With the AI boom in recent years, I think the number of people interested in AI development has increased. I think that there are many questions such as whether it is possible for individuals to develop AI, and in which languages it is possible to develop AI.
Therefore, in this article, we have comprehensively opened AI development methods, development languages, and learning steps. Please take a look to the end.
Table of Contents
- What is AI in the first place?
- AI can be developed by programming
- How to choose a programming language for AI development
- Is it possible/easy to create the AI you want to develop?
- language in demand
- Is your code readable?
- Easy to learn
- does it have a future
- 5 languages that can develop AI
- Language for AI development ① Python
- Language for AI development ② R
- Language for AI development ③ C++
- Language for AI Development ④ Julia
- Language for AI development ⑤ C
- The most recommended AI development language is “Python”
- Grammar is simple and easy to read and write
- demand and future
- Extensive machine learning library
- Recommended learning methods for Python
- go to programming school
- learn from books
- Learn from online courses/sites
- 3 steps to develop AI with Python
- Step 1｜Study Python
- Step 2｜Study Data Processing
- Step 3｜Implement machine learning and deep learning
- in conclusion
What is AI in the first place?
AI is an abbreviation for “Artificial Intelligence”. The definition of AI has not been clearly defined and is still being debated.
We are surrounded by products that make full use of AI, such as Google Translate and IoT home appliances that we use every day. As AI technology continues to develop and spread, the world will become even more convenient.
AI can be developed by programming
AI is developed programmatically. It can also be developed by individuals.
Of course, it is often developed by an organization in a company, but if you have programming knowledge and a development environment, you can develop it by yourself if it is a small scale.
Despite the rising demand for AI engineers, there is a shortage of human resources. Digitalization is progressing in every industry, so you will be able to work with high treatment after you get a job.
In the following, we will introduce programming languages that can be used to develop AI!
How to choose a programming language for AI development
Before explaining the actual language, I will explain what criteria should be used when selecting a programming language for AI development. This time, I will explain it by dividing it into five points.
Is it possible/easy to create the AI you want to develop?
First, check if the AI you want to develop is easy to create in that language. Ease of creation can be judged by the number of frameworks, the number of libraries, and the difficulty of learning.
Also, by studying a language that has a booming development community, more people will ask for help when they are in trouble.
You should find it easier to find the information you’re looking for online, such as a blog on how to resolve the error.
Once you start learning a minor language, you may find that you can’t find a solution to an error when you’re developing, which can be frustrating.
language in demand
If you don’t develop in a language that’s in demand, you can’t learn a language and get a real job out of it. If you are thinking about learning and getting a job, think about when you can’t get an AI development project, and find out if it’s versatile.
Some languages are used not only for AI development but also for web application development, so I recommend learning them.
Is your code readable?
The next criterion is code readability. When people with little programming experience try to read the code, there are some parts that cannot be read unless they understand it, and it may be difficult to read depending on how the language is written.
It is easy to read if the code description method and the grammatical difficulty of programming are low.
Easy to learn
If you have little programming experience or knowledge, it is difficult to write all the source code, so it is better to choose a language that has a lot of frameworks and libraries.
If it is difficult to learn the language, it may not be possible to develop it, so a low difficulty level is preferable.
does it have a future
In the long run, you have to decide if the language you decide to learn will be needed in the future. If the development language is not used much in the future, the volume of projects will likely drop to a certain extent.
If you want to get a job through your learning, you should carefully assess the future potential of the language.
5 languages that can develop AI
The following five programming languages are recommended for AI development.
- Python
- R.
- C++
- Julia
- C.
I will explain each.
Language for AI development ① Python
Python is arguably the most popular programming language today. It is characterized by the fact that it is easy to proceed with learning, partly because the machine learning library is overwhelmingly complete.
In addition, even programming beginners can easily read the code and the grammar is simple, so it can be said that the difficulty of learning is very low.
Python has a thriving development community, so if you have a problem with an error, it will be easy to search and solve it.
Python is used not only for AI development but also for web application development, etc., and is often used when you want to develop services that combine web applications and AI.
| Degree of recommendation | Five |
| what you can do | Machine learning , AI development, data analysis, web applications |
| Difficulty to learn | higher |
| Future | can be |
Language for AI development ② R
R language is famous as a language suitable for AI development. This language is mainly used in the statistical field. It compares well with Python, but its running time is slower than Python.
Unlike Python, web application development is not possible, but programs related to machine learning and statistics can be written more easily than Python. However, because it is not versatile, it requires a high degree of expertise to handle.
| Degree of recommendation | Four |
| what you can do | AI development |
| Difficulty to learn | slightly lower |
| Future | can be |
Language for AI development ③ C++
A feature of C++ is its high processing speed. Since it is an executable file close to machine language, processing is performed considerably faster than other languages.
When dealing with large data processing such as big data and AI learning, the processing speed can have a large impact on business productivity, so it can be said that the merit of introducing it is great for large-scale projects.
The difficulty of learning the language is high, but it is good to learn because the demand is hard to decrease. In addition, when combined with Python, the range that can be incorporated into services increases.
If you also learn Python, you can acquire general-purpose skills.
| Degree of recommendation | 3 |
| what you can do | AI development, web application |
| Difficulty to learn | higher |
| Future | can be |
Language for AI Development ④ Julia
Julia is a language that is good at technical computing. It is attracting attention because it can handle advanced calculations and numerical analysis levels. Processing speed is as fast as C language.
Julia, which came out in 2012, incorporates the best parts of other languages. The Python and R languages are also simple and easy to use, so if you are already learning Python, you will be able to learn it quickly.
Julia also has frameworks and is a language for machine learning development.
| Degree of recommendation | 2 |
| what you can do | AI development |
| Difficulty to learn | lower |
| Future | can be |
Language for AI development ⑤ C
Unlike other languages, C language requires an understanding of hardware such as memory and CPU. In AI development, hardware utilization and processing speed are important, so C language has the merit of learning.
If you are a beginner in programming, it is better to learn other languages because it is difficult to learn.
| Degree of recommendation | 1 |
| what you can do | Embedded apps for AI development, web apps, and robot control |
| Difficulty to learn | higher |
| Future | can be |
The most recommended AI development language is “Python”
Next, among the languages introduced above, the most recommended language for AI development is “ Python ”. Here are three reasons why Python is recommended.
Grammar is simple and easy to read and write
Compared to other languages, Python has an easy-to-understand syntax and is easy to read and write code. You can specify indentation when writing code, making it easier to read code written by others.
Another attractive point of Python is that the code is similar regardless of who writes it, because the writing rules are well defined. Even beginners can benefit from simple grammar rules and easy-to-read code.
demand and future
Among all programming languages, Python is popular as a machine learning language, and considering the future expansion of AI demand, it has potential.
Furthermore, Python is very popular as a language that can be used not only for AI development but also for web development.
For example, the development of YouTube and Instagram is also built with Python. Also, in terms of marketing, data scientists often use Python to extract data.
Extensive machine learning library
The fact that the machine learning library is substantial means that the source code required for development is less than other languages. Python has over 10,000 libraries for machine learning.
For that reason, there will be a lot of information that is explained in detail on the net and in books, and it will be easy to solve when you are in trouble with a bug during development.
Recommended learning methods for Python
Introducing the recommended way to learn Python. We recommend experimenting with different methods, as different methods work best for different people.
go to programming school
It takes money and time to go to programming school, but there is no doubt that you will acquire skills.
For those who think it is impossible to learn programming on their own, or those who have been frustrated by self-study once, it is better to go to school and acquire it.
Another advantage is that you can ask questions if you do not understand what you are studying. You can also use the study room when you are no longer motivated to study.
Depending on the school, night courses are available, and if you want to change jobs as an engineer, there are schools that offer job change support. Decide which school or course you want to attend, depending on how far you want to learn.
learn from books
The advantage of books is that they can be learned systematically. The cost of learning is cheaper than going to school and learning. Buy online and download to read and review on your way to school or work.
For those who want to acquire knowledge even in their spare time, or for those who want to learn systematically cheaply, we recommend studying with books. However, there is no support when you get stuck, so you will continue to study according to the book.
Learn from online courses/sites
You can also learn Python from online content. For example, the following three sites are highly recommended for beginners to start learning AI development.
Progate and dot install can be used to browse (experience) basic learning content related to AI development for about 1000 yen per month.
If you want to go into more detail, you should purchase Udemy’s content. There are courses for beginners that will teach you from the basics, and there are courses for different purposes.
There are AI development courses, web development and data-related courses, so Udemy gives you the opportunity to learn about various fields. By combining these knowledge, it may be possible to develop new products.
3 steps to develop AI with Python
Let’s take a look at the process of studying AI development through Python. We will divide the actual process into three steps.
Step 1｜Study Python
Get familiar with programming first. The basis is definitely code knowledge. As I explained earlier, let’s learn using schools, books, and online courses.
Step 2｜Study Data Processing
Data processing is an important and time-consuming process in AI development.
If you are learning at Udemy or school, I think you have already set up the environment at this point, but if you do not have a development environment yet, let’s build the environment immediately.
We recommend using Jupyter Notebook. ” [For Beginners] How to use Jupyter Notebook! Explanation from installation method>> “, so please refer to this article and set it up.
Next, let’s master the conditional branching, iterative processing, and functions necessary for data processing. In addition to that, you can reduce the effort of data processing by studying some libraries.
Step 3｜Implement machine learning and deep learning
Once the data processing is complete, it’s finally time to implement the AI. Implementation of AI requires a PC development environment with a high-performance GPU, so preparation is necessary.
There are very few cases where individuals own such PCs.
Therefore, Google provides an environment called ” Google Colaboratory “. It’s free to use, so it doesn’t cost you anything.
It is necessary to study again about the implementation of deep learning , and the following three are recommended as Japanese teaching materials.
| Able Programming’s “Deep Learning for Beginners” seriesPractical Machine Learning with scikit-learn, Keras, and TensorFlow by Aurélien GéronDeep Learning from Scratch (by Yasuki Saito) |
Once you’ve finished learning Python and studied the theoretical mathematics of AI, you’ll have a better understanding.
In conclusion
In order to develop AI, it is quick and easy to start by studying programming first. By learning from books, online courses, and schools, you can build a foundation for AI development.
