The future as we know it is all about Human-Machine interaction and collaboration, With Artificial Intelligence and Machine Learning being the “hottest” thing right now. Especially with all the attention surrounding deep learning. The most important reason why Machine learning has become such a hot phenomenon is its ability to take quick and better decisions based on data, It can take over many repetitive mindless manual tasks. Machine learning will help people across various fields become better at what they are doing by taking over the time consuming analytical tasks. Again, Not all analytical tasks can be taken over by the Machine, Not until few decades but Machine learning algorithms will definitely help us in making our choices clearer.
Machine learning was born when Math, Stats, and Computer Science decided to make a baby. The algorithms are so complex, it will take literally endless hours of programming and endless Lines of code to implement these Machine Learning algorithms. But with Open Source community by our side, We no longer have to spend time implementing these algorithms. People have already done the dirty work for us. Yes, We do have libraries but that doesn’t mean you don’t need to learn about the algorithm. It is absolutely necessary to understand these concepts before you plunge your hands into it.
In this article, We’ll talk about the top programming languages used widely for Machine learning. We’ve prepared this list in a random manner and not ranked by any criteria.
Top Programming/Scripting Languages for Machine Learning
- People have literally called Python the Swiss knife of Machine learning. Although the scripting language is not the fastest but with data analysis modules like Pandas and Numpy have done all the heavy lifting in C, Which makes Pythons performance for data analysis even better. The reason why Python is more popular amongst data scientists and automation experts is that of its ability to solve complex problems with minimum development time and effort, It literally has a module implemented for just about anything.
- You have machine learning libraries like Statsmodel, Scikit-learn, Pylearn and much more
- You can have interactive plots like Seaborn, ggplot2, Bokeh, Plotly by a couple of function calls.
- You have deep learning modules implemented Like Tensor flow, Skflow, Pylearn + Theano. There are even modules available for Natural Language Processing.
- Pythons simple readable code and ease of programming make it one of the first choices for carrying out automation and machine learning related tasks.
- Scala is an unholy but pragmatic marriage between FP and Java, Aimed to address criticism Java usually faces. I personally think Scala can be a good fit for machine learning related tasks because it provides better parameter tuning and debugging. Performance tuning helps machine learning related applications on the production environment.
- Scala is definitely a choice for people familiar with JVM-centric tech stack. People working with Java or Big Data technologies like Hadoop and Apache Spark and want to get their hands dirty by diving into Machine learning, Scala is definitely the answer. Scala works seamlessly with Apache Spark, Which is one of the major pros. Although you can always use Pyspark(Python based Spark interaction) for Apache Spark, its more restrictive and has less control on Spark libraries. Scala will always excel when its about Hadoop and Spark.
- Scala also has Data manipulation libraries like Saddle which is similar to Pythons Pandas module.
- Scala also has Deep learning modules DeepLeaning4j and H2o.ai
- This list would be incomplete without mentioning the programming language whose main purpose was statistical computing and visualizations. R is used widely by data scientists and data miners
- R has capabilities to manage multidimensional data, very easily and efficiently. The build in data structures is very comprehensive to represent any statistical data. Apart from providing you with data structures, R provides simplest of the functions to handle your data, It hides all the complexity from the end recipient so that the user can only focus on the problem he/she is trying to solve.
- R has a huge collection of machine learning algorithms within its environment. It provides all the algorithms implemented, Few function calls and you are done.
- You can check out the list of packages R provides for your ML related tasks.
- The only limitation we see is, R cannot cope with deep learning algorithms. Mainly due to computational constraints(GPU based processing)
- Julia surfaced a few years ago developed for the needs of high-performance numerical analysis and computational science. Julia is definitely a candidate suitable to carry out complex machine learning related tasks. Compared to R and Python it still has a long way to go.
- The reason we are in favor of Julia is that of its performance and its strikingly similar syntax to python. Julia provides distributed parallel execution, numerical accuracy, and extensive mathematical library. It provides brilliant computational performance comparable to that of C programming languages performance.
- Unlike R and Python, Julia is well suited for Parallel computation. So we have extensive mathematical functions with a brilliant mix of parallel computation.
- Julia’s implementation of message passing is different from other environments such as MPI (OpenMPI to be specific). Communication in Julia is generally “one-sided”, meaning that the programmer needs to explicitly manage only one process in a two-process operation. This makes the job of the programmer a lot easier. Furthermore, these operations typically do not look like “message send” and “message received” but rather resemble higher-level operations like calls to user functions.
- It’s statistical and machine learning packages are evolving at a good pace and provides an amazing library for Deep learning.
- Julia is good right now, and you can do pretty much everything with it. It is becoming better and better with every release. I will not say that it is the “only” future of programming, but it definitely is there somewhere and it definitely is worth learning.
With the Machine Learning, AI and automation revolution. We definitely have some dope programming languages and some amazing tools along with it to build some amazing solutions. Let us know which language we should dig deeper in the comments below!