Are you a Netflix or Amazon user? Ever thought about how it works to give you personalized suggestions on what you should watch?
Machine learning and artificial intelligence play a huge role here! To remain relevant and in the limelight, innovation is necessary. As such, it is important to keep track of new advances in science and technology and to know how it works.
What does Machine Learning do?
We know ML (Machine Learning) is a set of maths, algorithms, and data processed to deliver an AI capability. Machine learning is a fundamental tool behind any artificial intelligence product that we use today. They are human tools, to be deployed by someone to undertake a specific action. We program AI and ML technologies to create more innovations.
We use a number of programming languages accordingly in Machine learning to attain an output. Here we will discuss the best languages for machine learning in 2020. So, which programming languages will continue to be trending in 2020 and beyond? Let's check
Best Machine Learning Languages in 2021
If you are an aspiring developer, here is what you should know about the top programming languages that will stay in demand in 2020 for Machine Learning.
Python is the most popular and promising programming language for machine learning. There are lots of benefits of using Python for Machine learning. It ranks first among all other languages. Here is why:
- Python has a great library ecosystem that offers you various tools
- SciKit-learn handles classical ML algorithms
- Pandas for high-level data structures and analysis.
- Keras and TensorFlow for deep learning
- Matplotlib for visualization.
- You can easily use and learn Python because it's flexible and has fewer possibilities for error.
- Python is gaining momentum in the Analytics domain since the language is open-source.
- You can implement any changes and quickly see the results. There is no need to recompile the source code in Python.
- Python offers efficient exchange of algorithms and so, there’s no confusion, errors, or conflicting paradigms.
- Tools like IPython provide extra features like testing, debugging, tab completion.
We know Java is also a popular language. Java is used for larger projects in machine learning. Here is how Java contributes to ML
- Java has efficient Libraries for implementing Machine Learning. A few of them are:
- Weka supports deep learning.
- JavaML maintains good documentation with clear interfaces.
- Apache Mahout provides implementations of machine algorithms for the Apache Hadoop platform.
- ADAMS (Advanced Data Mining and Machine Learning Systems) maintain data-driven, performs retrieval, processing, mining, and visualization of data.
- You can easily use and debug Java since it has high performance and is faster.
- Java is effectively used for large-scale project development as you can experience better user interaction.
C++ would be the ‘first language’ we might have used. C++ is used in machine learning even though several programming languages came later. Let’s check how C++ perform in Machine learning :
- C++ is used in search engines, computer games, building neural networks as it facilitates faster execution of complex algorithms.
- You can save time and costs as C++ supports re-use of programs,
- C++ finds easy solutions for complex AI problems.
R Programming is a graphics-based language used for statistical computing and visualizations in machine learning. It is a popular programming language among statisticians. Let’s check how R operates in machine learning.
- R develops bioengineering, bioinformatics, and biomedical statistics.
- R is suited for one-off projects such as reports, research papers, and artifacts.
- You can create predictive models efficiently using the libraries and tools in R.
Scala provides functionalities for big data processing and machine learning analysis. Advantages of Scala are:
- You can develop, design, code, and deploy machine learning algorithms
- Scala programming language suitable for linear algebra, random number generation, and scientific computing.
- Scala can be used in conjunction with Apache Spark to deal with a large volume of data which can also be called Big Data.
Which ML Language should you Opt for?
Machine Learning requires and uses more than one programming language for its output. The above-mentioned are widely used languages in ML and AI. If you ask us the best of all, we would say it is Python, but every language has its specific benefits. Being an AI developer, it’s important that you choose the programming languages wisely for a better output.