Reasons Why Python is Good for AI and Machine Learning

September 23rd, 2023
10093
Python For AI and Machine Learning

AI and Machine Learning are the future. Is not it? These two technologies are taking the world by storm and that means that they are popular today but in the years to come, their use and popularity will multiply.

If you want to learn AI and machine learning, then a good Python course is essential. But do you know what makes Python a good fit for AI and ML? That is exactly what we are going to decode in this blog. In this blog, we will discuss the reasons why Python is Good for AI and Machine Learning.

How Far Spread is AI and Machine Learning?

The human brain is capable of analyzing large amounts of data. However, its capacity is nothing compared to what the AI can do. As AI holds the power to absorb unlimited amounts of data at all times without getting tired, it leads to improved business efficiency. How? By saving tons of time, money, and resources, all the while increasing productivity.

AI and Machine Learning
djangostars

But do you think any company can use AI and ML? Well, it's a two-way street. Using it for analytic processes requires developing tough technology. The product should be able to analyze huge volumes of data effectively and in a short span too. So, what really matters here is the right language.

And there is none better than Python. Let's see why Python is the best for AI and ML.

Why Is Python Best For AI And ML?

1. Extensive Selection of Frameworks and Libraries

A Python Developer can reduce the development time by taking the aid of an extensive selection of Python frameworks and libraries. Python is a rich technology stack and a wide range of libraries that can be used for AI and ML effectively.

Python IDEs offer a complete toolset for refactoring, testing, and debugging local build automation in a single interface.

  • SciPy - advanced computing
  • Seaborn & Matplotlib - data visualization
  • TensorFlow, Scikit-Learn, and Keras - ML
  • Pandas - general-purpose data analysis
  • NumPy - high-performance data analysis and scientific computing

2. High Flexibility

Python is the perfect language for ML because of its high flexibility. With flexibility, developers can pick the programming style that they are most comfortable with and even combine different styles as per the problem needs.

The four most often adopted programming styles are -

  • The imperative style
  • The object-oriented style
  • The functional style
  • The procedural style

3. Good Visualization Options

We have already established that Python has some amazing libraries and almost all of them have exclusive visualization tools. When we talk about AI, developers must highlight the visuals precisely because that is how they will get the desired attention and result.

Matplotlib is one of the helpful libraries for data scientists. It facilitates them to create plots, charts, and histograms to help with data visualization, representation, and comprehension. Thus, aiding in the creation of better reports.

4. Platform Independence

Platform independence refers to a framework or a programming language that allows the developer to implement things on one machine and then use them on other machines without any or minimal changes. Platform independence plays a key role in Python's popularity.

Python is supported by various platforms including macOS, windows, and Linux. This feature saves Python developers a lot of time and even money.

5. Great Community Support

Since Python is one of the most popular and in-demand programming languages throughout the globe, it is pretty easy for a Python developer to get the right support whenever needed. Thus, a developer might be stuck at something and they can always rely on the incredible Python community for support and aid.

Scientific Python packages like Matplotlib, SciPy, and Numpy can easily be installed in a program that is running on Python. These will help the developer to detect patterns in humongous datasets.

Even Google, Spotify, and Pixar use it for AI and ML.

You May Also Read- Top Six AI and Machine Learning Trends

How To Become A Python Developer?

If you are now wondering how to learn Python or how to become a Python developer? Since the scope of becoming a Python developer is great, don't worry we are here to help you with. Python is a versatile language and can be used in various ways. Here is how you can become a Python developer-

1. Learn Python via a Course

A Python training will help you learn everything about this language from an expert. That in itself is enough to help you gain confidence in your knowledge and skills. You will also be able to get all the latest industry changes and trends. It is most important to go with the best Python course.

2. Learn the Basics

Python is a not so complicated language with an easy syntax. Thus, it is easy to read, write, and learn. It will not take you long to get started with the basics of this language. When you set out to learn Python, doors will automatically open for you.

3. Explore Python Frameworks

As we have discussed above, Python has numerous frameworks and libraries. If you aim at becoming a good Python expert, then you must know where each of them is used and how to best utilize them.

4. Hone Essential Skills

Becoming a Python expert is not as easy as many think, in spite of being a beginner-friendly language. While it is easy to step into the language, gaining prowess is another aspect altogether.

5. Build a Portfolio

Once you master its basics, frameworks, libraries, and essential skills, all there's left to do is to begin building a portfolio. Start working on projects that will help you get noticed. You can also choose to get into an internship and then move ahead from there.

Conclusion

Artificial Intelligence and Machine Learning are changing and enhancing constantly. What you learn today might not even be valid a year later. However, it is all about beginning and once you do that, learning will become a process.

Once you step into the world of AI and ML with Python skills, nothing will remain unachievable or too difficult to learn. Using the right skills, technologies, and languages is all it takes.

Python For AI and Machine Learning FAQs (Frequently Asked Questions)

Q1. How long does it take to become a Python Developer?

Ans. Generally speaking, it takes around 2-6 months to become a Python developer. There are a lot of influencing factors here like your previous knowledge and experience.

Q2. Is Python fast enough for AI?

Ans. Python is a programming language full of features and incredible libraries and frameworks. All these come together to make Python fast enough for AI.

Q3. What level of Python is used for AI?

Ans. Advanced Python is used for AI since it involves versatile workflows and complex algorithms. Thus, only the advanced level of Python is useful.

Q4. Which Python framework works best for AI?

Ans. Tensorflow, Pytorch, Apache Spark, Scikit-Learn, and Keras are the best Python frameworks for AI.

Q5. Why is Python preferred for machine learning instead of Java?

Ans. While Java is a good programming language, Python is preferred for machine learning because it offers flexible and easy-to-use tools. It is also extensible and has a whole lot of libraries.

Course Schedule

Course NameBatch TypeDetails
Python Training
Every WeekdayView Details
Python Training
Every WeekendView Details

Drop Us a Query

Fields marked * are mandatory
×

Your Shopping Cart


Your shopping cart is empty.