Which Programming Language is Best for Machine Learning?

 

Machine learning (ML) is a topic that has been talked about much in recent times. So, what is ML? It is a branch of Artificial Intelligence (AI) that builds models and trains them to learn from data and make accurate predictions without being explicitly programmed. It is used in search engines to provide better results on SERPs, filtering out spam emails, making recommendations, and much more. Machine Learning training programs can help you acquire prowess in this emerging technology and prepare you to be a job-ready candidate.

To comprehend how a machine learning system works, let us see the lifecycle of an ML project. The ML lifecycle comprises seven stages that you can learn in detail at the best Machine Learning Bootcamp. Let us go over the various stages briefly.

• Data collection – The first step in an ML project lifecycle is to collect data from various sources. You need to identify the data sources such as files, the internet, database, etc., collect data from them, and integrate the data. The result of this step is a dataset.

• Data preparation – Here, you put the data in specific places and prepare it for ML training. The first process is exploration, in which you understand the nature, format, and characteristics of the dataset, and the following process is where you process the dataset for further analysis.

• Data cleaning – Data cleaning is the most crucial step in Machine Learning. The data you collect might have errors and issues like missing values, duplicate entries, invalidity, and so on. This step cleans the data, selects the variables to be used, and transforms the data into a suitable format.

• Data Analysis – In this stage, you select the ML algorithm to be used, such as Classification, Cluster analysis, Regression, Association, etc. After which, you have to build a model to apply the technique and analyze the outcome. 

• Training the model – The model is trained using various datasets and machine learning algorithms to optimize its performance. Training will help the model to understand patterns, features, and trends effortlessly.

• Model testing – Once the model is trained, the next stage is testing it. For this, a test dataset is provided to the model; the outcome is then analyzed to determine the model’s accuracy as per requirement. 

• Deployment – The final stage in an ML project is deployment. After testing the model for accuracy, it is then deployed for real-world use in systems.

What are some of the areas where Machine Learning is applied?

ML is a field that is swiftly growing. Even as you are reading this blog, the robust technology is being put to use in numerous real-world scenarios. Some of the significant applications of ML are:

• Image recognition: Image recognition is mainly used in social networking sites in auto-tagging suggestions. The technology used in this application is the face detection and recognition algorithm.

• Traffic prediction: Google Maps and similar applications use ML algorithms to predict whether the road is clear, the traffic is slow-moving, or if it is heavily congested based on real-time location, sensors, and the average time taken in the past days.  

• Social media: Social networking websites like Facebook and Instagram records your activities, chats, likes, and the time you spend on posts to give you a better experience by improving friend and page suggestions.

• Product recommendations: These algorithms are used by numerous e-commerce and entertainment websites like Amazon, Flipkart, Netflix, etc., to make recommendations to their customers. For instance, if we search for something in Amazon, suggestions for the same start appearing in the browser, and similar product recommendations are displayed on Amazon’s page.

• Virtual Personal Assistants: There are several virtual personal assistants in the market today, like Siri, Alexa, Cortana, etc. These assistants work on instructions; i.e., they send your voice commands to a cloud server, which then decodes the instructions to act accordingly.

• Email and spam malware filtering: Emails are automatically filtered and classified based on Important, Spam, Social, etc. Machine learning algorithms like Decision tree, Multi-Layer Perceptron, and Naive Bayes classifier are used to make such classifications.

• Banking and financial sector: Banks and various financial institutions are using this technology to detect fraudulent transactions and keep hackers at bay. The algorithms help in analyzing suspicious websites and prevent them from making transactions.

• Automatic Language Translation: Today, language is no longer a barrier to anything. Thanks to Machine Learning! With the technology called Machine Translation, anything on a page can be translated from one language to another effortlessly.

• Medical field: The healthcare sector is making use of ML for better management. These models help predict patients’ waiting time, detect diseases and disease situations, and plan therapy for patients.

If you are an aspiring Machine Learning programmer and want to register in a Machine Learning Bootcamp to improve your knowledge of this robust technology, a predominant question you would have in mind is which language to master. Although, as per industry experts, there is nothing known as one best language for ML, every language has its advantages and disadvantages; given below are a few languages you could learn to enter the world of ML and AI.

• Python

Python is a versatile language with a highly readable syntax. Unlike conventional software projects, AI projects have complex technology stacks and skills. Python, with its wide variety of libraries and frameworks, helps programmers in solving problems quickly. This programming language is platform-independent, which means the same code can be run on numerous systems like Windows, Linux, Macintosh, and so on, without an interpreter. This cuts down on the training cost. Some of the most prominent libraries offered by Python are

• Keras
• Tensorflow
• Scikit learn
• NumPy
• SciPy
• Pandas
• Seaborn

• R

R is an open-source language that excels in data analysis and visualization, as it was built for high-level statistics and data visualizations. You can create highly impressive ML models by using the rapid prototyping ability of R. It also has many versatile libraries to cover all the ML needs. In addition, R has an enormous support community along with GNU packages and competes with expensive alternatives like SAS, Matlab, etc. The major libraries of R are:

• Tidyr
• Ggplot2
• Dplyr
• Tidyquant

• Julia

Julia is a language that has the functionality of Python, Matlab, and R, with the execution speed of C++ and Java. It is excellent for executing basic math and science and is best suited for scientific computing. Julia is faster and much more scalable than Python and R. Some of the major libraries are:

• Flux
• Knet
• MLBase.jl
• TensorFlow.jl
• ScikitLearn.jl

• C/C++

C/C++ is one of the fastest and most efficient languages in the programming world. Most of the ML frameworks are implemented in C++, such as caffe, TensorFlow, vowpal, libsvm, wabbit, etc. C/++ can be used in ML projects when there is an extreme need for speed, Python libraries are not available, and you want to control memory usage. Popular C/C++ libraries are:

• Tensorflow
• Microsoft Cognitive Toolkit
• Caffe
• Mlpack
• DyNet (Dynamic Neural Network Toolkit)
• Shogun

• Java

Many data science frameworks are written in Java like Hadoop, Fink, Hive, and Spark. Integration and compatibility will be smoother since most organizations use Java to build their infrastructure and software applications. Java is a highly scalable language and can be used in various processes like statistical analysis, deep learning, data visualization, etc. Some of the most popular libraries offered by Java are:

• Weka
• Apache mahout
• Massive Online Analysis
• Deeplearning4j
• Mallet

• Scala

Scala is faster than Python and is a highly functional and object-oriented programming language. Programmers can build enhanced systems using Scala’s static types. Scala is the best fit for Apache Spark, considering the performance, learning curve, and usability. It has multiple libraries used for random number generation, linear algebra, scientific computing, etc. They are:

• Saddle
• Aerosol
• Breeze
• Scalalab
• NLP

As mentioned, there is nothing like the best language for machine learning. Experimenting and testing various languages help ML developers to reach a consensus on the language they would like to use in ML projects. Of course, the best thing to do would be to master more than one language to make your resume stand out in front of prospective employers. If you are looking for the best Machine Learning training program in California, then SynergisticIT is the best choice you can make. Register today and enlighten yourself with industry best practices and core concepts of ML along with hands-on training in real-world projects!

Source: https://buzztum.com/which-programming-language-is-best-for-machine-learning/