Which computer program language is important in future?
Answers
Answered by
1
To directly answer your question, I would go with Python, since it is not only one of the easiest programming languages to pick up, but also has some of the best tools for machine learning and AI style applications.
But I want to also emphasize that if you really do want to succeed in the field of AI (and any other field of computer science), it’s never about knowing the right language, knowing every last detail in the syntax, or keeping up with all of the “updates” in the language. Most of the successful data scientists in the field of AI and machine learning spend very little time on these details. Even if you become a master of Python, you will most likely still have no idea how to even start building a piece of software that uses machine learning or AI techniques. In my opinion, the most important skill is problem solving. The best way to set yourself up for success in the future is continually exercising these problem solving skills by actually building tangible software and diving into AI related problems. A common misconception in the AI or machine learning field is that it’s all about learning syntax and code, but to truly be able to build this software, it is important to understand the theoretical aspects of the field as well. And a lot of AI style problems require an ability to figure out the right model for the situation (for example, figuring out a good heuristic to model a search problem). These types of problems are incredibly difficult and require a lot of intuition, intuition that is only truly built from going into projects and theory. The field of AI and machine learning is also mathematically intensive, and it is also important to spend the time understanding the applications of the mathematical theory to solving these problems. Learning syntax for a programming language is not going to really help you with any of this. But by delving into the theory and applying them to your own projects, you start to build intuition and the problems solving abilities that are needed in this field.
But I want to also emphasize that if you really do want to succeed in the field of AI (and any other field of computer science), it’s never about knowing the right language, knowing every last detail in the syntax, or keeping up with all of the “updates” in the language. Most of the successful data scientists in the field of AI and machine learning spend very little time on these details. Even if you become a master of Python, you will most likely still have no idea how to even start building a piece of software that uses machine learning or AI techniques. In my opinion, the most important skill is problem solving. The best way to set yourself up for success in the future is continually exercising these problem solving skills by actually building tangible software and diving into AI related problems. A common misconception in the AI or machine learning field is that it’s all about learning syntax and code, but to truly be able to build this software, it is important to understand the theoretical aspects of the field as well. And a lot of AI style problems require an ability to figure out the right model for the situation (for example, figuring out a good heuristic to model a search problem). These types of problems are incredibly difficult and require a lot of intuition, intuition that is only truly built from going into projects and theory. The field of AI and machine learning is also mathematically intensive, and it is also important to spend the time understanding the applications of the mathematical theory to solving these problems. Learning syntax for a programming language is not going to really help you with any of this. But by delving into the theory and applying them to your own projects, you start to build intuition and the problems solving abilities that are needed in this field.
Answered by
1
Computer programmers have a full understanding of the how and why of computer systems, including system limitations, and can set realistic expectations and work around those limitations to fully maximize the use of the equipment and its accessories.
Similar questions