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Practical Machine Learning Tutorial with Python Intro p.1 Video Lecture | Machine Learning with Python - AI & ML
FAQs on Practical Machine Learning Tutorial with Python Intro p.1 Video Lecture - Machine Learning with Python - AI & ML
| 1. What is machine learning and how does it relate to artificial intelligence? |  |
Ans. Machine learning is a subset of artificial intelligence that focuses on enabling computers to learn and make predictions or decisions without being explicitly programmed. It uses algorithms and statistical models to analyze and interpret data, allowing machines to improve their performance over time through experience and feedback.
| 2. How can practical machine learning be implemented using Python? | |
Ans. Python provides various libraries and frameworks, such as scikit-learn, TensorFlow, and Keras, that make it easy to implement practical machine learning algorithms. These libraries offer pre-built functions and classes for tasks like data preprocessing, model training, and evaluation, enabling developers to quickly build machine learning models using Python.
| 3. What are some common applications of machine learning in real-world scenarios? | |
Ans. Machine learning has numerous applications across various industries. Some common examples include:
- Fraud detection: Machine learning algorithms can analyze patterns and anomalies in financial transactions to identify potential fraudulent activities.
- Image recognition: Machine learning models can be trained to recognize and classify objects or features in images, enabling applications like facial recognition or self-driving cars.
- Natural language processing: Machine learning techniques can be used to process and understand human language, enabling applications like chatbots or language translation.
- Predictive maintenance: Machine learning can analyze sensor data from equipment or machinery to predict and prevent potential failures or breakdowns.
| 4. What are the main steps involved in a typical machine learning project? | |
Ans. A typical machine learning project involves the following steps:
1. Data collection: Gathering relevant data that represents the problem or domain you are working on.
2. Data preprocessing: Cleaning and transforming the data to make it suitable for analysis and model training.
3. Model selection: Choosing an appropriate machine learning algorithm or model architecture based on the problem and data.
4. Model training: Using the collected data to train the chosen model by adjusting its parameters.
5. Model evaluation: Assessing the performance of the trained model using evaluation metrics and validation techniques.
6. Model deployment: Implementing the trained model into a production environment for real-world use.
7. Monitoring and maintenance: Continuously monitoring the model's performance and making necessary adjustments or updates as needed.
| 5. What are some key challenges or limitations in practical machine learning implementation? | |
Ans. Practical machine learning implementation can face the following challenges and limitations:
- Lack of quality data: Machine learning models heavily rely on high-quality and representative data. Insufficient or biased data can lead to inaccurate or biased predictions.
- Overfitting: Models that are too complex or trained on limited data can overfit the training data, resulting in poor generalization and performance on unseen data.
- Interpretability: Some machine learning models, such as deep neural networks, can be difficult to interpret and explain their decision-making process, which may be crucial in certain applications.
- Computational resources: Training and deploying complex machine learning models may require significant computational resources, including processing power and memory.
- Ethical considerations: Machine learning models can inadvertently reinforce biases or discriminate against certain groups if not carefully designed and monitored. Ethical considerations should be taken into account during implementation.
Text Transcript from Video
Well, girls and guys and welcome to an
in-depth machine learning tutorial series.
The objective of this tutorial series is
to give you a holistic understanding of
machine learning and how it works and
we're going to be doing this by covering
a variety of algorithms so first we're
gonna be covering regression then we're
going to be moving into classification
with k-nearest neighbors and support
vector machines and then we're going to
get into clustering with flat clustering
a hierarchical clustering and then
finally we'll be getting into deep
learning with neural networks.
So, each step of the way with each of the major algorithms
we're going to cover a theory,
application and then we're going to dive
in deep to the inner workings of each of
them
so with theory this is the high-level
intuitions it's actually pretty quick to
digest the theory of all the algorithms.
Most of the algorithms are actually
fairly basic since they need to be able
to scale to very large amounts of data.
Then we move on to application this is
also fairly quick
we're going to use a module like
scikit-learn and apply these algorithms
to real-world data to see exactly how
they work what kind of input they expect
from us and what kind of output we
should expect back from them.
Then finally we dive into the inner workings
in the way that we're going to do this
is by recreating the algorithms
themselves from scratch in code
including all the math and all that so
when you do this you'll be able to have
a truly complete understanding of how
these algorithms work which is going to
help you down the line
So, in order to
fall along with a series i strongly
suggest you at least have the basics of
Python 3 down understood if you don't
I do have a Python 3 basics tutorial
series here
check that out you really just need to
get at least to the point where we
install modules with pip.
After that you can continue on but you
really need those that initial I think
it's like maybe 10 or 15 videos of core
understanding of Python. We'll also be
covering a healthy amount of math here
but we'll be talking about the matting
and explaining that as we go for sure so
you're not really expected to know much
about math
becoming mostly algebra and geometry
that comes up.
So, machine learning was really came
about in the fifties so like more than a
half century ago now and it was defined
in 1959 by Arthur Samuel as the field of
study where we give machines the ability
to learn without being explicitly
programmed to do so.
So, the way I kind of like to think of it
is it's in viewing knowledge to a
machine without hard coding that
knowledge so interestingly enough when I
talk to people both programmers and
non-programmers and and and find out
what they know about machine learning
most people think machine learning is
hard-coded so when you ask them what's
wrong with machine learning or how is it
different from actual learning that's
where you find that most people think
it's hard coded so kind of interesting
issue there that most people are
completely unaware that this field
exists and it's actually not being hard
coded so that was one machine learning
was actually kind of defined in 1963
Vladimir Vapnik came up with the support
vector machine but this really went
pretty much overlooked until the
90s so Vladimir Vapnik was in the
Soviet Union and then in the 90s he
was actually scooped out of the Soviet
Union by bell labs and that's when he
showed that that the support vector
machine was better than the neural
network at the time doing handwritten
character recognition so it's
handwritten digits of our call right but
anyway it was better than the support
vector a better than the neural network
at that task the support vector machines
really took the lead for quite some time
really up until very recently when
Google basically has kind of come back
to really put some weight behind the
neural network specifically with deep
learning.
But if you think you're kind of late to the to the party
so to speak
with machine learning I assure you
you're not because I mean think about
computers in the 95s i mean
we're talking
we just started putting transistors on
printed circuits instructions to your
computer was a maximum of a handful of
bits at a time so pretty bad i mean even
think about your computers in the 90s
this was very hard at
even if you were like a PhD student is
very difficult to even get access to a
machine that could run significantly a
support vector machine at scale [let's say].
Whereas nowadays we live in a time where
you can engage in deep learning with
neural networks on like gigabytes or
even terabytes of data and what you can
do is you can spin up a you know
hundred-thousand-dollar GPU cluster on
amazon web services and basically just
rent it for a few dollars an hour and
then be done with it
that's incredible like we living an
incredible time this is the best time to
be alive is I think right now is the
first time we've really been able to
really stretch and flex the muscles of
machine learning up to this point it's
really been learning without the machine
part so we also so much to the point
where like with scikit-learn you can you
can use scikit-learn with almost no
understanding at all you just apply it
and you can usually get about
90~95% accuracy without
messing with the default parameter
yeah you can just get it with the
default parameters so that's also pretty
crazy right
you want to push the limits and get more
accuracy out of it then you need to
learn how they work and how you can
tweak those parameters so you're working
on self-driving car getting 90 to
95% accuracy and
identifying the difference between like
a blob of tar and a child in a blanket
that's not good enough you need much any
more accuracy than that
so anyway that's what this series is for
is for the people are really looking to
push the limits on what's available so
if you really just want to learn the
basics actually already have some simple
machine learning tutorials out there for
just applying machine learning to a
dataset you can do this actually very
very fast.
So anyways the first topic that we're
gonna be covering is regression and
let's go ahead and get into it.
in-depth machine learning tutorial series.
The objective of this tutorial series is
to give you a holistic understanding of
machine learning and how it works and
we're going to be doing this by covering
a variety of algorithms so first we're
gonna be covering regression then we're
going to be moving into classification
with k-nearest neighbors and support
vector machines and then we're going to
get into clustering with flat clustering
a hierarchical clustering and then
finally we'll be getting into deep
learning with neural networks.
So, each step of the way with each of the major algorithms
we're going to cover a theory,
application and then we're going to dive
in deep to the inner workings of each of
them
so with theory this is the high-level
intuitions it's actually pretty quick to
digest the theory of all the algorithms.
Most of the algorithms are actually
fairly basic since they need to be able
to scale to very large amounts of data.
Then we move on to application this is
also fairly quick
we're going to use a module like
scikit-learn and apply these algorithms
to real-world data to see exactly how
they work what kind of input they expect
from us and what kind of output we
should expect back from them.
Then finally we dive into the inner workings
in the way that we're going to do this
is by recreating the algorithms
themselves from scratch in code
including all the math and all that so
when you do this you'll be able to have
a truly complete understanding of how
these algorithms work which is going to
help you down the line
So, in order to
fall along with a series i strongly
suggest you at least have the basics of
Python 3 down understood if you don't
I do have a Python 3 basics tutorial
series here
check that out you really just need to
get at least to the point where we
install modules with pip.
After that you can continue on but you
really need those that initial I think
it's like maybe 10 or 15 videos of core
understanding of Python. We'll also be
covering a healthy amount of math here
but we'll be talking about the matting
and explaining that as we go for sure so
you're not really expected to know much
about math
becoming mostly algebra and geometry
that comes up.
So, machine learning was really came
about in the fifties so like more than a
half century ago now and it was defined
in 1959 by Arthur Samuel as the field of
study where we give machines the ability
to learn without being explicitly
programmed to do so.
So, the way I kind of like to think of it
is it's in viewing knowledge to a
machine without hard coding that
knowledge so interestingly enough when I
talk to people both programmers and
non-programmers and and and find out
what they know about machine learning
most people think machine learning is
hard-coded so when you ask them what's
wrong with machine learning or how is it
different from actual learning that's
where you find that most people think
it's hard coded so kind of interesting
issue there that most people are
completely unaware that this field
exists and it's actually not being hard
coded so that was one machine learning
was actually kind of defined in 1963
Vladimir Vapnik came up with the support
vector machine but this really went
pretty much overlooked until the
90s so Vladimir Vapnik was in the
Soviet Union and then in the 90s he
was actually scooped out of the Soviet
Union by bell labs and that's when he
showed that that the support vector
machine was better than the neural
network at the time doing handwritten
character recognition so it's
handwritten digits of our call right but
anyway it was better than the support
vector a better than the neural network
at that task the support vector machines
really took the lead for quite some time
really up until very recently when
Google basically has kind of come back
to really put some weight behind the
neural network specifically with deep
learning.
But if you think you're kind of late to the to the party
so to speak
with machine learning I assure you
you're not because I mean think about
computers in the 95s i mean
we're talking
we just started putting transistors on
printed circuits instructions to your
computer was a maximum of a handful of
bits at a time so pretty bad i mean even
think about your computers in the 90s
this was very hard at
even if you were like a PhD student is
very difficult to even get access to a
machine that could run significantly a
support vector machine at scale [let's say].
Whereas nowadays we live in a time where
you can engage in deep learning with
neural networks on like gigabytes or
even terabytes of data and what you can
do is you can spin up a you know
hundred-thousand-dollar GPU cluster on
amazon web services and basically just
rent it for a few dollars an hour and
then be done with it
that's incredible like we living an
incredible time this is the best time to
be alive is I think right now is the
first time we've really been able to
really stretch and flex the muscles of
machine learning up to this point it's
really been learning without the machine
part so we also so much to the point
where like with scikit-learn you can you
can use scikit-learn with almost no
understanding at all you just apply it
and you can usually get about
90~95% accuracy without
messing with the default parameter
yeah you can just get it with the
default parameters so that's also pretty
crazy right
you want to push the limits and get more
accuracy out of it then you need to
learn how they work and how you can
tweak those parameters so you're working
on self-driving car getting 90 to
95% accuracy and
identifying the difference between like
a blob of tar and a child in a blanket
that's not good enough you need much any
more accuracy than that
so anyway that's what this series is for
is for the people are really looking to
push the limits on what's available so
if you really just want to learn the
basics actually already have some simple
machine learning tutorials out there for
just applying machine learning to a
dataset you can do this actually very
very fast.
So anyways the first topic that we're
gonna be covering is regression and
let's go ahead and get into it.
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Oct 25, 2024 Last updated |
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