collageIn the data-driven era, artificial intelligence, and its powerful subset – machine learning has been the driving force behind numerous businesses. Acting as a catalyst in speeding up performance and productivity, machine learning has been supporting many a business activity on a routine basis. Machine learning-based solutions are being adapted on an increasing spree.
As a powerful science behind machines that can do human-based activities, machine learning focuses on the study of computing algorithms to make the system take decisions without writing manual code.
There have been many programming languages for machine learning that are popular worldwide. Before we explore the best programming language for machine learning and artificial intelligence, let us quickly understand the basics of machine learning, its sample application areas, and the companies using machine learning.
What Is Machine Learning?
Machine learning is a modern-day discipline that utilizes statistics, algorithms, probability to extract the best out of data and offer insightful information that can be leveraged to create intelligent applications. Being a strong part of AI, it has a pool of algorithms and methods that connect the data depending upon the patterns and analytical methods.
Machine learning has a stringent focus on pattern recognition, predictive analytics, data mining and has a close association with big data and data analytics. It has the power to enable machines to mimic human decision-making through mathematical models and advanced prediction statistics.
Machine learning basically consists of three types – Supervised (based on labeled data), Unsupervised (based on unlabelled data, hidden patterns), and Reinforcement learning (based on learning from mistakes, trials, errors).
Machine Learning Uses:
- Self-driving autonomous cars
- Speech translation
- Face recognition
- Sentiment analysis
- Social media analysis
- Financial trading
- Fraud detection
- Product recommendation
- Medical diagnosis and predictions
Companies Using Machine Learning:
- Yelp
- HubSpot
- Pindrop
- Apple
- Salesforce
- Intel
- Microsoft
- IBM
- Baidu
It is debatable as to how much knowledge of programming languages is needed to implement machine learning models with effectiveness. It totally depends on the type of usage and what type of real-world problems are being solved. To explore the best of machine learning, it is vital to have basic knowledge of programming languages and their salient features like algorithms, data structures, logic, memory management, etc. Of course, machine learning has its own set of libraries to act upon that makes it easy for programmers to implement machine learning logic along with certain standard programming languages.
There are many programming languages around the globe, that raise the question of what programming language is best for machine learning. Here is a list of the best programming language for Machine learning.
Python:
Python is a lightweight, versatile, simple programming language that can power complex scripting and web app if used in an effective framework. It was created in 1991 as a general-purpose programming language. Developers have always admired it as a simple, easy to learn and its popularity knows no bounds. It supports multiple frameworks and libraries making it versatile.
Python developers are in trend since it is one of the most sought-after languages in the machine learning, data analytics, and web development arena, and developers find it fast to code and easy to learn. Python is liked by all since it allows a great deal of flexibility while coding. Thanks to its scalability and open-source nature, it has multiple visualization packages and important core libraries like sklearn, seaborn, etc. These powerful libraries make coding an easy task and empower machines to learn more.
Python supports object-oriented, functional, imperative, and procedural development paradigms. Two highly popular machine learning libraries with Python developers are TensorFlow and Scikit. It is considered ideal for prototyping, scientific computing, sentiment analysis, natural language processing, and data science.
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