The online discussion forums are buzzing with so many questions on career options in data science and how to become a successful data scientist.

For example:

Am I cut out for the advanced Artificial Intelligence and Machine Learning certification programs?

What is the most important skill in ML development do I have to learn to become successful?

What percentage of the data science ML course involves working with programming languages?

Which programming languages do I have to learn to clear Machine Learning certification?

Let’s try to get answers to these common FAQs.

A large number of fresh graduates are enrolling in online machine learning certification courses. This is the best time to join an ML course online as many organizations are hiring data scientists at Junior and mid-management levels to work on enticing Artificial Intelligence and Data Science projects. However, the journey to becoming a certified ML engineer or developer is not that easy. You have to keep in mind the finest nuances of the certification program before diving into the curriculum.

Pillars of Machine Learning Online Courses

Programming languages, Basic Mathematics, and Advanced Statistical Science are the three pillars of the machine learning development project. The pillars of machine learning haven’t changed one bit in the last five decades, except for one thing. Now you can use AI to create codes for coding, we are finding new ways to automate writing codes. From defining data sets to identifying variables, coefficients, and functions in various equations, we find innovative ways of using AI for everything.

Programming Language is Chosen Based on Project Demands and Outcomes

Programming languages in the Machine Learning certification are worked out based on the dataset available. In real-life scenarios, the exact opposite happens. Datasets may be trained based on the ML developers available and what programming skills they have. That’s why so many projects fail to ever produce results.

The commercial side of the ML development should stick to a simple rule – choose a language that can help analysts build algorithms that differentiate between the lions, tigers, cats, and mice.

There are hundreds of ML programming languages. Yet, only a handful of these is ever used commercially. Rest are mostly developed and included in the curriculum for academic purposes. In a survey of 10,000+ machine learning developers and analysts, it was found that 57% of the developers have no control of the programming language they are going to use for the project. 93% use the available resources to start coding from the scratch. Many develop collaborative coding platforms to build interesting frameworks for their AI and ML training activities.

Python is popular, and hence most widely used

Python is the number one programming language for machine learning projects. A majority of the projects use Python training at the conception and development stages. Owing to the massive development in the fields of Deep Learning, GPUs, and Computer Vision, developers prefer to use a language that is readily available, is resource intensive, and can be easily deployed on any machine for quick ML training. Python frameworks work on any home PC with bare minimum configuration and can be upgraded to the latest versions. These can be integrated with any open source data set and other libraries available from online resources.

Python’s superiority can be gauged from the fact that it is recommended for use with the TensorFlow library for neural networks.

What is TensorFlow?

TensorFlow is an end-to-end open-source platform for AI and machine learning. Anyone with knowledge of Python can build and deploy ML-powered applications on the TensorFlow framework.

Once you master Python for AI ML base, you can expand your horizon into learning other languages that are used for various applications. These languages are:

  • R
  • C / C++
  • Java
  • Octave
  • SCALA, and so on.

Of these, you will find R used more often as a Python complementary language. It is still not clear if R would replace Python in the coming years. In contrast, Java, the most popular coding language otherwise is also gaining popularity. Cloud Security, IT and DevOps, and Mobile application development teams prefer to use Java and JavaScript. For gaming, and AR VR projects, C/ C++ is used with R as a standby.

A large portion of a Machine Learning certification course involves working on advanced programming languages. If you are new to the ML field, chances are you may find working with the AI programming languages an interesting proposition to deal with. If that happens, you are doing your best.

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