Python for Machine Learning: Benefits & Challenges
Hey! I am a Software Consultant with 12 yrs exp. and blogging is my passion.
Initially released in 1991, Python is a general-purpose programming language that was designed with a philosophy of optimizing the code readability. It is often referred to as a "batteries included" language because of its comprehensive standard library.
Python being used for Machine Learning is a hot topic that has been doing the rounds all over the industry. According to web design stats
What’s so special about Python?
Many people don’t know this but Python was conceived as a successor to the ABC language. Guido van Rossum, the guy who designed Python, really liked a few features of the ABC language but also had a fair share of grievances with the language, the biggest issues being lack of extensibility, which gave birth to Python.
Let’s dive deep into the specifics of why people love Python:-
Free and open-source
Even though several of them are, in fact, free and open-source, it’s still one of the features of Python that makes it stand out as a programming language. You can download Python for free, which means that Python developers are able to download its source code, make modifications to it and even distribute it. Python comes with an extensive collection of libraries that support you to carry out your tasks.
Awesome Collection of Inbuilt Libraries
Python offers a vast number of in-built libraries that the Python development companies can use for data manipulation, data mining, and machine learning, such as:-
- NumPy - Used for scientific calculation.
- Scikit-learn - For data mining and analysis which optimizes Python’s machine learning usability.
- Panda - offers developers with high-performance structures and data analysis tools that help them reduce the project implementation time.
- SciPy - Used for advanced computation.
- Pybrain - Used for machine learning.
Moderate Learning Curve
Many people claim that Python is really simple to understand, and given the functionality and scalability it offers, Python as a programming language is easy to learn and use. It focuses on code readability and is a versatile and well-structured language. How hard Python is, depends on you. For instance, if a newbie is provided with good study material and a decent teacher, Python can easily be understood. Even decent Python developers can teach Python to a newbie.
General-purpose programming language
What it means is that Python can be used to build just about anything. It is extremely useful for backend Web Development, Artificial Intelligence, Scientific Computing, and Data Analysis. Python is primarily be used for web development, system operations, Server & Administrative tools, scientific modeling and can also be used by several developers to build productivity tools, desktop apps, and games.
Easy to integrate
Python is being used as an integration language in many places, to stick the existing components together. Python is easy to integrate with other lower-level languages such as C, C++, or Java. Similarly, it is easy to incorporate a Python based-stack with data scientist’s work, which allows it to bring efficiency into production.
Easy to create prototypes
As we already know that Python is simple to learn and has the ability to develop websites quickly. Python requires less coding, which means that you are able to create prototypes and test your concepts quickly and easily in Python as compared to several other programming languages. Developing prototypes saves developer's time and decreases your company’s overall expenditure as well.
Understanding Machine Learning
A new research report
suggests that the market size of Machine Learning is expected to grow from $1.41 Billion in 2017 to $ 8.81 Billion by 2022, at a Compound Annual Growth Rate (CAGR) of 44.1%. What it means for business owners is to tap right into the opportunity while it’s still in its infancy. But, in order to invest in it, you need to understand it well. Since it’s a very detailed subject and I’m not allowed to assume that you know everything about Machine Learning, I’m going to dive deep into it, from scratch.
Machine learning is simply a subset of Artificial Intelligence (AI) that provides systems with the ability to automatically learn without human supervision or assistance and evolve their actions accordingly. Based on experiences and observations, it analyzes certain patterns within a given data set, draws conclusions and acts accordingly, without having to be explicitly programmed.
Let’s talk about the basic types of Machine Learning methods:-
Supervised Machine Learning
In supervised machine learning, we teach the model by training it with some labeled dataset. It’s important to note that the data is labeled. What sets it apart is the fact that we already have prior knowledge of what the output values for our samples should be. Note that the output is determined entirely from the training data, so as long as the data labels are correct, so will the output. If they’re incorrect, it will definitely reduce the effectiveness of your model.
Unsupervised Machine Learning
This model does exactly what it sounds like. Here we let Machine Learning algorithms to draw conclusions from Unlabeled data. It has more difficult algorithms than supervised learning as we don’t have almost zero information about the data set that it uses, and hence, we also don’t have any information about the outcome. Because of this, we also have fewer tests and models that can be used so as to test the accuracy of the data predicted by them.
How Good is Python for Machine Learning?
At this point, you’re already aware of how big corporations rely on AI and Machine Learning for numerous operations, which also calls for a huge demand for experts in these technologies. According to Jean Francois Puget
, from IBM’s Machine Learning Department, Python is the most popular language for Machine Learning, based on trending search results on indeed.com.
Based on several parameters, we bring you a comprehensive list of factors that make Python the most preferred programming language for Machine Learning:-
An Amazing Collection of Libraries
A great library ecosystem is one of the primary reasons why Python is preferred for Machine Learning. Machine Learning requires continuous data processing, and in order to make that effective, Python’s libraries can let you access, handle and transform data. Let’s look at these libraries:-
- Scikit-learn - Used for handling basic Machine Learning algorithms like clustering, regression, linear and logistic regressions, classification, etc.
- Pandas - Used for high-level data structures and analysis.
- TensorFlow - It works with Deep Learning by setting up, training, and employing artificial neural networks with large datasets.
- Keras - Used for deep learning. It allows fast calculations and prototyping, as it also utilizes the GPU apart from the CPU of the computer.
- Matplotlib - Used for creating 2D plots, histograms, charts, etc.
- Scikit-image - It handles image processing.
- NLTK - Works with language recognition, computational linguistics, and processing.
- PyBrain - Used for neural networks, unsupervised and reinforcement learning.
- Caffe - Used for deep learning and allows switching between CPU and GPU and processes 60+ million images a day using just the NVIDIA K40 GPU.
- StatsModels - It performs statistical algorithms and data exploration.
A Lower Barrier to Entry
Just like blockchain development trends
, learning Python is often termed as learning the English language. If you speak english already, well, good for you. All you need to do is install Python and start using it for Machine Learning development as it wouldn’t take much effort to learn the language. The syntax of the language is simple and it allows you to work with complex systems very conveniently, ensuring clear relations between the system elements.
Even though, saying that it has a simpler vocabulary would be the most apt description, it is a high-level language nonetheless. It does almost everything, and you are not stuck in the minutiae like you would with C++ or others near the machine code language.
Python is a great choice for Machine Learning as it offers flexibility, that further enables the developers to choose the programming styles that they find easier to use. They can even combine these programming styles to solve various types of problems in the most productive way. Let’s have a look at these styles:-
- Imperative style - You can define the sequence of computations that happen like a change in the program state.
- Functional style - It declares what operations should be performed without considering the program state. It declares statements in the form of mathematical equations.
- Object-oriented style - It is based on two parameters, namely, class and object. Similar objects form classes. But since this style can’t fully perform encapsulation, it is not fully supported by Python. However, developers can still use it to a degree.
- Procedural style - This is most common among beginners since it performs tasks in a step-by-step format. It’s used for sequencing, modularization, iteration, and selection.
Apart from being flexible and easy to use, it’s very well known for being versatile. What I mean is that any Python program written on a Windows PC can be run on any platform including MacOS, Linux, Unix, and 21 other platforms and vice versa. Here’s the catch though - In order to transfer the program from one platform to another, the developers need to perform several micro-level changes and modify some codes so as to create an executable form of code for the chosen platform. Packages like PyInstaller can be really useful when it comes to preparing the code for running on different platforms. This doesn’t just save time and money for tests on various platforms but also makes the overall process more concise.
Good visualization options
For AI and Machine Learning developers, it’s important to realize that the data interpreted by these technologies is beyond human comprehension, unless it is represented in an organized manner, in a human-readable format. Python offers a variety of libraries, several of which are great visualization tools. Matplotlib, seaborn, ggplot are some of the many popular visualization tools that allow data scientists to build charts, histograms, and plots for better understanding of data. If you scroll through the Python Package Index, you'll be able to find libraries for all kinds of data visualization needs.
Isn’t it really helpful when you have the support of the community striving to progress in the same direction? Not many programming languages offer that privilege but there’s a strong community support built around Python. It is an open-source language and is absolutely free along with a variety of useful libraries and tools.
A lot of documentation is available online when it comes to Youtube videos and other informational content but specifically in Python, there are community forums where Python developers and Machine Learning developers discuss problems and try to eliminate them and help each other out.
TIOBE, a popular software quality assessment company, have a community index for measuring the popularity of a programming language, in the list of which, Python continued to rise and made it to the top of the list in 2018, and was called the Language of the Year. It was only in January 2019 that it was taken over by Java and C programming languages and Python ranked 3rd, a position which it still stands in August 2019’s popularity index
The growing popularity entails the fact that Python developers will be easier to find and be hired. It is said that Python is the most frequently taught first language in Universities, which is what makes Python the most apt choice for Machine Learning. Conclusion
Machine Learning is growing at a fast pace and it’s about time companies adopted the technology, especially if you want to take your business to the internet by automating certain tasks. It isn’t even a hassle to find and hire Indian developers
as we’re already aware of the popularity of the programming language.
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