All Machine Learning Models Explained in 5 Minutes | Types of ML Models Basics

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Confused about understanding machine learning models? Well, this video will help you grab the basics of each one of them. From what they are, to why they are used, and what purpose do they serve.

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After watching this video, you'll be able to answer,
- How many machine learning models are there
- Some common machine learning models explained
- What is supervised learning
- What is unsupervised learning
- What is regression
- Types of ml models
- Common types of regression
- Common types of classification
- What is classification
- What are popular ML models explained
- What are the types of supervised learning
- What are the types of unsupervised learning
- Understanding the basics of machine learning models
- Learn machine learning models from scratch
- What are common machine learning models for beginners
- Understand machine learning models overview
- Whats are few ml models basics to grasp

Obviously, there is a ton of complexity if you dive into any particular model, but this should give you a fundamental understanding of how each machine learning model works!

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[philipp]

please half the volume of the background music in future videos! good content though, thumbs up!

[katiecallaghan]

Learned more in this 5 minute video than I have in the past two months of watching other videos. Thanks so much for your help

[christopherwright]

I always find it interesting that the statement "logistic regression is a classification method" is repeated so often, despite 'regression' being right there in the name. Like decision trees (CART), random forest and other similar methods, there are both regression and classification versions of the method. In a binary outcome problem, we model the process as flipping a coin that has a probability p of coming up heads (1) and 1-p of coming up tails (0). We can either predict p, a continuous value between 0 and 1, or we can predict the outcome, itself, which is in {0, 1}. The former is regression; the latter is classification. Logistic regression predicts that probability p and logistic classification takes that probability and uses a cutoff (sometimes, but not always, 0.5) to predict the outcome. Many times, the probability is more important (for example, lots of the same type of customer -> you do not want to assume all of them are 1's, when p is estimated to be 0.6) and sometimes the outcome is more important (for example, making a decision to approve an application for credit.)

[maxturgeon]

It may surprise some people, but logistic REGRESSION is in fact a regression model. The output is continuous: it's a probability. Of course you can dichotomize the output and create a classifier. Just like you can dichotomize any score function or regression model output. So the distinction between regression and classification is not so clear cut.

[KennTollens]

Thank you, I really needed an overview. Now I have a container to put the information in as I learn it.

[pauloszewierenko]

Great ! It gives an overview of the subject and acts as a roadmap to dive deeper into the study of ML

[rkpunbf]

Been trying to fully figure it all out for a while and here you were concisely explaining the key pointers in exactly 5 mins. Thanks for your help & keep on with the good job mate !

[isaachernandez]

I'm taking a machine learning course and this is what I needed to completely understand each method. Great video, concise and easy to follow. Thanks!

[edwardalabi]

Amazing content, straight to the point whilst still being detailed

[byrospyro]

You can also get multi-class classification where the output is more then 2, also you can use neural networks for classification also.

[bunnyistaken]

It was an amazing refresher for me.. Now I know where I need to concentrate more and learn more.

[CC-edjr]

Thank you, my class was s bit overwhelming and this helps to distinguish the stuff more

[luiscastellanos]

I love how the explanation is made to everyone with experience or not in this issue. I’m in the second group and now I have a reference to start. Thank you ☺️

[ernestoa]

Muchas gracias. Video muy ilustrativo, concreto y preciso para tener un panorama de los modelos de aprendizaje automático (ML)

[TheRealDouglasJOlson]

I find that you glossed over the details of the more difficult models. But provided examples and more depth for the simpler models. For individuals learning ML for the first time, there is a need for people to understand the more complex models in depth. I suggest adding deep dives into each specific model.

[CromaCampusOfficial]

Thanks for explaining all the learning modes of machine learning.

[levon]

Really nice summary, thanks for putting this together.

[PalataoArmy]

Only few thousands sees this video and I found this is the most brief and easiest to understand the basics of ml model. Thank you.

[anandnarvane]

He has expainened all branches of machine learning is good for biginers like me. To understand ml..

[MrShubi]

i needed this straight forward video for a very long time thank you so much :)