Introduction to AI:
Between the years 2010 and 2019, the number of job postings for Artificial Intelligence skills grew by a factor of ten in absolute numbers and by a factor of four as a proportion of total job posting.
Therefore, if you are planning to learn about AI, then this is probably the best time.
The field of AI and machine learning has evolved at a fast pace and grown tremendously in the last few years. Today, a quick Google search will introduce you to a world of courses by experts. These courses are polished and available on high-quality open-source software tools and libraries.
Every day, you will find new online courses or blog posts. This is because machine learning has driven billions of dollars in revenue across industries. It has enabled unparalleled resources and new job opportunities.
This might seem a bit overwhelming in the beginning and if you are thinking, “can I learn artificial intelligence by myself?” The answer is yes.
But here’s how I’d like to approach it, in case you ever got stuck anywhere in this process, you can always go on Quora or similar places like Kaggle. A simple search in any of these pleases will solve your problem (because there is a good chance someone has hit your issue before).
You can also post on these forums, which will both give you answers and help you be a part of a community.
Without wasting more time, I will start with the steps you can take to start learning AI in the first place and then we go deep into how you can learn.
How to Self-study Artificial Intelligence?
1. Start by Asking “WHY”?
Before starting anything, it is important to know why you are starting.
Start questioning yourself – why?
Some of your questions can be:
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Why do you want to learn these skills?
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Do you want to build things?
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What do you want to achieve at the end of your learning?
Obviously, there is no right or wrong reason as all are valid in their own way. But, you at least know why you started which will keep you motivated in your journey.
2. Start with a Problem or an Interesting Topic
Your first step on your journey will be to select either a problem or a topic that you might find interesting. This will ensure that you are more focused on finding a solution and learning more of it without getting bored.
There is a wide range of topics in artificial intelligence; you can choose to learn about AI assistants found in every mobile phone to Chabot available on every website.
Artificial Intelligence can be both interesting and mundane. But, it is you who is responsible to make it fun and interesting. Randomly choosing a topic will only intimidate and disconnect you.
Choosing a topic you are interested in will also help you stay motivated and more involved in the process of learning.
The first part of any learning process is reading, so start reading. You can focus on finding a solution for a certain problem instead of just passively reading. Solving a problem will deepen your focus and increase your engagement with the topic.
A good problem will cover an area you have a personal interest in, data is already available to address the problem, and you can work with the data comfortably.
If you still cannot find a problem, no worries! Go one Quora or Kaggle and you will find an onramp of machine learning problems.
3. Look for a Quick and Simple Solution
Getting bogged down in the implementation of detail of the wrong machine learning algorithm is easy. You have to avoid this!
Here, your goal will be to get something basic that covers the problem from reading the data, training a basic model, processing it for ML, creating a result, and evaluating its performance.
4. Play with your Solution
Once you find a simple, quick, and easy solution, it is time to get a little creative. Developing an easy and simple solution will help you understand the basics. It will create a great foundation for you to start experimenting because we all know it is the best way to learn.
So, start by taking baby steps towards improving each component of your initial solution. After every little change, you need to measure its impact. This will help you understand where it makes sense to spend time.
There are so many instances, where acquiring more data or improving data cleaning and pre-processing steps can provide you with a higher ROI than optimizing the ML models.
In this part, you should include being hands-on with the data. For instance, inspecting individual rows and visualizing distributions for a better understanding of its structure. This will give you a deeper knowledge as getting your hands dirty is something that you will never regret. Try some free Data visualization tools to make it much easier to grasp the trends and tendencies your data represents.
5. Get Feedback
Now is the time for feedback!
No one is perfect. Everything and everyone is constantly “under construction”.
We need to find our mistakes and learn from them. However, most people cannot see our own mistakes. Therefore, you must get feedback on your solution.
But, where can you get feedback and from whom?
Well, there are millions of forums out there where you can share and discuss; you can even join a community. Write about your solution and post it, then collect as much feedback as you can.
Trust me it’s the best way to understand it better.
This will enable two ways of learning as others will understand what you’ve done and provide feedback while you will also learn.
6. Help Others Learn
The above steps will help you learn, but to make you grasp over the concept more firm, teaching others will help.
There’s a lot of different ways you can interact, learn and help people learn, for instance, writing research papers, blog posts, or tutorials, answering questions on Kaggle, Quora, and other sites, or mentorship and tutoring.
7. Repeat, Repeat, Repeat
We all know the one-way route to success, which is constancy and failure.
Repeat this process as many times as you like. Pick up a new problem or topic and start working on it.
Doing it several more times across a different set of domains will give you an edge over others.
You can simply work on a topic or problem similar to the first one. This will give you a deeper and wider knowledge of that.
8. Compete and Learn
Now that you have learned a good part of your machine learning. It’s time to give a shot to the same problem that thousands of others are working on.
This is a tremendous learning opportunity as it forces you to iterate on the problem over and over again.
Some forums for an individual competition also provide you with information on how others are approaching the problem. This will give you precious insight into other’s perspectives.
And the best part, the winning blog posts are showcased at the end of every competitor which will be a great opportunity for you to build your portfolio.
Here is a list of competitions that are great for you:
· Kaggle
· Coding Game
· HackerEarth
· Robocup
9. Start Applying your Knowledge Professionally
All the steps above will motivate you, help you learn, and build your portfolio. Now, it is time to decide what type of role you’d like to pursue. You need to start adding great achievements in your portfolio of projects related to this.
If you’re not ready for a job, that’s okay too!
You can start taking new projects in your current role, seek consulting opportunities, and involve yourself with data-related community service opportunities.
All these things will give you an additional foothold in the vast field.
Conclusion:
Artificial intelligence self-study might not sound so simple, but once you put your legs in the water, you’ll realize that it’s not that cold after all.
If you are looking to learn Artificial Intelligence, following all the steps above will be enormously helpful.