Is Python Enough for Machine Learning

Machine learning is one of the hottest topics in today’s time and industries are incorporating machine learning into their apps and products to provide the best solutions/products to their customers. Tech Giants such as Facebook, Google, and Twitter are using machine learning models in their apps. For example, twitter’s algorithm displays the tweet, which likely to drive the most engagement. The algorithm makes decisions based on individual preferences resulting in a curated Twitter feed or timeline. Salesforce (Tech giant) uses Intelligent CRMs (Customer relationship management) software to analyze the customer’s relationship to generate a more detailed customer profile to increase their sales and provide effective customer service.

What is Machine Learning?

Machine learning is an application of Artificial intelligence that aims to enable computers to learn automatically. From a theoretical perspective, machine learning is a combination of calculus, statistics, matrix multiplication, and programming. 

The process of training the models begins with the observation of data, identifying patterns in data, and making efficient decisions in the future based on the data we train the model on and without any intervention of human beings.

What are the Types of Machine Learning? 

There are three types of machine learning: Supervised, Unsupervised, and Reinforcement Algorithms.

Supervised vs. Unsupervised vs. Reinforcement Learning

Supervised learning algorithm produces a function that generates a prediction about the output values. Unsupervised learning generates a function to explore the hidden patterns from unclassified and unlabeled data. In Reinforcement learning, the model interacts with the environment to provide feedback to the program’s response in the environment to maximize the performance like driving a car, inventory management, and delivery management systems.

Is Python Enough for Machine Learning?

Python is undoubtedly a great choice when it comes to choosing a programming language for Machine Learning. Python provides almost everything an ML developer needs, in the form of built-in tools and libraries. While Python certainly helps with quickly and efficiently building ML models, it’s not the only skill that’s required.

Machine learning is not about coding from scratch, Python provides almost everything an ML developer could wish for. Even though there are many libraries you could use to perform the statistical and mathematical analysis while not being good at them, you would be no more than an average professional.

Hence, to be amongst the top-tier ML professionals, it’s necessary to be proficient in Python and have your strong basics built when it comes to mathematics and statistics. This way, you would develop a solution and make it as optimal as possible, which is the real deal nowadays.

Also, other programming languages might be required for specific tasks in Machine Learning, such as statistical analysis. A great example is the R programming language. Though Python can do almost everything it can do, there are areas where R is simply better optimized.

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Why is Python Suitable for Machine Learning?

We’ve established that Python is not the standalone skill you need to acquire for machine learning, but let’s look at some reasons why it’s probably the most important skill to have. 

  • Great Learning Curve
  • Access to Various Libraries and Frameworks
  • Platform Independence
  • Wide Variety of Visualizations
  • Large Community

There are a handful of reasons why ML developers worldwide agree that Python is the suitable for Machine Learning, such as:

Great Learning Curve 

The best thing that Python offers is that its syntax is simple, efficient, and easily understandable by the developers. This way, the developers can focus more on the actual machine learning project rather than handling the complexities of the programming language. Also, one can perform a task with fewer lines of code when compared to other programming languages. There are tools available which are interactive and user friendly as it provides features such as testing and debugging which facilitate the process.

Access to Various Libraries and Frameworks

No language can compare it with Python when matching the number of libraries and frameworks. The library is a module that includes a pre-written piece of code that allows users to achieve a specific result without explicitly programming the entire function. This feature saves development time as they don’t have to code them from the beginning. The most commonly used libraries of Python are:

  • Pandas: For the manipulation of data and analysis, data filtering as well as importing of data from other sources
  • Scikit-learn: For handling machine learning algorithms such as regression, clustering, classification, etc.
  • SciPy: For advanced computing
  • Seaborn and Matplotlib: For visualizations of data with charts, histograms, pie charts, plots, etc.
  • Keras: For Deep learning is used to reduce the cognitive load by performing fast calculations.
  • NLTK: For the processing of text and language recognition

Platform Independence 

Python is quite a versatile programming language. It can run on multiple platforms, including Windows, Linux, Mac, UNIX, etc. To run on different platforms, developers need to make minimal changes to the program to make it an executable form of code for the particular platform. For this, they can also use PyInstaller for changes in the code.

Wide Variety of Visualizations

Machine learning is used to draw conclusions from raw data and extract meaningful insights out of it. Python has several libraries and frameworks for visualizations of results so that data can be transformed to an intuitive and easy-to-understand format. For example, Matplotlib is used for the generation of histograms, charts, pie charts, etc. Seaborn is another great visualization tool that helps come up with fancy visualizations.

Large Community

Python is among the top 10 popular programming languages in the world. According to the google survey, Python is the most searched language on google than any other language. This essentially results in a wide community of developers worldwide that help each other grow. No matter what kind of problem you’re facing, you will always find someone who went through the same course and can guide you in the best way possible. Such a huge community also facilitates resource sharing, and everybody saves their time and gets to learn from others’ mistakes.

Before we go into the key take aways, let me recommend you some interesting articles to check right after this one.

Key Takeaway

Machine learning is a versatile field. Its pipeline comprises complex data processing, high-level coding, and implementation of several algorithms. While there are many programming languages one can choose from, Python remains to be the most-liked throughout the world. There are countless reasons why this is so. Throughout the article, we have discussed the reasons and saw why it’s the hot favorite.

So…Is Python Enough for Machine Learning?

Python is not the only thing you need to ace if you’re looking to reach the pinnacle of Machine learning. You also need to master the art of combining statistics with data and how it affects the real world. Moreover, you might need to learn other programming languages and perform some specific things that Python isn’t the best at. 

So, if you’re looking to jump into the world of Machine Learning, Python should be amongst the top entries on your priority list. But don’t fall for it if anybody tries to tell you that Python can single-handedly manage everything Machine Learning is about.

Instead, keep working on your mathematical and statistical skills and keep them applying as well. If you’re into statistical research or that sort of development, don’t forget to check out other programming languages like R, as they have much better support than Python when it comes to specific purposes.

Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

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