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There is no denying that artificial intelligence has been making a change in every industry, whether it is healthcare, finance, entertainment, or self-driving cars. But does one really need a degree to learn about AI? The answer is definitely no. You can find an engagement for yourself in AI self-learning without literally graduating from any formal institution. This is how you go about it:
You need to have a working knowledge of the basics without which you shouldn't jump into AI. This comprises:
Mathematics: This is basically the ground upon which various AI capabilities are resting. In some broader sense, it revolves around linear algebra, calculus, probability, and statistics. These are what the machine learning techniques build upon.
Programming: One language must be mastered in deep depth, such as Python, R, or Java. In AI, Python has been very much in use because it is relatively simple, and many libraries and frameworks to support it have been developed.
Computational Thinking: To know model building for artificial intelligence and how further optimization is done, one has to be well aware of the concepts of data structures, algorithms, and computer architecture.
There's so much learning you can find on AI across the web. So, it is impossible to count great institutions and learning platforms offering free or almost free courses in which they walk through all the matters about AI. The most famous among them are Coursera, classes of giants like Stanford and MIT, that walk through all of these matters. Those most vital courses worth recommending are `Machine Learning' by Andrew Ng and `Deep Learning Specialization'.
edX: Courses by Harvard, Berkeley, and more. Great offerings for beginners include "CS50's Introduction to Artificial Intelligence with Python."
Udacity: Although it has deeper "Nanodegree" programs on AI, Machine Learning, and Deep Learning.
Khan Academy: This makes courses on math and on the very basics of computer science available for free.
Books are a good source for deepening one's knowledge. Some of the books on AI, and which I can recommend to participants as a learning resource, are:
"Artificial Intelligence: A Modern Approach" by Stuart Russell and Peter Norvig. They provide fairly detailed coverage of arguments on all the topics within the realm of Artificial Intelligence.
"Deep Learning" by Ian Goodfellow, Yoshua Bengio, and Aaron Courville. These are only introductory materials to deep learning.
"Pattern Recognition and Machine Learning" by Christopher Bishop: This book is really very good for the basics of machine learning.
Follow research in AI on NeurIPS, ICML, or CVPR to keep up with everything new.
Theory is not enough. In AI, practice counts. Here's how you practice:
Kaggle: Use it to participate in data science competitions. Participating in the Kaggle competitions provides real-world problems with insights, which are, in turn, learnings from fellow participants.
Go through open-source AI projects on GitHub and chip in. Through contributing, you will learn how AI models have been implemented and also accumulate practical experience in coding.
Personal Projects: Work on your own AI projects, anything from a simple chatbot to a very sophisticated system of image recognition. This reflects skill and can be incorporated into the portfolio.
Get involved in AI communities for support, inspiration, and the creation of opportunity, among many other learners and professionals. Here are some ways you can connect with the AI community:
Online Forums: Websites like Reddit, Stack Overflow, and AI-specific forums are excellent platforms to pose questions, share what you know, or even learn something from others.
Conferences and Meetups: Look out for the number of meetups, workshops, or conferences happening in and around your area. This helps in making learning from experts even more significant and improving the network among fellow peers, which in turn keeps you updated with the new trends in the industry.
Social Media: Stay abreast with the activity and contributions of all researchers, experts, and organizations worldwide working in the field of AI and allied domains. Very often, their content is in the form of sharing ideas.
While an undergraduate degree may not be essential, professional certifications can cry out to your skills and expertise. There are, of course, many. Most online platforms provide the following:
Google AI Certification: Provides several certifications, such as TensorFlow Developer and Machine Learning Engineer.
Microsoft Certified: Azure AI Engineer Associate: The critical competency with this is in using Azure services while undertaking the development and deployment of AI solutions.
IBM AI Engineering Professional Certificate: This one is another from Coursera and is specially meant for program addicts. It general courses on Machine Learning, Deep Learning, and AI Applications.
Since AI is high paced, one should keep oneself updated about the trends; the tools and technologies used, some of the below resources where that can be done
Blog Posts and News Websites: One must always keep in touch with the blog posts and news website related to AI, such as Towards Data Science, AI Weekly, and AI Publication on Medium.
Now a good portfolio explains all your skills and projects before potential employers or collaborators. The following are ways to develop an attractive AI portfolio:
Project Documentation: Keep records of all the projects you have worked on. Clearly mention the problem statements, approaches, tools used, and results. Share the code on platforms like GitHub.
Blogging: Your capacity to blog about your projects and learning experiences could well be your proof of concept for skills in communication. You can do this on platforms like Medium, and of course, personal blogs.
Personal Website: This is what everything links back to—a central location for your portfolio, personal blog, and your contact information.
