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Can anyone tell me what should I learn first AI or ML?

 
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Hi

Can anyone help me in suggesting the best course preferably to learn as a fresher?

Would it be better to go with AI or Machine learning?
And certainly what will be the duration and methods of learning this course ?
 
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peterr paul wrote:Hi

Can anyone help me in suggesting the best course preferably to learn as a fresher?

Would it be better to go with AI or Machine learning?
And certainly what will be the duration and methods of learning this course ?



Machine Learning is a type of AI. AI broadly includes symbolic rule-based approached we used many years ago. Today the paradigm is Machine Learning, i.e., we train systems by giving them examples of input and output signals, rather than program rules for every situation, which doesn't really work for complex problems.

TL;DR - Machine Learning is what you are looking to study, and that is the most prominent type of AI you should care about right now
 
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There are some great courses my Andrew Ng on Coursera.
https://www.coursera.org/learn/machine-learning/home/welcome
https://www.coursera.org/specializations/deep-learning#courses
 
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My advice is don't do online courses - instead do practical calculations on the dataset(s) and then eventually go back to theory later.

Go to Kaggle as early as possible and start competing and don't be afraid to fail, because 'practice makes perfect'.
 
Greenhorn
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I agree with Lucian, project-based learning is a great way to focus on getting practical skills without going uncesessarily too deep in theory

Kaggle is a great source of inpiration for projects, I do recommend checking it.

You can also check a repository with the projects from my book: https://github.com/alexeygrigorev/mlbookcamp-code

There are 4 example projects so far:

- predicting the price of a car
- churn prediction (determining which customers are likely to stop using the services of a company)
- credit risk scoring
- classifying the type of clothes
 
Greenhorn
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Hi everyone,
   I do not completely agree with the "don't do online courses" advice, actually.
I mean, I agree that practical examples are very useful and you should definitely try them out if you really want to grasp the subject.
But, as my personal experience, I started looking into some of the practical examples in ML and had the feeling that I was looking at a black box, and when it was time to change maybe some parameters and see what was going on I realized that I had no idea where to start.
That was the point I decided to start from the theory, instead, and I took one of the mentioned online courses and found it really helpful.
Most of these courses, in any case, combine theory with practical exercises, so you can try out things by yourself, but understanding what you are doing and why you are doing it.
So, my personal advice, is try to find a good combination of theory and practice, as in most of the things, so you understand how to do things but also why!
 
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Ilenia Salvadori wrote:. . . good combination of theory and practice . . .

If only all courses were like that. Some face‑to‑face teaching is bad, too, but it is much easier to put a poor course on the Net than persuade somebody to pay you to stand up and talk.
If only it were easy to tell that a course is any good . . . before paying lots of money for it.
 
Ilenia Salvadori
Greenhorn
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I did not say it would be easy
But I personally had a good experience with the online course by Andrew Ng on Coursera and I would recommend it.
That said, I think the "good combination of theory and practice" I was talking about also depends on what background you have.
Some people find easier to learn directly through practical exercises, and are maybe "scared" by too much theory, while some other (like me, for instance) feel more comfortable getting first a theoretical basis, before trying things out themselves.
But for sure you would need a bit of both to really grasp a topic!

 
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It is not necessary to learn Machine Learning first to learn Artificial Intelligence. If you are interested in Machine Learning, you can directly start with ML. If you are interested in implementing Computer vision and Natural Language Processing applications, you can directly start with AI. ML is not a pre-requisite for AI or vice-versa. The only requirements before starting either ML or AI are programming skills, statistics, and linear algebra.
 
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