What is the best way to learn Artificial Intelligence for beginners? [Part 2]
The very human learning chronology for Artificial Intelligence
How can you get started? From Absolute Beginner :
Great you made comfortably to Part-2, Here the real learning journey begins but in case if you missed Part 1 do check it.
Before you get to start learning artificial intelligence make sure to learn the thing in small step don’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.
Here are some good resources for Python:
2. Basics Maths Knowledge about Algebra, Calculus, Probability & Statistics
This is a must requirement if you want to know what is really working behind scenes. Having some basic knowledge about it would be good Since we can take 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 algorithms using algebra, calculus concepts.
- Whichever algorithm you learn or work mathematics is the basic functional unit so focus on learning concepts rather than running away or just ignoring.
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
libraries which will be useful while writing Machine Learning algorithms.
First actual step in learning artificial intelligence.
4. Andrew-Ng Course :
This is an Excellent and Highly Recommended Free Course by Andrew Ng at coursera, this course is a very good starting point for you to have a basic understanding of algorithms and the different concepts to Machine Learning.
Highly Recommended: I don’t think so anyone in today's AI|ML|DS diaspora not aware of this course
Now you know a bit about how the actual things are happening in artificial intelligence.
5. Some of the best data science blogs to follow:
- Data Science Central
- KD Nuggets
- Towards data science
- Simply Statistics
- insideBIGDATA
- Insaid Blog
- Analytics Vidhya
6. 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 Edition2by 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
7. Practice time:
You should also take part in various Programming Competitions happening over 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. 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 a rank among the top 10. But still, I am working on them. Because to achieve rank you need to invest a lot of time in which I lag. My prominent aim to participate in this 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 holder let’s say have 0.98598 accuracy score and person at 500th rank will have 0.98198 accuracy score. The rank difference is very high. But the score is nearly the same.
- Kaggle(My favorite one)
- Coding Game
- HackerEarth
- Robocup
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 are 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 resources that I already cited in this blog are more than enough.
- Be Persistent
- Make your daily basis goals and try hard to achieve most of them.
“Wow, A Great journey so far. Hurray, you are halfway down the line”.
This is a big-bang 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.
To learn new things I do follow a simple principle :
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|AI every week something new comes up in the picture.
To give an estimate, I begin my journey of learning as a complete beginner & the entire saga took more than 1.5 years. This could be less in your case, as now you have proper guidance.
For any considerable question ping me on @vikramvinay1 I love to answer all queries related to AI.
Final Thoughts and Closing Comments
There are some vital points many people fail to understand while they pursue their Data Science or AI journey. If you are one of them and looking for a way to counterbalance these cons, check out the certification programs provided by INSAID on their website. If you liked this story, I recommend you to go with the Global Certificate in Data Science & AI because this one will cover your foundations, machine learning algorithms, and deep neural networks (basic to advance).
If you like this blog and it helps you anyway then hit the follow & clap button. Your appreciation really inspired me to write more.
If you have any query or suggestion that you want me to include in this blog please show me your response in the comment section so that I can get to know about your thought process and if you feel something is missing let me know that too, so we can together improve this more and make it more feasible for every beginner.
