A Guide to Mastering AI | Introduction
Artificial Intelligence abbreviated as AI is the stimulation of human intelligence by machines. It is in contrast to the natural intelligence shown by man. Ever since John McCarthy coined the term artificial intelligence in 1995 the use of AI and its development has grown to such a large extent that today we see the use of AI almost everywhere, from robotics to internet-based services to iOS’s personal assistant Siri.
With the development of AI growing concerns among the masses regarding robots being given such power. All that was left was the release of blockbusters such as Terminator to stem more fear among the masses against the spread of AI. But the chances of an actual Terminator taking over the world are highly unlikely. AI, on the other hand, will improve over time and grow more especially as a career field.
Why should you Study AI?
Why not? There are so many reasons why you should choose AI as your career field. Let’s discuss them so you get an idea of why AI is that perfect career option you shouldn’t ignore.
- Challenging and Exciting: AI is a challenging field no doubt, but it is equally exciting. It is constantly evolving growing better and better and no one really knows the limit. From autonomous cars to human behavior prediction and talking robots, the ways this field is growing is unpredictable.
- High Industry Demand: Yes, the demand for data scientists and AI specialists in the market is actually really high. It not only has more job options for you but has increasing value too.
- High Pay: For those of you, who fear that you won’t be well paid, fear not. This job is equally rewarding as demanding. It is, in fact, one of the most well-paid jobs today.
Still not convinced? Read on and maybe you might be inclined towards changing your mind. Now we will discuss how you can become an AI professional.
Level 0: Setting up your Ground:
Not intimidated by math? Love to code? Well, then this field is perfect for you. The important thing is to be clear about your base. Of course, you can always practice and polish up your skills. So don’t give up on AI if you think your coding is mediocre or your math skills need improvement.
Level 1: Covering the Basics:
At this stage, you will first have to strengthen your roots and by those we mean base. There are a lot of concepts that are all integral parts of this field so you should get a profound idea regarding most of them.
- Covering Linear Algebra, Statistics, and Probability: Math has to be the most basic thing you need to cover first. First, you should cover the individual concepts such as vectors, matrices and then work up the ladder to dimensionality, statistics, and statistical tests. Next move on to concepts of probability such as the Bayes Theorem. Math is a very crucial part of AI so if you aren’t good, you can become better. As mentioned before, this is no easy task and requires practice.
- Understanding Data Structures: Next you should improve the way you solve problems involving data, make your analysis of data more accurate so you can design your own systems with minimum errors. Learn the various parts of programming languages that will help you understand data structures such as Stacks, linked lists, dictionaries etc.
- Understanding Regression: Yes, regression is important. You should learn about regression in detail and have a strong grasp of its concept before you move on. It will help you make predictions in real-life applications and understand the basics of machine learning.
- Understanding different Machine Learning Models and their Working: The next step is getting to know legacy machine learning algorithms such as SVM, KNN, Random Forests, Decision trees etc. Try to implement them in solving problems by fully understanding algorithms. This is not easy so you will have to work hard to perfect your skills. Key is to be clear and logical.
- Understanding Machine Learning Problems and Solutions: The next step is understanding how a case uses machine learning algorithms and how that algorithm can be implemented in different cases, where it is suitable to its function and so on. There are 3 basic steps you need to perfect yourself in, Supervised Learning, Unsupervised Learning, and Reinforcement Learning before you move into level 2.
Level 2: Deep Learning involving AI
Next comes the complex part of AI where you start learning more in-depth concepts.
- Learning about Neural Networks: A neural network is basically a computer system modeled on the human brain and nervous system. It works by incorporating data via an algorithm it is built on. These are the basics of how AI machines function so having a clear understanding of them is important
- Understanding the Math behind Neural Networks: Neural networks are built in layers. Each layer has ‘nodes’ which are interconnected and each node has an ‘activation function’. The ‘input layer’ presents patterns to the network and the inner layers do the processing with the help of ‘connections’. The inner hidden layers then give an output to the ‘output layer’. You will have to study the math behind this whole operation and processing. Some basic keywords you will learn about include weights, activation functions, loss reduction, backpropagation, gradient descent approach etc.
- Mastering different neural networks: Now you should learn about the different types of neural networks and their use in different cases. The basic math functions are the same but the implementation may be different and there may be a few modifications. Multilayer perceptrons, Recurrent Neural Nets, Convolutional Neural Nets, LSTMS etc. are a few types of neural networks.
- Learning about the domains of AI: Now you are ready to learn about the applications of these neural networks and to build your own applications. Each application can be different and may require different approaches and sometimes you cannot master all the fields in AI at once so take it to step by step. First, opt for one specific field and then move on to other domains.
- Getting to know Big Data: This step is not mandatory but it is a great part of AI, therefore, it is suggested that you get a basic idea of Big Data as it will help you in this field.
Level 3: Mastering AI
The last level involves more application of what you have learned so far. This is the final stage for mastering AI.
- Mastering Optimization of Algorithms: Optimization of algorithms basically helps to minimize or maximize an objective function (Error function). These functions depend on the Models internal learnable parameters which play a role in the efficiency and accuracy of results. That is why you need to learn to apply optimization strategies and algorithms to model’s parameters to obtain accuracy and optimum values of such parameters.
- Putting your Brain to the test: The next step is to put yourself out there by taking part in competitions. Take part in data science competitions and hackathons to increase your knowledge in the practical field and implement your knowledge.
- Publishing and Reading Research: Next you need to take it a step further and go into research. Start reading research papers on AI and learn to become an innovator. Try to start your own research and understanding to cases which are still developing. Testing is also crucial.
- Roll out your own Algorithm: After you do research, the next stage is starting to make your own algorithms to solve such cases. Try to work around the math and see how it can integrate into AI in all possible ways. You never know, you just might bring the next revolution.
Coming to the end, you might be thinking it is too complicated. We won’t lie; it is complicated and takes time to master. However, that does not make it impossible. All it requires is hard work and practice, be consistent in your work and soon you will master AI.