A Beginners Guide to Artificial Intelligence: The Learning Curve of AI
Welcome to AirVenture’s beginner’s guide to AI. This Guide should provide you with a very basic understanding of what AI/Ml/DL is, what it can do, and how to learn it.
Artificial intelligence brings a technological paradigm shift in whole IT sector. Now the job prospectives are changed, older monotonous jobs are suckup by artificial intelligence. But it also brings some new job opportunities. To grab those jobs today’s youth shown a keen interest.
But here the problem came, As the field of artificial intelligence is very new, So the learning curve and its trajectory seem a bit complicated for beginners & somewhere down the line it is true, as all that technological shift just happened in last 10 years.
Even I personally taking Beginners question over different social media platform. And over there I found Beginners are so excited to learn about artificial intelligence. But they don’t know how to &where to begin.
Few popular questions I recently answer over quora .
The list goes on… So I decide, Wouldn’t it be great if I made a simple and easy to understand beginner’s guide to artificial intelligence? So here the artificial intelligence Ai Venture guide to everything you need to know about artificial intelligence.
"From what it is, through how to use it and even where to learn it- I’ve got you covered!"
WHAT IS ARTIFICIAL INTELLIGENCE?
Step 1, Answering the question ‘what is artificial intelligence ?’
I am guessing that if you’ve landed on this page because you have some interest in demystifying the world of artificial intelligence. Fear, not all will become clear soon. I’m here to help you start to understand the topic in this my beginner’s guide to artificial intelligence.
Definition of artificial intelligence says:
Artificial intelligence is the theory and development of computer systems able to perform tasks usually requiring human intelligence, i.e the cognitive tasks such as visual, speech recognition, decision making, perception, judgment, problem-solving, memory. Sound Great right! Great to have a definition but what does that mean in practical terms.
What does that mean for you?
Essentially that means is that artificial intelligence is a machine having skills that mimic human’s in every aspect. One thing I want to make it clear that a machine having the intelligence to process human speech and converse is not the same as thinking like a human.
So In general Artificial intelligence (AI) is an area of computer science that emphasizes the creation of machines having intelligence that work and react like humans. AI is designed to have the most predominant capabilities that humans equipped with like-
Like to read more about Artificial Intelligence: Introduction of Artificial Intelligence.
Broadly, AI is classified into the following:
Artificial intelligence composed of :
- Machine learning: Machine learning is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Read more about “Machine learning”
- Deep learning: Deep learning is an aspect of artificial intelligence inspired from the structural and functional behavior of brain(the network of billion’s of the neuron), operates with an approach to learn things the similar way human brain did. Read more about. “ Deep Learning”
- Data Science: Data science is a multidisciplinary blend of data inference, algorithm development, and technology in order to solve analytically complex problems. Needs a partial knowledge of data science.
- Natural language processing: Natural language processing is a sub-field of computer science, information engineering, and artificial intelligence concerned with the interactions between computers and human languages, in particular how to program computers to process and analyze large amounts of natural language data.
Today tiny fragments of AI are all around us. Siri is AI, Alexa is AI, automatic turning off of the light based on the number of people inside the room is AI. But the full and final state of AI would be reached when the human mind can be implemented or emulated completely. That emulated human mind computer would behave exactly like the human and maybe even more efficient. It would provide exactly the same responses.
Before you get to start learning artificial intelligence make sure to learn the thing in small steps doesn’t go for the hardest first. If you start with the hardest stuff first, It will be way easier to get discouraged and give up so create small achievable goals during the learning process to stay motivated.
Make a daily plan and try to execute most of it.
1. Choose Programming Language:
The first thing you need to do is learn a programming language. Though there are a lot of languages that you can start with, Python is what many prefer to start with because its libraries are better suited to Machine Learning.
“Python is a Good Choice” scientific and numeric computing (with the help of libraries such as NumPy, SciPy, etc.), Support’s wide range of Libraries for various algorithms and have a large community in ML.
Read more about 5 best programming languages for AI development
Here are some good resources for Python:
- Python 3: Series of Basics
- Learn Python the hard way
- Coursera Python
- Introduction to Computer science
2. Basics Maths Knowledge about Algebra, Calculus, Probability & Statistics
This is must if you want to know what is really working behind scenes. Having some basic knowledge about it would be good Since we can take the advantage of Python Scientific libraries like Numpy & Scipy. Because while learning different algorithms you need to make visualization about the data & use its properties in algorithm’s using algebra, calculus concept’s.
3. Learn Python Libraries:
There are tons of machine learning libraries already written for Python. Just Learn it one by one. In Python, start learning SciPy, PyBrain, Matplotlib and Numpy libraries which will be useful while writing Machine Learning algorithms.
First actual step in learning artificial intelligence.
4. Andrew-Ng Course :
There is an Excellent and Highly Recommended Free Course by Andrew Ng at course, the course is a very good starting point for you to get your understanding about algorithms in Theory and the different concept’s to Machine Learning.
Now you know a bit about how the actual things are happening in artificial intelligence.
5. Learn Scikit-Learn Library :
One of the most powerful API with different Algorithms Powerful Data Encoders etc.
I would Highly Suggest you read Python Machine learning Edition2 by Sebastian Raschka
“I too read this book when I began my journey of learning AI. In this book, you get to know about how to implement different algorithms of machine learning”.
From theory(Mathematical Explanations) of different machine learning algorithms & optimization methods to practical code, cover large varieties of Practical Algorithms with Python, as well as Using it with Scikit-Learn API,
Here is a list of resources for you to learn & practice
- A Visual Introduction to Machine Learning
- Machine Learning (By Andrew Ng)
- Machine Learning Lectures (Tom Mitchell)
- Artificial Intelligence (edX) (Especially for practice exercise in Python)
- Intro to Statistics
- Intro to Artificial Intelligence (Includes Logic and Robotics)
- Artificial Intelligence
6. Practice time:
You should also take part in various Programming Contests at different places on the Internet. Make sure all these competitions are very time-consuming. Possibly No/ I think surely, you didn’t achieve a better rank in the beginning because so many wizards are working on them they. In the beginning, you are nowhere in comparison to their knowledge. So don’t lose hope, work continuously and learn each and every single day.
I personally never achieve rank among top 10. But still, I am working on them. Because to achieve rank you need to invest a lot of time which I lag. My prominent aim to participate in these competition is to learn more and explore more.
Remember while participating in these competitions your aim is not to win millions of dollars by winning these competitions, your aim is to learn something.RANKS REALLY DOESN’T MATTER. You know, in these ML competitions, 1st rank will have 1st say 0.98598 accuracy and person at 500th rank will have 0.97198 accuracies. Rank difference is very high. But the score is nearly the same.
Some Mistakes to Avoid:
- Please don’t run away from the Math, It’s ok if you didn’t get the math in the very first attempt. Try hard and come back stronger!
- Don’t unnecessarily abstract away with things. What I mean is that may be in the end you are going to work with high-end APIs that is going to simply apply your pipeline but if your process breaks down (which is almost certain will while building) you don’t know how to fix it. And this, according to the experts is one of the biggest barriers to flow in the field.
- Don’t fall into the resource dump. There’s a ton of resources out there and it’s not practical or requires to cover everything. Even all the resource that I already cited in this blog are more than enough.
- Be Persistent
- Make you daily basis goals and try hard to achieve most of it.
This is a big, big journey. Very tiring, very irritating and exceptionally time-consuming. If you can make your way through this list, by the end you should at least be familiar with the field of machine learning, and be prepared to figure out what you want to learn next.
Personal note :
AI is a vast ocean. Even some great researchers don’t know all the concepts fully. And you actually don’t need to digest all concepts. And even one knows all the concepts fully, his/her knowledge will not “full” after a week or two. Because in ML, every week something new comes.
To give an estimate, I begin my journey of learning as a complete beginner & the entire saga take me more than 1 year. This could be less in your case as you have now a proper guidance. Good luck! 💡