How to Learn Artificial Intelligence

Artificial Intelligence (AI) has been a hot topic in the news over the last few years and with good reason. While it is often unclear exactly how AI works, the field of AI is expected to play a growing role in our lives in the future.

AI algorithms help computers accurately complete pre-assigned tasks. For instance, Amazon likely uses AI to inform their Alexa voice assistant, which learns how your voice sounds so that Alexa can provide tailored services when you call out “Alexa.”

This guide charts about how you can learn Artificial Intelligence. We will start by talking about what you need to learn. Then, we will discuss courses and other learning resources on which you can depend to learn about AI.

What is Artificial Intelligence?

Artificial Intelligence refers to the tools, processes, and technologies used to create human-like intelligence in machines. AI could help automate tasks such as driving a car which may have otherwise been performed by a human. AI algorithms are built by reading large datasets and building programs that are capable of learning from the data they consume.

AI may help automate repetitive tasks and should execute those tasks in the way that a human would. Here are a few ways in which AI is used:

  • Recognizing images. Detect violence in a photo, or to turn the contents of a written document into a digital one.
  • Spam detection. Detecting email or text spam.
  • Driving cars. AI cars may be able to drive.
  • Reading and responding to text.

While AI is commonly associated with fiction and movies, today AI is a crucial part of our day-to-day lives. Amazon’s Alexa uses AI technology to improve even if the assistant is not entirely built on AI. And the possibilities are only starting to become clear. AI is a new field in the world of computing and so the applications of AI are still being explored.

What You Need to Know About Artificial Intelligence

AI focuses on creating computer programs that improve over time. It is unclear exactly what an AI program knows at any given time but the architecture behind the program is clearly defined by a professional.

Below are a few concepts you will need to learn about if you want to pursue a career in AI:

  • AI Terminology. What is AI? How does AI learn? Where is AI used? You must be able to answer these questions accurately before you start to implement an AI algorithm.
  • Learn Machine Learning. There are significant overlaps between machine learning and AI and so having an understanding of machine learning will go a long way. Learn about clustering, k-means, framing problems, preparing data, recommendation systems, debugging machine learning algorithms, and any other basic machine learning concepts before you start to write AI programs.
  • Learn an AI Tool. You will need to feel comfortable working with an AI tool to write algorithms. For instance, you may decide to learn TensorFlow, an open-source machine learning platform by Google. Or you may learn scikit-learn, a Python library used for machine learning and artificial intelligence.
  • Ethics of AI. AI has become subject to many ethical debates. Should AI be limited by legislation? What tasks need to be done by humans rather than computers (i.e. certain healthcare analyses)? You need to learn about the ethics of AI which will come up in any role involving AI.

These are four areas in which you will need to be proficient to become an expert of AI. Of course, all of these areas are broad classifications. Each area includes its own topics that you need to know. Some of the AI specific concepts with which you will need to be familiar include:

  • AI algorithms
  • Algorithm optimization
  • Planning projects
  • Pattern recognition
  • Supervised and unsupervised learning
  • Neural networks
  • Using PyTorch and scikit-learn (assuming you use Python for your work)

As you learn more about AI, you will develop a clearer understanding in your mind of exactly what you need to learn to prospect in your career.

Skills Needed to Learn Artificial Intelligence

You will need to have a deep understanding of programming, algebra, statistics, and data analysis before you begin learning Artificial Intelligence. All of these areas will come up in your day-to-day work and it is hard to comprehend many AI concepts without feeling confident in all of these areas.

AI is not an area that you learn overnight. People who decide to specialize in this field usually do so after some experience in another domain, such as machine learning or data analysis. This is because AI is very complex and other data-driven fields give a good foundation upon which you can build a career in AI.

Why You Should Learn Artificial Intelligence

Learning Artificial Intelligence will open up a number of opportunities to work on interesting projects. Today, AI is used for applications like building self-driving cars, managing traffic, and building voice assistants. You could work on one of these complex projects if you had a solid understanding of AI.

In addition to the type of work you would be doing, you would be fairly compensated. Indeed, AI engineers are some of the highest-paid coders due to the amount of time and effort one must expend in order to become proficient in AI engineering. We talk more about the career prospects in AI later in this article.

How Long Does it Take to Learn Artificial Intelligence?

Assuming you know a programming language but have never done any data analysis, expect to spend about six months learning the basic data concepts you will need to know to learn AI. You should expect to spend another six months to a year mastering the basics of AI, at the very least. You will probably have to study for a few years before you are ready to work in a job in AI, even at the entry-level, due to the complexity of the work.

Learning Artificial Intelligence: A Study Guide

There are many online resources on which you can depend to build an understanding of AI. A lot of the resources you will find online are positioned for people with an understanding of mathematics, data, and programming, but some courses do not require as much experience.

Below we have curated five resources which will help you build an understanding of AI.

Introduction to AI by IBM on Coursera

  • Resource Type: Course
  • Price: Free
  • Audience: Beginners to AI (No programming experience necessary)

This course, which takes about eight hours to complete, is a good introduction to basic AI concepts and terminology you are likely to encounter. In this course, you will build a basic understanding of what AI is and some jargon in the industry. You will also touch on the basics of AI ethics as well as what problems AI is uniquely positioned to solve.

This course will not teach you how to implement any particular AI algorithms or use a programming language to write AI code. The subject matter of this course is the basic theory behind AI, which is why no programming or computer science experience is necessary.

Artificial Intelligence Nanodegree by Udacity

  • Resource Type: Course
  • Price: $399
  • Audience: Beginner to AI

This course will teach you the technical foundations of writing AI programs. To participate in this course, you must have a good understanding of calculus, algebra, statistics, and Python.

The AI Nanodegree is split up into four modules: constraint satisfaction, “search, optimization, and planning,” adversarial search, and fundamentals of probabilistic graphical models. You will learn how to write Python code to build a series of projects which showcase your knowledge of AI in this course.

AI Programming with Python Nanodegree by Udacity

  • Resource Type: Course
  • Price: $399
  • Audience: Beginner to AI, knowledge of algebra and Python necessary

This course teaches the fundamentals of AI programming using the Python language. This course is good for people who have an understanding of Python but want to expand their knowledge into the field of AI.

In this course, you will learn how to use Jupyter Notebooks, Anaconda, NumPy, Pandas, and Matplotlib, which are all commonly used in AI. You will also learn about the mathematical concepts you will use in an AI career. Like all Udacity Nanodegrees, you will work on a series of projects to help you reinforce the knowledge you learn in the classroom.

Get Started with AI by Google AI

  • Resource Type: Directory of resources
  • Price: Free
  • Audience: Beginner to AI

The Get Started with AI directory is a list of tutorials and tools you can use for AI development. For instance, you will find resources on AI for social good, the human choices involved in designing machine learning algorithms, and clustering.

This directory is a great place to go to build an understanding of the technical details behind AI and learn about some key terminology and responsible practices.

Kaggle Courses

  • Resource Type: Course
  • Price: Free
  • Audience: All audiences, basic programming experience preferred

The courses listed on Kaggle’s Courses page are a good place to go to learn about data analysis, machine learning, and the basics of AI. Each course features detailed tutorials and code snippets to help you master a range of data-related topics. For instance, the data cleaning course tells you how to handle missing values, parse dates, and deal with character encodings.

Communities for People Studying Artificial Intelligence

There are a few online communities to which you can go with questions about AI or to find other people who share your interest in AI. Below we have curated some of the top communities for people studying AI to look into joining.

Kaggle

Kaggle has a community forum in which you can post questions related to data analysis and science. Some discussions talk about AI, too. You will also find datasets on the Kaggle website that you may be able to use in your projects.

r/artificial on Reddit

The r/artificial community is a good place to stay up-to-date on the latest in the AI community. You will find a lot of learning resources and project features on this Reddit forum as well as a host of videos that display AI at work. A big part of this community is entertainment, as many of the projects taken on by members have a playful tone.

Stack Overflow

Stack Overflow is a good place to go with any questions related to programming. In the context of Artificial Intelligence, you may find threads that feature topics you are currently learning or working with. You should find no trouble finding answers to data science questions on Stack Overflow (many data science questions can be applied to machine learning in some way).

How Hard is It to Learn Artificial Intelligence?

AI is built on the foundations of mathematics, programming, and data, so you will need to know these subjects before you even start learning about AI algorithms. For this reason, learning Artificial Intelligence will take an extensive investment of time and energy. You will face a lot of challenges learning AI, even if you are a competent developer.

If you stay focused on your goal of learning AI and are willing to invest what might be years into studying AI, you can acquire a strong understanding of AI development.

Will Learning Artificial Intelligence Help Me Find a Job?

AI is an emerging field and as a result jobs in this industry are opening up all the time. Jobs in AI are not as available as jobs in other computing fields like data science because AI is quite new. But, as the potential of AI is explored further, jobs in the industry will become more common. Below, we provide some statistics on the impact of AI on your employability.

  • Salaries. Glassdoor reports the average AI engineer earns $114,121 at the time of writing this article. That salary is much higher than a lot of other occupations in the technology field. With more experience under your belt, you may be able to earn a much higher salary than the average.
  • Job Openings. Glassdoor reports there are 4,544 open jobs that involve AI in the United States.
  • Industry Growth. The U.S. Bureau of Labor Statistics estimates employment for computer and information research scientists will grow at a rate of 15% (“much faster than average”) between 2019 and 2020. This category includes AI engineers.

Conclusion: Should I Learn Artificial Intelligence?

Artificial Intelligence (AI) is a complex field that presents a lot of opportunities. You could use your knowledge to help improve healthcare services. Or you could use your knowledge to build autonomous cars. Or voice assistants. Or programs that analyze text for certain terms.

You will need to devote months to learning the basics of AI and perhaps years of study before you are ready to work in a job in the field. So, you need to carefully consider if you are ready to invest the time it would take to become an AI engineer. If you are really interested in the field of AI, you can justify this decision. Otherwise, you may need to spend more time thinking about if AI is for you.

As applications for AI become clearer, employment for AI engineers will rise commensurately. Because AI is a new field, we can expect a lot of developments in terms of improved technologies, greater understanding of techniques, and new problems which AI can help solve.

Find the best data science bootcamps to get you hired.

bootcamprankings

Get matched to top data science bootcamps

By continuing you indicate that you have read and agree to Study Data Science Privacy Policy

Powered By
Career Karma

X

Register

You don't have permission to register