The roadmap is for beginners and advanced developers alike since learning in AI never stops.

Artificial Intelligence or AI is one of the most hyped and broad fields in the IT industry in the 21st century. Long back when watching Terminator or an Iron Man movie, who would have thought that it could become a possibility one day. AI is making its mark, literally everywhere. Be it your social media, video streaming apps, e-commerce, software, smartphone cameras, and more.

      AI is also contributing actively to the detection of diseases, helping in making medicines stronger and in predicting stock market trends. All you have to do is type in a few algorithms in your favorite programming Language, and you are out there with a whole new software solving problems. If you want to know more than just basics about this industry, then we
have created this roadmap for you.

Learning curve 
You might be all geared up to take the journey to the world of AI and build your first AI model, but you cannot just leave out the preparation. Building AI models is nothing less than an adventure, but to enjoy the adventure, you will need proper tools and background. Therefore, to start winning gold, you must begin running the winding tracks of learning. This is one of the very first steps that require your attention, patience, and determination. Once you arc through with this phase, there is no turning back. Hop on, tighten your seat belts, and let the journey begin. 

Know your statistics 
AI is used in a vast number of applications these days. Your high-school statistics, mathematics, and a lot of your "irrelevant" education from back then, will play a significant role. Probability, classification, clustering, variance, trigonometry up to a certain extent (if you want the machine to move) are a must. You cannot leave out anything from your basic Mathematics and Statistics course, because if you do, you might be in for trouble. Therefore, it is better if you could brush up some concepts before getting your hands dirty.

Start loving languages 
No, not your mother tongue, programming languages The system requires instructions, and you need to convert the instructions the way you want your system to understand them. You have a long list of programming languages that you can use to build your first AI. We will discuss some of the popular ones below:

Python 
As with most applications, Python leads the pack with AI too. You get unparalleled support, fantastic community, and so many APIs and support libraries to help you achieve the heights. You name the job and Python has a library ready to save you the number of lines of code required to build your first AI model. Again, learning and understanding every aspect of it is very crucial. Once you start using it in and out every day, you will know the difference. We will talk about some of the best AI courses 
available out there in  a while. Python has NLTK for natural language processing. PyTorch, MXNet, Apache, and Tensorflow to catch up with Deep Learning too. You get Numpy, Pandas, SciPy that will make your AI development journey smoother and enjoyable.  
  
Java 
Java is one of the best object-oriented programming languages and still survives in the 21st century for most applications you name. It was the first one of its kind programming language when it came out in 1995 and keeps improving even today. Java has been a much more portable, maintainable, and
transparent language that is supported by a massive set of libraries. NLP is the king of the internet right now, and Java has a vast support library and vibrant community to support this fantastic platform. Being associated with Apache Spark and Hadoop, you get to see a lot more of it with data analytics-related A1 development.

R
Yes, another programming language that is coming out at the top in a  short period is R. It has a massive set of libraries, and the language itself is straightforward to learn. You get many support libraries that aim at making your job as an A1/ ML developer better. It is also known as a statistical workhorse by the developers who dedicatedly use R for their daily A1 model development. Once you go deep into the subject, you will  realize that statistics is the base of building an accurate or near-accurate AI model. R offers you extreme case and support with its library to get the job done.

Study Machine Learning (ML) 
To understand AI better and build better models, you need to know the difference between ML and AI. Most people either confuse the two words or use them interchangeably, which is a big blunder. You should understand that machine learning allows computers to improve on their own, while AI s a bigger game. AI is trying  to machines make decisions which only humans could make based on their logic and unprecedented brainpower. So basically, machine learning is used to achieve belter AI models and not vice-versa. Artificial intelligence helps in decision-making, while ML allows your system to learn new things via different learning experience. Supervised learning and unsupervised learning arc the two essential and inseparable terms in the world or ML. Supervised learning means providing the algorithm with  expected solutions and letting it learn as the progress of the computation concerning the key. Unsupervised learning, on the other hand, finds a pattern and extracts features, ties to  make sense the solution and focuses  continuously until  get the right results.

Participate in extra-curriculars 
You cannot focus on this any less than your academics and programming languages. We all know that we need extra motivation to learn what we learn. There is nothing different from building AI models. Join a fantastic AI ML community to understand what is new in the world of ML. Try and attend meetups, meet and network with the experts in the industry; they will help you understand the subject better. Here are a few meetups and communities you can attend or join, respectively to learn more.

Data Quest 
 Data quest is an online data science community. It is one of the biggest online Slack communities for data science and AI professionals. In this group, professionals share resources, tutorials and find other professionals to collaborate on similar interests and projects. 

Global AI community 
As the name suggests, this community is aimed at connecting various AI communities with each other. This enables communities to network, collaborate, and understand various aspects of the fields from individuals all  over the world.

AIMinds 
Alminds is an initiative by Analytics India Magazine which is a monthly meetup aimed at promoting Artificial Intelligence development in India. They invite some of the best minds and well-known AI practitioners and researchers to exchange ideas and share knowledge. These professionals help budding and aspiring professionals to make it big in the AI field. 
            These communities and meetups will help you to network with many professionals. These meetups will also help you keep up with the major and recent developments in the field of A1 from
various professionals actually working towards making them possible. 
          Find, read, research as many resources as you can to make sure that you are never on the backfoot in the world of AI. All work and no play make Jack a dull boy. Well, that goes just perfect for an aspiring AI developer. Meet people in the same field, try and participate in online quizzes and communities, follow the field carefully, because A1 is so much volatile and fast-progressing that it does not even take a week for something to go obsolete within weeks. 
                       Here are a few websites and books you can refer for additional resources:
AI trends
AI trend is one of the blogs websites you should keep a tab on if you want to stay updated with latest applications, concepts, and advancements in the field of AI.

Machine Learning mastery 
If you are looking for a resource worth following all the time, you can rely on Machine Learning Mastery. This website is owned by Jason Brownlee, who has a PhD in A1. He focuses on explaining ML algorithms with small projects and their application in the real world. This blog is aimed at absolute amateurs and it is quite good at what it does. 
 
Kaggle 
Being an AI or ML developer or enthusiast, you might be acquainted with Kaggle. Kaggle is the largest data science community focusing on AI and ML problems. It has a variety of projects and competitions for all ML and AI enthusiasts. These competitions and little projects will also help you build a good portfolio of projects over time. You will find the links to each of these resources explained and some 
 additional ones at the end in the Additional Learning Resources section.

Understand algorithms 
Algorithms are an essential aspect of programming. If you are into AI model development, you are undoubtedly aware of the various programming concepts like algorithms. Having a right prediction or learning algorithm handy is always a great start to building your AI, but more than that, understanding whether that algorithm is perfect for your problem is the greater deed that you need to perform. You cannot just pick an algorithm and go ahead with the problem. Therefore, make sure you understand the algorithms and perform the operations as you want and if you want it. 
          Now, that you know the basics of starting with the AI model building, you should move towards understanding AI at a deeper level so that you can build some fantastic models. Remember that you must first master the learning curve and then move towards a Algorithmic and programming understanding of AI and its syntaxes. 
           The reason being, if you do not know what clustering and classification mean, you will not know when to use them. If you do not know when to use probability and why should you use variance, you cannot understand their application. Therefore, win the curve and reach the next level of the race with following AI courses that you can take over the internet from the comfort of your home.


E-Learning courses 
Online education is becoming a norm over the last few years. One good thing about those courses is that you don't have to compromise with quality of education; in fact, online platforms try to get experienced faculties and industry experts. We have a little list of our own compiled for you. 
Take a look:

1. PG diploma in Machine Learning and AI - UpGrad 
 UpGrad is a popular name in the whole online education scenario, especially over the last few years when the demand for it boomed. The PG diploma course by UpGrad is aimed at working professionals and aspiring ML developers, Data Scientists, AI architects, and so on. You also get to learn AI concepts like deep learning, model deployment, NLP, Reinforcement learning and other AI concepts in detail. You get to do this using Python, Tensorflow, MySQL and so on. At the end of the course, you get a PG certificate by IIIT-B which is one of the esteemed institutes of India. 

 2. AI for Everyone by Andrew Ng - Coursera 
 Andrew Ng is the co-rounder of Coursera and an adjunct professional Stanford University. He is also known for leading Google Brain, an initiative by Google to develop massive-scale deep learning algorithms. Coming back to the course, this ono focuses on every thing AI. It has explanations 
about AI concepts, basic terminologies, how it works in the real world, and everything you as a software engineer or an outsider should know about at the ground level.

3. Artificial Intelligence A-Z: Learn how to build an AI - Udemy 
Udemy is a vast marketplace, and we all know that it has some fantastic courses lined up for everyone from learning basic Python to understanding digital marketing. It is also known for its A-Z series which focuses on teaching you everything you need to learn about a subject. It teaches concepts based on a real-life problem like self-driving cars. This course is aimed at people who appreciate project-based learning and learning concepts concerning the application.

4. Google AI education 
Google is known for its initiatives to shape the technology world. It has a set of tools under its brand wagon, and we all know most of them. Google is also conducting instructor-led training for free for people who might not be able to afford courses but have the capability of capturing information in its raw form. Google AI education is also a part of this program where the expert instructors help you understand AI coding basics. This course is aimed at beginners who might have little to no information about the basics of AI coding, but hungry to learn new concepts and take their career to new heights.

Additional learning 
You can check out the following resources to understand more about Artificial Intelligence and Machine Learning. These resources will help you enhance your knowledge and help you stay in trend with the latest AI developments. 

Blogs 
Books  
1.Rebooting AI: Building Artificial Intelligence we can trust by Gary Marcus and Ernest Davis 
2.AI for People and Business by Alex Castrounis 
3. The Hundred-Page Machine Learning Book by Andriy Burkov 
4. Artificial Intelligence: Everyone Needs to Know by Jerry Kaplan 
5.Artificial Intelligence by Example by Denis Rothman