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Many programming languages facilitate machine learning, a few prominent ones being Java, Python, R and C++. Python leads the field in its ease of use and the simplicity of programming code. Here are some very compelling reasons for why one should use Python for machine learning.
Python — an open source programming language for machine learning
Python is simply the Swiss army knife of Machine Learning. It is one of the open source programming languages widely used to perform complex operations and has a full set of tools for increasing the productivity of Machine Learning.
Python is popular among its users because it is easy to learn and simple when it comes to programming. It is considered to be one of the most consistent math-like programming languages. Python has its own set of built-in functions and utilities, which help it perform a plethora of complex operations with just a few lines of code. It is also way ahead in terms of its easy syntactical character when compared to other programming languages.
Python has a large set of libraries that can be easily used for machine learning, such as SciPy, NumPy, ScikitLearn, PyBrain, etc. Simplicity and wide applicability make Python a popular ML language.
At times, the code developed in Python almost seems to be written in the English language. The way Python mirrors human language or its mathematical counterparts makes ML a bit easier.. Python has outperformed R in the fields of data science and ML for the last five years.
Python’s capabilities are given a big boost by the different available packages in ML and data analytics. Pandas, one of the best known data analysis packages, gives Python high-performance structures and several data analysis tools as well. Python excels in offering a playground for playing with data, not just numerically, but also for the following other functions:
- Downloading varied contents from websites and APIs
- Interfacing with different databases and spreadsheets
- Manipulating audio, text and images
- Availability of sophisticated tools for the exploration and presentation of results, like Pandas, Jupyter, etc.
Why is Python suitable for machine learning?
Here are some important reasons why Python is considered ideal for ML.
1. Python is an easy to learn and user-friendly programming language that is pretty simple to start with. Its syntax is simple and the code can be written in fewer lines as compared to other programming languages.
2. It has numerous built-in packages for ML and other computations — for example, Numpy, Keras, Pandas, etc. All these packages are well-documented, and hence are quite helpful in starting with any project or solution. It accelerates the process of fixing bugs.
3. All the available libraries of Python are quite powerful. They include many features that are helpful in performing complex computations. These help in fast, efficient and stable development. Python also uses a wide range of computation speed improvements continuously to improve the performance of libraries.
4. Python has got considerable support from the community, so developers can easily find a large number of tutorials and valuable tips during the development process. This makes it easier to use any new technology from scratch.
5. Python is like the new FORTRAN of the scientific world. It’s very popular in the non-computer scientist’s world, equipping users with an enormously large toolbox for different kinds of applied programming problems.
6. Python makes it very easy to quickly implement or experiment with any new ideas and prototypes. Hence, different scientific and research communities love to use it. That is why it is being widely used in ML and data science.
Different open source scientific Python libraries used for machine learning
There are many open source libraries used to implement ML for different applications. They are widely referred to as scientific Python libraries since they are put to use while performing the elementary machine learning tasks.
- Numpy: This is a Python library that is widely used for N-dimensional array objects.
- Pandas: This library file is used for Python data analysis, including different structures such as data frames.
- Matplotlib: This is a 2D plotting library that produces publication-quality figures.
- Scikit-learn: This library contains a couple of the ML learning algorithms that are used for data mining and data analysis tasks.
Seaborn: Sometimes it is difficult to get accurate plots with the help of Matplotlib as it focuses on line plots. In such cases, one can go with a more specific library, called Seaborn. It focuses on the visual aspects of different statistical models including heat maps, and also depicts the overall distribution of the data.
Machine learning (ML) is a type of programming that empowers computers to automatically learn from data presented to them and improve from experience without being programmed again and again. Machine learning isn’t just used in the IT business. Machine learning nowadays plays an important role in promotion, banking, transport, and various domains. This innovation is constantly advancing, and subsequently, it is methodically obtaining new fields in which it is an essential part. Python is a high-level programming language. Besides being an open-source programming language, python is an especially described, object-oriented, and interactive programming language.
Python is easy to understand.
To iterate, Machine Learning is just recognizing patterns in your data to be capable of making changes and smart judgments on its own.
Its readability, non-complexity, and ability to fast prototyping make it a popular language between developers and programmers all over the world.
Different Libraries and Frameworks
Python is already very well-known and therefore, it has many various libraries and frameworks that can be used by engineers. Those libraries and frameworks are actually important in saving time which gives Python significantly more well-known.
Since machine learning involves an actual bunch of math, which is very difficult, the readability of the code (also outside libraries) is important if we need to be perfect. Programmers should think not about how to write, but very important what to write, all things considered.
Python developers are passionate about making code that is not hard to read. Moreover, this particular language is very strict about proper spaces. Another benefit of Python is its multi-paradigm nature, which enables engineers to be more flexible and approach problems using the simplest way possible.
Python allows easy and powerful implementation.
For most of programming languages, it requires coding beginners or learners to familiarize themselves with the language first before moving to use it for ML or AI.
This is not the problem with Python. Likewise, if you only have a basic understanding of the Python language, you can then use it for Machine Learning because of the large number of libraries, support, and tools available for you.
Additionally, you will consume less time composing code and debugging errors on Python than on ruby, java, or C++.
Portable and Extensible
This is an important reason why Python is so mainstream in Machine Learning. So many cross-language jobs can be accomplished efficiently on Python due to its portable and extensible environment. Numerous data scientists favor utilizing Graphics Processing Units (GPUs) for training their ML models on their machines and the versatile idea of Python is appropriate for this.
Python provides broad support. Because a group of people, both programmers and ordinary users, view Python as a standard, its support community is very large, developing Python’s reputation even more.
Machine learning is making a PC perform a task without expressly programming it. In this day and time, each framework does well as a machine learning algorithm at its core. Machine learning is at present apparently the hottest topic in the business and organizations have been running to have it consolidated into their products, particularly applications.