Let’s get straight to the point: there is no one BEST way to “learn AI” and join the industry (yes, I click-baited you). Instead, you have many options, some being better than others, rather than there being a completely optimal one. This article aims to guide you through some options while giving you the pros and cons of each so that you can start your own journey in this increasingly important field. 

Replicate an AI project

To define what I mean by “replicate an AI project”, I’m specifically referring to finding a popular AI project online that has been done before and trying to replicate it by yourself with as little help as possible. Adding onto that last point, it is fine to use documentation for whatever framework or library you are using or use StackOverflow when you find yourself stuck. In fact, using these resources will most likely help you learn the skills that you will use when, in the future, you start creating your own AI projects. Just try to not follow a tutorial that explicitly gives you the exact steps and code to create a functioning final product, as that defeats the whole purpose of replicating the project in the first place (more on this in a bit). 

Pros: 

By replicating a project that already exists, you get hands-on experience with coding an AI project while not having to worry about large roadblocks during your work. You get an introduction to the general process of creating an AI project, you get to freely make mistakes and subsequently learn from them, and if you want to go deeper, you can extend the project by testing different models, trying to collect or curate more data, doing hyperparameter tuning, or any other ideas you can come up with. Another pro is that if you are ever in a pinch and don’t know where to go, there are tutorials and code notebooks online that can help redirect you to the right path. However, the important idea to keep note of is that you should only use these resources when you are REALLY stuck. Treat this project as a practice test with an answer key: if you immediately look at the answer key after barely trying to find the answer yourself, you’ll end up doing nothing but wasting your time and being no smarter than you were before. 

Cons: 

This option may be daunting as it requires you to get out of your comfort zone and try to do something completely new without any guidance tailored towards you. This can be amazing for some people, but not so great for others, especially if you have no programming experience or if you are not good at learning by yourself and need a teacher figure to help you out. Also, if you inevitably end up just following a tutorial to create the project, then you’ll end up just wasting your time as said before.

Overall, replicating an existing AI project is an amazing option. You have the freedom to make mistakes, don’t have to worry about roadblocks, and you get hands-on experience that you can apply to future endeavors. The only real drawback to this option is that it may not fit the needs of certain people (which is completely fine). 

Before moving onto the next option, I want to quickly give two examples of projects that are good to start off with:

1. House Pricing Dataset
   • Goal: Predict house pricing based on the given input
   • General Guideline: Use linear regression, then try to test other regression models
   • Good introduction to general Machine Learning (ML)
2. Fashion MNIST
   • Goal: Predict clothing classification based on given image
   • General Guideline: Start with logistic regression or SVM, then try out Neural Network
   • Good introduction to Deep Learning (DL)

There are also many sources such as kaggle and youtube that have AI projects that you can aim to replicate.

Follow a course

Another option is to find an introductory course to AI and use that course as your gateway into the field. This option is more straightforward than the previous option, so we can just hop straight into the pros and cons. 

Pros:

Assuming you choose a good course, following a course can be a great segue into the AI field. You get a large burst of information and get to be taught by an industry professional. There are usually hands-on projects where you code along with the instructor, which also allows you to gain the experience and skills that you need when you start working in the AI field for real. Expanding on that “large burst of information” I mentioned before, courses can give you a lot of knowledge on the AI field, specifically by examining different sub-fields in AI, looking at different frameworks and libraries that are useful, and introducing vast amounts of terminology that you otherwise may not have ever learned. 

Cons: 

The largest con of following a course lies in that first statement I made in the pros section. Basically, there is a good chance that the course you choose ends up being flawed or possibly even a scam. There are many problems that a course can have, such as a lack of hands-on activities, having an unqualified instructor, containing too much conceptual jargon, as well as others. Another large disadvantage with courses is that you are prone to forgetting a majority, if not everything, of what you learned, even with the more hands-on courses. This is especially true if you do not apply the skills you acquired after you take the courses. 

Overall, following an online course is a relatively good option, but has many problems of its own. However, I would not completely disregard online courses, as there are many instances where they can be very informative and add to your skills in the AI field. Many courses are also tailored to beginners, so they can be helpful when you are just starting in the field (which I’m assuming you are if you are reading this article). As long as you do your research on the course you take and apply what you learn afterwards, then this option is definitely worth considering. 

To end off this section, here are some courses I recommend, both free and paid. 

Free:

1. Machine Learning for Everybody– FreeCodeCamp.org
2. PyTorch for Deep Learning – FreeCodeCamp.org

Paid:

1. Complete Machine Learning and Data Science Course – Daniel Bourke
2. PyTorch for Deep Learning – Daniel Bourke (paid continuation of the other “PyTorch for Deep Learning” course listed in the free section)
   

* Note: Most courses, at least on Udemy, go on sale for a lot cheaper than they normally are really often. If you do buy a course, make sure to wait until the price is ACAP (as cheap as possible).  Also, I am not in any way affiliated with any of these courses.

To summarize, out of the two options, I would definitely recommend replicating a pre-existing AI project if you want an introduction to the field. It gives you hands-on experience and the skills that you need to succeed in real-world AI projects. And unlike with online courses, you will likely remember most of the skills and information that you gained through replicating the project. However, depending on how you prefer learning and overall personal preferences, taking a course could be a better option. As I said, there is no one best option to “learn AI” and join the field. The best thing to do is to just dive in and start learning. No matter how you start, you will end up succeeding as long as you stay consistent and work hard…most of the time. Anyways, thank you for reading, and I hope to see you again tomorrow!