
algorithm
then your supervised learning algorithm
will produce some function
we'll write this function as lowercase f
where F stands for function historically
this function used to be called a
hypothesis but I'm just going to call it
a function f in this clause
and the job of f is to take a new input
X
and upwards an estimate or prediction
which I'm going to call Y hat and it's
written like the variable y with this
little hat symbol on top
in machine learning the convention is
that y hat is the estimate or the
prediction for y
the function f is called the model
X is called the input or the input
feature and the output of the model is
the prediction y hat
the model's prediction is the estimated
value of y
when the symbol is just a letter Y then
that refers to the Target which is the
actual True Value in the training set in
contrast y hat is an estimate it may or
may not be the actual True Value
well if you're helping your client to
sell the house well the true price of
the house is unknown until they sell it
so your model f given the size or
pressure price which is the estimated
that is the prediction of what the true
price will be
now when we design a learning algorithm
a key question is how are we going to
represent the function f or in other
words what is the math formula we're
going to use to compute f
for now let's stick with f being a
straight line
so your function can be written as F
subscript W comma B of x equals I'm
going to use W Times X plus b
I'll Define w and B soon but for now
just know that W and B are numbers and
the values chosen for w and B will
determine the prediction y hat based on
the input feature X so this FWB of X
means f is a function that takes X's
input and depending on the values of w
and b f will output some value of a
prediction y hat
as an alternative to writing this FW
comma B of X I'll sometimes just write f
of x without explicitly including W and
B in the subscript it's just a simple
notation but means exactly the same
thing as FWB of x
let's plot the trading set on the graph
where the input feature X is on the
horizontal axis and the output targets Y
is on the vertical axis remember the
album learns from this data and
generates a best fit line like maybe
this one here
this straight line is the linear
function f w b of x equals W Times X
plus b
or more simply we can drop W and B and
just write f of x equals WX plus b
here's what this function is doing is
making predictions for the value of y
using a straight line function of x
so you may ask why are we choosing a
linear function where linear function is
just a fancy term for a straight line
instead of some nonlinear function like
a curve or a parabola
well sometimes you want to fit more
complex non-linear functions as well
like a curve like this but since this
linear function is relatively simple and
easy to work with let's use a line as a
foundation that will eventually help you
to get to more complex models that are
non-linear
this particular model as a name is
called linear regression more
specifically this is linear regression
with one variable with a phrase one
variable means that there's a single
input variable or feature X namely the
size of the host
another name for a linear model with one
input variable is univariate linear
regression where uni means one in Latin
and where variate means variable so univ
variance is just a fancy way of saying
one variable
in a later video you also see a
variation of regression where you want
to make a prediction based not just on
the size of a hose but on a bunch of
other things that you may know about the
whole such as number of bedrooms and
other features and by the way when
you're done with this video there is
another optional lab you don't need to
write any code just review it run the
code and see what it does that will show
you how to define in Python a straight
line function and the lab will let you
choose the values of wmb to try to fit
the training data
you don't have to do the lab if you
don't want to but I hope you play of it
when you're done watching this video
so that's linear regression in order for
you to make this work one of the most
important things you have to do is
construct a cost function
the idea of a cost function is one of
the most universal and important ideas
in machine learning and is used in both
linear regression and in training many
of the most advanced AI models in the
world so let's go on to the next video
and take a look at how you can construct
a cost function
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