Artificial intelligence is an interesting and rapidly expanding area. Although we are still a long way from having the robot servants depicted in science fiction films, artificial intelligence (AI) is already a part of our daily life. While certain AI applications, such as self-driving cars, are still in the early phases of research, others, such as predictive analysis, are currently available.
Because AI is a broad topic with applications across various sectors, AI-related occupations are in great demand. According to a McKinsey report, AI is rapidly being employed for service improvement, product development, risk modelling, and fraud detection. The need for computer and information research employment is predicted to increase by 22% between now and 2030.
One of the most difficult aspects of studying AI is determining where to begin. It is a wide field with several components. Many AI concepts rely on high arithmetic and formal logic, which might be a barrier to entry into the sector. We’ve broken down the field of AI into a simple step-by-step approach to mastery to assist you overcome these obstacles.
Steps to Learn AI:
Step 1: Understand the Prerequisites:
Fundamentals of Computer Science:
- Theory and algorithms like Boolean algebra, binary mathematics, and computing theory
- Computer hardware systems, which include computer physical components, digital logic, computer architecture, and network architecture
- Programming languages, compilers, computer graphics, and operating systems are examples of software systems and elements.
Probability and Statistics:
Some subjects in probability and statistics for AI should be covered:
- Numerical and graphical description of data
- Elements of probability
- Sampling distributions
- Probability distribution functions
- Estimation of population parameters
- Hypothesis tests
Learn the basic mathematics required for AI such as Linear Algebra, Calculus, Regression, etc.
After that start learning Programming languages such as Python, Java, R, Prolog, SQL, etc.
AI requires data input in order to learn. Data structures are various techniques of structuring data so that it may be used efficiently. If you want to work in artificial intelligence, you’ll need to know how to use and apply the best data structure for your application. The following are some of the most prevalent types of data structures:
- Linked list
- Binary tree
- Binary search tree
Step 2: Ace AI Theory:
The goal of AI is to solve a problem using a variety of approaches such as algorithms and heuristics. An agent and its surroundings comprise an AI system. An agent is the programme that makes choices in AI. A problem-solving agent in AI is intent on attaining its objective. Once the aim is defined, a problem-solving strategy is developed through problem formulation. This includes various elements, including:
- The agent’s initial state
- Actions that the agent could take
- Each activity is described by a transaction model.
- A goal test to see if the goal has been met.
- Each action path’s cost
The process of drawing conclusions or generating predictions based on prior information is known as reasoning. Because robots cannot think, they must be designed to perform this type of reasoning using algorithms. When programming AI to make decisions, you must educate it how to finish a task using one of various reasoning approaches. Those methods include Deductive reasoning, Inductive Reasoning, Common-sense reasoning, Abductive reasoning, Monotonic reasoning, etc.
Natural Language Understanding:
Natural language comprehension is a subset of natural language processing in which human speech is understood via programming. It enables computers to understand human speech in the absence of explicit grammar in computer languages. Natural language understanding also enables computers to converse with people in their native language.
The process of teaching computers to see and comprehend visual information is known as computer vision. It enables computers to extract data from photos, movies, and other forms of visual input. The information can then be used by the software to take action or make recommendations. Computers can process visual data far quicker than humans, processing hundreds of photos every minute.
Computer vision, like other AI training methods, requires enormous data sets to detect minor variations and recognise specific pictures. Machine learning algorithms allow the computer to train itself about visual data.
And, thus, slowly and gradually you can learn machine learning with a very strong base and could start working on AI projects to get expert in the field of AI.
How to learn Robotics?
Step 1: Learn the basics:
Learn the fundamentals of coding so that you can programme your robots. Coding is a fundamental component of robotics, and you will need to understand variables, conditional statements, functions, and other fundamental coding abilities. To increase your coding abilities, choose a coding language and study it on your own or attend coding lessons.
Learn linear algebra to get ready to create robots. Basic algebra is required for both programming and fine-tuning a robot’s code. Take a lot of algebra classes in high school or college, or study algebra on your own to improve your math skills.
Learn physics as a foundation for studying robotics. Understanding how your robot moves and interacts with the outside environment is required while building robotics. If you have never taken a physics course, enroll in an online programme or teach yourself fundamental physics.
Study computer science to improve your robotics abilities. Robotics is inextricably linked to computer science, and a solid grasp of the latter is required to study the former. Practice or learn basic computer skills, and attempt to stay current on computer science papers or websites.
Step 2: Build your Robotics Skills:
To create your first robot, you can use a kit or do it yourself. Once you’ve mastered the fundamentals of robotics, get a robot construction kit and follow the instructions to construct your robot using code, maths, and manual tinkering. Alternatively, you may make your own using materials and equipment available at most hardware stores.
Take robotics classes in person or online to hone your abilities. While self-studying robotics is beneficial, robotics classes may teach you advanced methods.
Join a robotics group to meet new people and get guidance on your hobby. Robotics is more than simply a hobby—if you enjoy making robots with other people, it may be a great way to form long-lasting connections.
When you’re ready to show off your work, enter robotics competitions. Enroll your innovations in a local or big robotics competition once you’ve learnt how to programme simple robots. Whatever your results are, robotics tournaments may be a terrific method to test your robots and learn how to improve them.
Step 3: Study Advanced Robotics:
For advanced robotics, learn trigonometry and statistics. While linear algebra is sufficient for rudimentary robots, higher mathematics may be required to develop complicated ones.
To modify your projects, learn about the Robot Operating System. The most prevalent software system for constructing robots is the Robot Operating System (ROS). If you want to create complicated robots without using a kit or a tutorial, understanding how to utilize ROS can assist you in programming your creations.
If desired, get a degree in robotics engineering. A degree in robotics engineering may educate you how to design, diagnose, and repair complicated robots if you’re passionate about robotics.
Step 4: Big Projects
Work on the big Robotics Projects and thus, you are an expert in the field of Robotics.