Are you interested in becoming an AI Engineer?
Want to know if you can be a self-taught AI Engineer?
In this article, we’ll discuss topics relating to being a self-taught AI Engineer
So can you be a self-taught AI Engineer?
Yes, it is possible to AI engineer yourself. If you already have a strong background in Mathematics and Computer Science, you should not have a problem becoming an AI developer/engineer. AI Engineering can be self-taught, ideally through reading books and reputable Coursera courses.
Once you understand the logic, it’s fairly simple. It shouldn’t take too long.
However, the answer is the same as for any “is anything difficult” question. It’s not that difficult once you’ve grasped it.
Before we go any further, it’s important to understand what an AI Engineer is,
AI engineers are in charge of creating new apps and systems that use AI to boost productivity, make better decisions, save expenses, and raise profitability.
Artificial intelligence (AI) is the ability of a computer or a computer-controlled robot to perform tasks that are normally performed by humans and require human intelligence.
What is considered today as an “AI” skillset is a mix of mathematics (esp. statistics), computer science, and application-related skills.
Can I Become An AI engineer Without A Degree? (Solved)
Yes, you can become an AI Engineer without a degree. However, you do need to demonstrate to companies that want to hire people who know the field they are applying for, and you somehow have to prove that you have the skills.
There are certificates available, such as those offered by Coursera, that can provide you with certificates for completed courses that can be used to demonstrate your knowledge to potential employers.
These qualifications will improve the value of your resume and help you get an in-depth understanding of AI topics, as well as raise your income to match that of an AI Engineer.
What Is The Easiest Way To Self-Learn AI Engineering?
Online courses are the easiest way option to self-learn AI engineering. The advantage of online classes is that you may learn at your own pace, from anywhere and at any time that is convenient for you.
And even better news the courses online are cheap and often free – just got to pick a reputable course.
It is important to begin with, the fundamentals and works your way up to more complicated courses in any learning journey.
Below are some reputable courses online through Coursera.
There are other Artificial Intelligence courses on Coursera, but the ones below are worth mentioning.
Introduction to Artificial Intelligence
Rating: 4.7 out of 5 Stars
Duration: 11 Hours to complete
28,078 already enrolled
This course is designed for beginners.
In this course, you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics bias, & jobs, and get advice from experts about learning and starting a career in AI.
You will also demonstrate AI in action with a mini-project.
This course is run by the prestigious Darden School Foundation.
Course Subjects
- What is AI? Applications and Examples of AI
- AI concepts, Terminology, and Application Areas
- At Issues, Concerns and Ethical Considerations
- The Future with AI, and AI in action
In the end, there’s a Shareable certificate that can help you get a job.
What The Reviews Are Saying
Great Module to understand basic AI & how it will be useful. Watson lab was worth doing, it is very interesting how AI is classifying the object in the picture. Will be doing the next module shortly.
Source Coursera
This introduction course is a great way to start your way in the AI world. It gives some advice that will give an idea to newbies on what to learn particularly they recommended to learn math and stat.
Source Coursera
The course design is excellent especially for beginners to study and understand the basic concepts in Artificial Intelligence. The lessons and course material are perfect and apt for this course level.
Source Coursera
AI For Everyone
Rating: 4.8 out of 5 Stars
Duration: 11 Hours to complete
34,770 already enrolled
This beginner course is run by DeepLearning.AI
In this course, you will learn: The meaning behind common AI terminology, including neural networks, machine learning, deep learning, and data science. Including, what AI realistically can–and cannot–do.
- How to spot opportunities to apply AI to problems in your own organization.
- What it feels like to build machine learning and data science projects
- How to work with an AI team and build an AI strategy in your company
- How to navigate ethical and societal discussions surrounding AI
- Though this course is largely non-technical, engineers can also take this course to learn the business aspects of AI
What The Reviews Are Saying
I used to have an abstract idea of what AI is. Most times, all I can think of are robots when it comes to AI. The course is an eye-opener for anyone who wants to understand AI in the simplest of ways.
Source Coursera
Everyone should take this course small step for a big picture. Andrew Ng is awesome when explaining things. The quality and background are good compared to other videos. Became a fan of Coursera after this.
Source Coursera
I got a comprehensive overview of what AI is and the meanings of various concepts being talked about in this context. Excellent course for one to start on solid ground. Five stars! Thank you, Andrew.
Source Coursera
Machine Learning
Rating: 4.9 out of 5 Stars
Duration: 61 Hours to complete
4.6m already enrolled
Thing mixed skilled course (beginner and advanced) is offered by the highly prestigious Standford University.
This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition.
Topics include:
(i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks).
(ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning).
(iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).
The course will also draw from numerous case studies and applications so that you’ll also learn how to apply learning algorithms to building smart robots (perception, control), text understanding (web search, anti-spam), computer vision, medical informatics, audio, database mining, and other areas.
What The Reviews Are Saying
A brilliant sequence of topics and fundamentals to get a stronghold on ML. The learnings I obtained from this course will always be my guiding factor in working through the projects in my life ahead.
Source Coursera
Excellent course, a highly mathematical overview of how introductory machine learning models work. Thanks to Andrew Ng for putting together a lot of great material and challenging quizzes and exercises.
Source Coursera
Great overview, enough details to have a good understanding of why the techniques work well. Especially appreciated the practical advice regarding debugging, algorithm evaluation and ceiling analysis.
Source Coursera
What Skills Do You Need To Become AI Engineer?
While the most in-demand skills vary by organisation, depending on business objectives, there are several essential competencies that are valued by all companies in AI engineers. Engineers must be able to examine enormous volumes of data for patterns, understand algorithms in-depth, and have problem-solving and math skills.
According to recent job postings, the major tech companies expect their AI engineers to be proficient with:
- Java: 60%
- Artificial Intelligence: 57%
- Software Development: 48%
- C++: 38%
- Linux: 37%
- Python: 36%
Final Thoughts
Yes, you can learn AI Engineering on your own. Self-education in AI Engineering is possible, ideally through books and respected online courses such as Coursera.
There are three basic methods for self-learning AI Engineering.
The first and most efficient method is to study through online courses, while the second is to study through traditional methods such as reading books.
Finally, the third and most costly is enrolling in and attending college.
Online learning is the most enjoyable, convenient, and cost-effective way to learn and study Network Engineering.
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