How To Get Started Learning Artificial Intelligence In 2021 [7 Steps]. When you want to read more about artificial intelligence, the first question you would have answered is; What exactly is artificial intelligence?
Following that, the chase begins. Artificial intelligence is described differently by all. And there seems to be no agreement about what this definition really means.
That is, before now. We will not only characterise artificial intelligence in this post, but we will also look at a step-by-step method to follow if you wish to pursue a career in artificial intelligence.
If you want to practise artificial intelligence in 2021, these are the exact moves you need to take. Still, before we go any further, let’s get the concept of artificial intelligence straight.
What is Artificial Intelligence?
Artificial intelligence (AI) is a field of computer science concerned with developing computers capable of executing functions that would usually necessitate human intelligence and action.
Artificial intelligence is a wide area that includes a range of methods and models, but machine learning and deep learning have seen the most important advances.
Even with the meaning, identifying what AI is can be difficult since the words are also used interchangeably.
For instance, you might be asked, “What is the difference between artificial intelligence and machine learning?” What about deep learning and machine learning? What is natural language processing and voice recognition?
What’s that? You’re about to lose your mind.
Simply put, consider artificial intelligence to be any kind of machine, bot, or robot that exhibits human-like intelligence.
A computer can learn from examples and previous interactions, identify objects, interpret language, make decisions, and solve problems using artificial intelligence.
Is that clearer now?
Nice. Now that we’ve addressed the challenge, “What is artificial intelligence?” it’s time to get down to business and figure out how to learn artificial intelligence in 2021.
Still, before we get into the how to learn artificial intelligence tutorial, let’s take a look at a few AI apps to see what we’re talking about.
Applications of artificial intelligence
Where does artificial intelligence come into play?
Although there are several different implementations of artificial intelligence in 2021, we will only look at the three most common ones for the sake of brevity.
However, I’ll write another post later on that covers all of AI’s current applications.
1. Marketing
Have you ever pondered the roots of your Netflix addiction?
When I learned about AI in marketing, the first thing that came to mind was how video streaming sites like Netflix and YouTube use artificial intelligence to hold you hooked.
It’s as if these platforms would read our minds with near-human accuracy.
Netflix will closely monitor your experiences with other movies, episodes, and films on their website, and then use predictive algorithms to make incredibly insightful recommendations for films and shows based on your past behaviour.
2. Banking
Have you ever had the suspicion that you were talking with a bot rather than a person on a help website?
Many businesses, including banks, have begun to use artificial intelligence to offer customer service.
Although, apart from that, the most intriguing use of AI in baking is in the detection of irregularities and fraud. Although the use of artificial intelligence (AI) for fraud detection is not a novel phenomenon, it has significantly improved security in a variety of industries, including banking.
Banks have been able to detect endpoint access and card use using artificial intelligence to efficiently deter fraud.
3. Trading
Finally, let’s take a look at how the pros are profiting from artificial intelligence in the stock market.
Have you ever used the phrase “you can beat the home” when it comes to gambling?
Ok, your ability to reliably forecast the future is crucial to your performance in stock trading. To me, it seems to be gambling. But you don’t go with your gut; instead, you rely on evidence.
Data scientists have created stock trading machines that can learn to understand trends in the past and forecast how they will replicate in the future, helping financial firms to boost their stock trading efficiency and maximise profits.
See what I mean?
This days, artificial intelligence is everywhere.
As a result, artificial intelligence developers are in high demand. So it’s understandable that in 2021, you’d like to learn how to start studying artificial intelligence from the ground up.
So let’s get this party underway.
How to learn artificial intelligence
How do you go about studying artificial intelligence and starting a career in this field?
There are now two types of people or desires in this community.
There are those who want to get into artificial intelligence for science and science, and those who want to work on applications in order to find a well-paying career in the field.
I’m not a researcher; I don’t really have a master’s degree.
So, if you’re looking for a mentor to direct you into AI research for academic purposes, I’ll give you a minute to leave. This guide is only for software engineers interested with working in AI.
Okay, that’s perfect.
So, in this segment, I’ll teach you how to study artificial intelligence from the ground up, as well as the skills you’ll need to acquire, how to publicise your newfound genius abilities, and how to secure your first job as an AI engineer.
Are you ready for this?
Let’s get this party underway.
1. Learn Mathematics
I remember I said this guide isn’t for those looking to get into AI for testing purposes… That was something I didn’t forget.
So you might be thinking to yourself, “But Math is for scholars, right?”
No, not at all. Unfortunately, if you try to practise artificial intelligence in 2021, you won’t be able to stop learning Algebra.
From my experience designing artificial intelligence applications, a strong base in linear algebra and calculus is so important that I’d slap everyone in the face if they said beginners in AI could miss this step.
This is why. You must be proficient in algebra to work with machine learning algorithms. Furthermore, neural network teaching necessitates a solid understanding of calculus.
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When you first start working with artificial intelligence, you’re eager to get started coding something.
But it also means you’ll have to wing it, with a lot of trial and error. This causes a lot of pain and aggravation. Furthermore, the last thing your manager needs to find out after you’ve been promoted is that you have no idea what you’re doing.
So, to begin, I recommend that you devote some time to a solid algebra and calculus course.
Instead of simply shaking your head, make sure you work along with the practise scenarios. If you don’t, you’re going to understand zero.
Here, I’m not suggesting that you should know everything there is to know about algebra and calculus before diving into artificial intelligence. To get started, you’ll need at least a rudimentary grasp of the fundamentals.
Aside from Algebra, I’d recommend brushing up on your probability and statistics skills with some tutorials.
2. Learn Code
Think a black screen with several odd characters that seem to be English.
Learning to code in any sort of programming language is the next logical step in getting started with artificial intelligence after learning Math.
AI programmes are simply software that is designed and developed to run on the target computers, allowing them to make observations, learn, and make decisions in the same way as humans do.
And it has to be done in a programming language of some kind.
Now, I don’t want to drive Python, which is my preferred AI programming language, too far, because there are plenty of other AI programming languages that are just as sweet.
My recommendation is to use a standard programming language rather than a less common language for two reasons:
- A well-known programming language would provide you with a plethora of high-quality artificial intelligence software and libraries that will function right out of the box.
- A mainstream language can also have the benefit of standing out in terms of efficiency because its authors have had ample time to tweak it.
However, C++, Java, Python, and even R are some of the more common artificial intelligence programming languages.
The point is to choose a programming language and get started with it.
What is the explanation for this?
Since, though having all of the Python fundamentals and then moving on to a more oriented Python course would be ideal, it would also require a significant amount of time upfront. Starting with a Python for AI tutorial, on the other hand, would easily expose you to the commands and functions unique to AI.
Notice that learning artificial intelligence is a project that most people begin yet cannot finish… they get distracted and quit.
3. Choose a Focus
If you’re still reading, congratulations; we’re on the verge of embarking on a journey that will undoubtedly take you to a rewarding and exciting career.
I’m sure you’ve perfected your math and numbers skills by now.
You’ve already picked up a programming language and are eager to get started creating AI applications and impressing your Twitter followers.
However, you must take a step back.
I discussed earlier in this article that AI is a vast field that incorporates a number of other interdisciplinary sciences. You can’t treat it with a catch-all attitude.
So, in 2021, the next step in studying artificial intelligence from the ground up is to decide which branch of AI you want to pursue… The better if you have a clear dilemma in mind to tackle.
Most online guides on how to get started with artificial intelligence won’t tell you this, but going at it without a specific objective or target is the leading cause of burnout and despair among AI beginners.
While discussing these types of AI will help you decide which way you want to go, it will also open up a can of worms because the online community has yet to settle about how many types of AI there are.
Furthermore, learning the different forms of AI would not help you determine what you want to read.
Types of Artificial Intelligence (AI)
Here are the two most widely debated forms of AI (Artificial Intelligence):
- Weak AI, also known as Narrow AI, refers to AI applications that concentrate on completing a single task at a time while optimising its execution. Self-driving vehicles, facial recognition apps, Google Translate, and Apple’s Siri are only a few examples.
- Strong AI, also known as General AI, refers to a broader machine intelligence that, rather than concentrating on a particular mission, teaches the machine to comprehend and reason on a broad basis, as a person does. There is currently no real-world application of Strong AI.
Can you see what I mean?
Since almost everything you’d like to build, or might potentially build, is in category one, this categorisation is absolutely useless in assisting you in deciding which path to take when getting started in AI as a beginner.
That’s why I said that having a specific solution or mission in mind would make it easier to find your concentration.
But if you’re really stumped, I’ve got something that could help you figure out what you want to do with artificial intelligence.
Let’s take a look at the most popular AI technologies. Since these technologies have varying strengths, they will at least give you an idea about what to concentrate your learning efforts on. The more proficient you become with some of these technologies, the easier it will be to venture out into others.
Artificial Intelligence (AI) Technologies
As an AI engineer, you’ll mainly work with these technologies. The bulk of them are derived from AI or from one another.
It should be noted, though, that this is not an exhaustive list. There are many AI technologies and fields, each with its own mathematical and engineering divisions.
But these are the most important ones for a beginner learning AI to investigate.
- Machine Learning is a term that refers to the study of AI is often discussed in conjunction with machine learning. Machine learning (ML) is a subset of artificial intelligence that seeks to create computers that can learn from data, recognise patterns, and make decisions without the need for human interference. It’s at the forefront of the majority of commercial AI applications.
- Deep Learning is a term that refers to the study of It is a subset of machine learning that focuses on teaching a computer to perform tasks such as speech recognition, image identification, and prediction using artificial neural networks and algorithms.
- Natural Language Processing is a term that refers to the process of It is a subset of artificial intelligence that uses text analytics to analyse sentence structures and teach computers to grasp, translate, and control human language.
- Computer Vision is a term that refers to the study of This branch of artificial intelligence entails teaching computers to perceive and comprehend visual information. Face recognition, image search, and licence plate recognition are all great examples of how this can be used.
- Robotics is the study of robots. Think robotics as the automation of operating business processes that imitate human actions. This is where most people get shivers when they believe AI can eventually replace humans. Yes, to an extent, but not totally.
Part on choosing your focus
This section on deciding on a focus was very lengthy, and I just scratched the surface.
So, if you’re a total beginner who wants to learn AI from the ground up, I hope you now have a clearer sense of where you should start.
To sum up this step in selecting a target, I believe that having a specific, well-defined target in mind is the best way to go.
You might, for example, say
“I’d like to develop a weather-prediction algorithm.”
It’s then a simple matter of organising and focusing your attention on this specific target, rather than being distracted by the vast array of AI possibilities available today.
Take a minute to find out what your target is for studying AI if you haven’t already. It would be immensely helpful in the future.
Now let’s move on to the next step, which is to find the appropriate learning materials.
4. Take an Online Course
There are a variety of free online resources available today that will assist you in mastering your special expertise in artificial intelligence.
But, with so many options, it’s easy to get overwhelmed… since you’re unsure which content is right for you and don’t want to waste your time
In any case, I agree that a decent video tutorial is a perfect way to go.
And, if you’ve already determined what you want to develop in the previous phase, it’ll be simple to restrict your quest for learning materials to those that concentrate on the technologies you want to learn.
I used a handful of books when I first started studying AI.
Later, I came across video tutorials on the internet, especially on YouTube, and I’ve never looked back. In reality, when studying AI as a beginner, I’ve found that working with a video tutorial is preferable to reading a book. A video guide/tutorial makes it easy to follow along.
Simple 3 step process that I use to find tutorials on Artificial Intelligence
I don’t want to waste too much time on this because everybody has their own interests when it comes to video tutorials. However, I have a basic three-step method that I use to find tutorials for every topic I’m learning.
- Enter the technology you’re attempting to understand and the term “tutorial” into Google or YouTube.
- Try each lesson from the search results before you find a teacher with a voice, organisation, and teaching style that you love.
- Take the course and complete it to the best of your ability. If you switch lessons in the middle of a lesson, you’ll end up with knowledge overload.
Your key aim should be to absorb or pick up as much artificial intelligence philosophy and information as possible when doing so.
If you find a good guide, it will have some practise scenarios and exercises that you can do with the teacher to insure that you understand the principles completely.
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The trick is to actually train rather than simply nod along.
There are now two forms of tutorials online.
The first are those who go into great detail on artificial intelligence theories and best practises without addressing any specific issues. To put it another way, you won’t be writing any code. These are the classes you should start with.
The ones that are jam-packed with realistic coding practise and exercises are the second form. This are the ones where you really put algorithms in place, feed them data, and then analyse and refine the results. This are the ones you should pay heed to. They assist you in solidifying the idea you learned earlier.
Although you’ll just be going along with the teacher, I find these lessons to be informative and enjoyable at the same time.
In 2021, after you’ve mastered this hand-holding, you’ll want to take the next step in mastering artificial intelligence from the ground up.
5. Build a Project
You learned how to choose the right lessons that can really help you understand artificial intelligence theory and experience in the previous segment.
But the most enjoyable (though challenging) aspect is now creating your own templates and apps without the assistance of a tutorial mentor.
Let’s look at how to design your own programmes, grow a portfolio, and eventually launch your AI models in this segment of getting started with artificial intelligence.
Also bear in mind that when you go to phase 7 and start searching for a real AI career, you’ll still need those portfolio projects you’ve been working on. During an interview, they’ll give you the chance to prove what you’re capable of. You can’t get a job in this sector without previous knowledge.
Methods of developing an Artificial Intelligence (AI) app
Here are three pointers to help you manage the methods of developing an AI app.
- Begin by solving a simple and straightforward problem. You can also play with various ways to leverage the strength of algorithmic decision making when you’re at it.
- Then, play with your simple approach to improve it. As a result, you essentially update different components while monitoring the results to see which one has the fastest solution.
- Thirdly, please keep in mind that everything should be done gradually. Start by writing basic neural networks, then progress to more complex ones as you gain experience.
Best artificial intelligence projects for total beginners
To get you started, I’m currently working on an article about the best artificial intelligence projects for total beginners. I’ll come back and refresh the reference once the article is over.
Meanwhile, here are three artificial intelligence research concepts that I find both fascinating and daunting for beginners:
- Prediction of housing prices. I’d choose a random place, say New York, and try to forecast the price of a new home there. I’ll need a dataset containing the prices of various homes in various parts of the city for this.
- Stock price outlook. Share business forecast is fascinating to beginners in artificial intelligence and machine learning. It’s because there’s still a lot of details online to get started with.
- Consumer endorsement. For an ecommerce website like Amazon, you can consider a consumer product recommendation framework. In this scenario, the customer’s search or buying background would be the primary data base.
While it can all sound like rainbows and sunshine, the key issue you’ll run into as soon as you start designing your own AI projects is that any of these projects would need a large amount of data to produce real results.
And this information isn’t always easy to come by.
The 3 key data bases – Artificial Intelligence
But here are three key data bases that I’ve found to be very helpful when I need to deal with datasets.
Then what’s the good news? The details is available at no fee.
With all of this in mind, I believe you now have a better understanding of what you need to do when it comes to developing your own ideas.
Of course, you’ll need some equipment to assist you in your work.
So I wrote another post on the best data science software & tools, which I believe would be a good addition to the AI toolkit.
But, just to give you a heads up, here are the three key methods you’ll be engaging with:
- TensorFlow
- SciKit
- Deeplearning4j
Let’s tie this up and move on to the next step in the guide to studying AI from the ground up. It’s getting out of control.
In the next part, we’ll go into how to deploy your apps.
6. Deploy & Compete
If you’ve made it this far, I’m confident you’ve created an incredible AI application that you’re ready to share with the world.
But you figure you’ll just put it up on joint hosting like a WordPress blog.
Unfortunately, running an artificial intelligence or machine learning programme effectively necessitates a large amount of hardware. Furthermore, the majority of shared hosting services would not be able to accommodate you.
So, what are the alternatives?
- Build a powerful computing hardware
- Use a cloud base supermachine
Because of the difficulty and costs involved, most people who study artificial intelligence fail to deploy their models.
However, having these model implementation capabilities on your resume would position you ahead of the competition.
I mixed deploying and competing because once the models are in the cloud, you can use them as a portfolio or show them…
But, most importantly, you must put your ability to the test at this stage.
How do you equate to other Artificial Intelligence (AI) researchers?
You’ve been working on your own concerns the entire time. You found and solved problems.
Now, for a multitude of reasons, this may be skewed:
- You could have preferred simple issues that did not sufficiently test you.
- You don’t get to pick the kind of AI implementation an organisation builds in the real world. They decide what they want and then ask you to join them.
- If you work for a corporation, you would almost definitely be part of a squad. However, AI theory does not teach coordination.
The benefit of participating in a competition is that you can bring your recently learned talents to work.
You do this by tackling challenges that other developers are tackling. Joining a Kaggle competition is the easiest way to get acquainted with this.
So, in addition to the wonderful ability to bring different techniques to the test in order to discover the right answers for the competition project challenges, you can also develop useful team collaboration skills.
You’ll be able to network and meet new people as a part of these competitions, which might help you succeed in your career.
Working on a project teaches you how to ask questions in AI, when to ask these questions, how to express your thoughts, and how to stay up to date on the new trends in AI.
Since, as I can testify, this area grows at such a fast rate that a one-year skill difference can appear to be a decade.
You could also earn money by participating in a draw.
We’ve almost completed the journey from studying AI from the ground up as a total novice to deploying the first app and competing in a race.
The only thing left is to go out and find a proper job.
7. Work
Shouldn’t you be looking for work now?
If you’ve worked in the software development industry for some amount of time, you’re well aware of the imposter syndrome that many developers undergo in their early careers.
It’s where you learned to code and designed applications, but you feel inept because you’ve never served in a business atmosphere or on a true customer-facing programme. As if your powers are still seriously missing.
And the only way to get rid of it is to get a real job and work, and then work some more.
So, at this stage, my advice is to find a career.
This is where the majority of AI developers encounter a brick wall and give up.
Despite the strong demand for AI engineers, getting a job in this area is still competitive and needs a plan.
Here’s a five-step plan for landing your first AI job:
- GitHub is a wonderful place to share your projects. Hiring managers like to see the code and how you put it into practise. As a result, make sure that all of the efforts are public on GitHub, preferably with ties to where they were deployed.
- Make a LinkedIn account. When looking to recruit in AI, most recruiting managers start with LinkedIn, a technical networking site. Make your LinkedIn profile more fun by listing and referring to your GitHub creations.
- This is an artificial intelligence blog. After all the scripting, problem-solving, and math, you’re bound to have plenty to tell about AI and machine learning. On Medium, write something about these topics. It’s possible that one of the readers is your potential boss.
- Take part in meet-ups. Apart from competing, attending AI-related meetups is another way to network and spread the news about your abilities. You’ll finally get heard if you participate in these groups and give presentations.
- Employment opportunities in AI and machine learning are available. So far, we’ve just discussed passive modes of transportation. However, you should regularly check for job openings on Indeed, submit, and see what happens. Doesn’t seem to sting, does it?
Okay, that’s it.
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All of this can seem to be daunting. As if you need to go through all of that just to get your dream career after months of studying?
Of course, you do not need to complete any of these steps to get there.
Still, if what you’re doing isn’t working, I’ve given you all of your options. You’ll probably settle on something if you do any of these.
That concludes this guide for full beginners on how to get started studying artificial intelligence in 2021.
Please share your views on this method of teaching artificial intelligence from the ground up in the comments section below.
Why Should You Learn Artificial Intelligence?
To sum up this guide to getting started with artificial intelligence, I’d like to share three explanations why I believe studying artificial intelligence in 2021 is a brilliant idea.
1. It is the skill of the century
It is a century-old talent. Let’s dispel the idea that AI is going to take on all human work and then wash civilisation out in one fell swoop.
That couldn’t be more untrue.
Although it is possible that AI would make certain employment redundant, it is an evolving technology that is creating ripples across almost every industry, from fashion to finance. It is estimated that more than 130 million jobs will be generated throughout the economy.
As a consequence, mastering artificial intelligence helps you to engage in this transformation.
2. Bright career prospects and pay
Why don’t you just follow the money?
If the fact that AI is a common ability to learn in 2021 isn’t reason enough, another reason you should consider learning AI is the promising job opportunities it provides.
Since AI is used in virtually every industry, the market for AI engineers has skyrocketed. It suggests that a significant number of firms are recruiting. Computer scientist, machine learning engineer, business intelligence developer, and other positions are available.
Furthermore, unlike other industries, jobs in artificial intelligence pays well, with average wages ranging from $100,000 to $150,000 in the United States.
3. Handle big data and some more
What about the excitement of actually dealing with a huge volume of data?
Look, we’re no longer in the nineteenth century (as if I know anything about the 19th century).
Humans, on the other hand, produce massive volumes of data, averaging 2.5 quintillion bytes per day. And no, I didn’t just pull the number out of my mind. Here’s where I got my details.
So, who is going to deal with all of this information and make sense of it? It’s you, of course. Whatever word you prefer: data scientist, machine learning engineer, artificial intelligence developer, etc.
You’ll be able to take this data, feed it into machine learning algorithms, and recover behavioural patterns in 2021 if you study artificial intelligence.
Consumer habits can be turned into very helpful observations for companies to turn a profit or for policymakers to defend themselves against a possible infringement that could drag a whole civilisation down.
Do you have a sense of how important AI will be in the near future?
Conclusion
I hope that this guide to learning artificial intelligence in 2021 has provided you with a roadmap for launching a fruitful AI career.
It goes without mentioning that everybody has their own method for learning artificial intelligence from the ground up… This is simply the approach that I found to be efficient.
The bottom line is that if you want to get into artificial intelligence, you can do it now.
It makes no difference where you come from.
You’ll have to work out the most of these stuff as you go along.
Are you a nascent artificial intelligence programmer or have you been working in this area for a few years?
What do you think are some of the better ways to practise artificial intelligence from the ground up that I haven’t included in this list?
In the comments section below, please express your reflections and experiences.
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