## Practical Machine Learning Tutorial with Python Intro p.1

The objective of this course is to give you a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms.

In this series, we'll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks.

For each major algorithm that we cover, we will discuss the high level intuitions of the algorithms and how they are logically meant to work. Next, we'll apply the algorithms in code using real world data sets along with a module, such as with Scikit-Learn. Finally, we'll be diving into the inner workings of each of the algorithms by recreating them in code, from scratch, ourselves, including all of the math involved. This should give you a complete understanding of exactly how the algorithms work, how they can be tweaked, what advantages are, and what their disadvantages are.

In order to follow along with the series, I suggest you have at the very least a basic understanding of Python. If you do not, I suggest you at least follow the Python 3 Basics tutorial until the module installation with pip tutorial. If you have a basic understanding of Python, and the willingness to learn/ask questions, you will be able to follow along here with no issues. Most of the machine learning algorithms are actually quite simple, since they need to be in order to scale to large datasets. Math involved is typically linear algebra, but I will do my best to still explain all of the math. If you are confused/lost/curious about anything, ask in the comments section on YouTube, the community here, or by emailing me. You will also need Scikit-Learn and Pandas installed, along with others that we'll grab along the way.

Machine learning was defined in 1959 by Arthur Samuel as the "field of study that gives computers the ability to learn without being explicitly programmed." This means imbuing knowledge to machines without hard-coding it.

https://pythonprogramming.net/machine-learning-tutorial-python-introduction/

https://twitter.com/sentdex

https://www.facebook.com/pythonprogra...

https://plus.google.com/+sentdex

The objective of this course is to give you a holistic understanding of machine learning, covering theory, application, and inner workings of supervised, unsupervised, and deep learning algorithms.

In this series, we’ll be covering linear regression, K Nearest Neighbors, Support Vector Machines (SVM), flat clustering, hierarchical clustering, and neural networks.

For each major algorithm that we cover, we will discuss the high level intuitions of the algorithms and how they are logically meant to work. Next, we’ll apply the algorithms in code using real world data sets along with a module, such as with Scikit-Learn. Finally, we’ll be diving into the inner workings of each of the algorithms by recreating them in code, from scratch, ourselves, including all of the math involved. This should give you a complete understanding of exactly how the algorithms work, how they can be tweaked, what advantages are, and what their disadvantages are.

In order to follow along with the series, I suggest you have at the very least a basic understanding of Python. If you do not, I suggest you at least follow the Python 3 Basics tutorial until the module installation with pip tutorial. If you have a basic understanding of Python, and the willingness to learn/ask questions, you will be able to follow along here with no issues. Most of the machine learning algorithms are actually quite simple, since they need to be in order to scale to large datasets. Math involved is typically linear algebra, but I will do my best to still explain all of the math. If you are confused/lost/curious about anything, ask in the comments section on YouTube, the community here, or by emailing me. You will also need Scikit-Learn and Pandas installed, along with others that we’ll grab along the way.

Machine learning was defined in 1959 by Arthur Samuel as the “field of study that gives computers the ability to learn without being explicitly programmed.” This means imbuing knowledge to machines without hard-coding it.

https://pythonprogramming.net/machine-learning-tutorial-python-introduction/

https://twitter.com/sentdex

https://www.facebook.com/pythonprogra…

https://plus.google.com/+sentdex

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*Related*

Hello, is this course still valid for 2019, thanks in advance !!

You look like Ben stokes !

I see that this video was filmed over 3 years ago. I've been in college for only 1 year now and I know that everything in programming changes a lot everyday. Can you tell me if this course is still up to date ? If yes I will watch all of it and like every single video. Thank you đź™‚

Would you help me in setting tensorflow gpu on Sierra 10.12.6?

I actually loved the ad from Simplilearn.đź”Ą

He looks like a mix of Snowden and Zuckerberg…

I'm trying to learn this. I have been working with python for two to three years and love it!!

You are a great person in internet thank you

Hi,

before to take up these videos. is there anything to learn basic of Machine Learning prior to these video, because am new to ML and DL.

i need to learn basic also, notes or videos link of Basics. Please and Thanks.

I want to be a drone maker. What do I need to know for that? Right now I know python 3. Thank you in advance for the answer.

Hi sir,

My name is jyoti kant from india, I am a job seeker here and I don't have so much money â‚ą.25m do ai courses will you please guide me to what to do to learn Python or any other ai supported language free.

Can someone please advise on what laptop is best suited for machine learning. I know you could train your models on AWS, taking advantage of the high computing GPUs they provide, but in a case where one hasn't got internet access and you have a significantly large dataset, what laptop is best suited for this scenarios?

3b1b and this are the only channels you need to learn neural networks. I recommend watching 3b1b neural network series before this

Why does sentdex look like edward snowden, just me??

how can i contact with you

Will this work on a raspberry pi 3 too?

Wow. Three years ago, this video popped up in my recommended. I had just finished my High School Computer Science class and I was interested in learning more about computers. After watching this video, I decided that before I began learning something like machine learning, I should learn something like Python.

Welp, here I am today after three years of Python where I went down rabbit holes in web design/scraping, bots, data analysis, and a handful of other languages. And now I'm back here; full circle.

Seriously though, thank you for putting me down this route. I 100% wouldn't be in CS as much as I am without this video. đź™‚

4:40 … Is 2019 still the best time ?

Is this series still up to date?

Thanks alot sentdexđź‘Ť..am I to pick one out of machine learning,deep learning,Ai or learn everything???