Looking for the best language for machine learning? If you’re new to the topic, the hardest part of mastering machine learning is figuring out where to start. It is normal to question the ideal language for machine learning, regardless of whether you are looking to brush up on your machine learning knowledge or completely change careers.

Finding the ideal programming language for machine learning is undoubtedly difficult because over 700 distinct programming languages are widely used, and each has advantages and disadvantages. The good news is that you’ll start to identify which programming language will best suit a business problem you are trying to address as you start your journey as a machine learning engineer.

Best language for machine learning

Which programming language is ideal for machine learning is certainly on your mind if you’re considering a career in this area. While numerous options are available for various uses, in this post, we’ll focus on the top machine learning languages.

Machine learning programming

It’s crucial to comprehend the fundamentals of creating an ML model before discovering why particular programming languages are better suited for ML.

The closest thing to imitating the human brain is machine learning. In order to create predictions, ML algorithms look for patterns in large swathes of data, such as photos, numbers, or text. These robust machine learning algorithms are what power search engines and content recommendation systems.

The dataset must correspond to the predictions the model will produce in the real world. Training data can be categorized into classes, and the model will learn to distinguish between the classes by identifying distinctive characteristics and patterns. For instance, the dataset will contain images of the two animals with the appropriate labels if you train a model to sort zebras and giraffes.

To ensure that the model’s predictions are accurate and fair, you should create an inclusive dataset. The dataset ought to be comprehensive, deduplicated, randomized, deduplicated, and divided into training and testing sets.

The training set is used to train the model, and the test set is used to evaluate the model’s accuracy and spot any potential problems. There shouldn’t be any data overlap between the training and test sets.

The model iteratively trains on the dataset, modifying weights and biases in response to inaccurate outputs.

Unraveling the matryoshka doll: AI vs. ML vs. ANN vs. DL

Testing or evaluating the model on fresh data that has never been used for training will allow you to determine how accurate it is. This will enable you to comprehend how your ML model functions in practical situations.

After evaluation, you can adjust hyperparameters that we might have initially taken for granted during training; doing so can sometimes be an experimental process that varies depending on your dataset, model, and training procedure. After understanding the basics of ML programming, the next step you need to take is to find the best language for machine learning, considering the needs of your project.

The future of machine learning

In addition to providing fantastic development prospects, machine learning is upending established sectors. One of humanity’s greatest partners, machine learning enables organizations to make better decisions, aids developers in solving challenges creatively, and provides insights continuously at superhuman speeds and accuracy.

Healthcare, finance, banking, retail, manufacturing, and transportation are just a few of the industries that have used machine learning. Machine learning has the potential to transform these industries in a variety of ways, from ground-breaking innovations to more routine tasks like fraud detection or customer service.

Predictive analysis is another use of machine learning that will be used more frequently. Predictive analytics requires the creation and assessment of models to provide accurate projections. There are several utilized, including Python, R, and RapidMiner. The model also offers sales and marketing forecasts.

Predictions are made by identifying patterns and evaluating causal relationships in both recent and historical data. Future data forecasting also makes use of this information. Examples of this type of analytics include regression, classification, and segmentation training algorithms.

For instance, banks can test hypotheses using machine learning approaches. A bank can predict which customers are most likely to default in the future by looking at previous data on which customers have defaulted. You’ll be in a better position to decide whether to approve a loan as a result. If you want to change the future, maybe finding the best language for machine learning for your initiative can be a key factor.

Machine learning language list

• PythonScala
• R programming
• Java and JavaScript
• C++
• Shell
• Golang
• Lisp
• Julia
• TypeScript

Top 10 machine learning languages

Now let’s examine the best language for machine learning. Will we be able to find it? At the end of the day, the best language for machine learning will depend on your project!

Python

Python is a lightweight, adaptable, and straightforward programming language that can power sophisticated scripting and online applications when used in a strong framework. As a general-purpose programming language, it was developed in 1991. Developers have long praised its simplicity and ease of learning, and its popularity knows no bounds. It is versatile and supports a variety of frameworks and libraries.

Because Python is one of the most in-demand programming languages for machine learning, data analytics, and web development, and because it is quick to code and simple to learn, Python engineers are in demand. Everyone enjoys Python because it offers a lot of coding flexibility. It has a number of visualization packages and significant core libraries like sklearn, seaborn, etc., because of its scalability and open-source nature. These strong libraries facilitate coding and give computers the ability to learn more.

ML engineers build the bridge between data and AI

The procedural, imperative, functional, and object-oriented development paradigms are all supported by Python. TensorFlow and Scikit are two extremely well-liked machine learning libraries among Python programmers. It is regarded as the best for data science, sentiment analysis, natural language processing, and data science prototyping. Python is considered the best language for machine learning by a lot of coding experts.

Scala

A popular compiled language called Scala makes executable code run quickly. It has a static type of system that works well with Java libraries and frameworks. Enterprise-level programs with large databases and scalable solutions are known to be handled by Scala. Its unique selling point is the ability to build apps that are powered by big data and include vast amounts of data.

Because of its powerful backend language, it can handle a large volume of data flow. Scala’s MLLIB library, which the well-known Apache Spark supports, provides competitive functionality. Combining Spark’s capabilities with other big data tools and technologies gives developers an efficient way to create, design, and deploy machine learning algorithms.

Many useful libraries in Scala, including Aerosol, Saddle, and others, can be used to create programs for scientific computing, linear algebra, and random number generation. These libraries provide excellent data manipulation capabilities through various features like automated data alignment, 2D data structures, and other features. Scala can easily be counted as the best language for machine learning if you are looking to handle a large volume of data flow.

R programming

R is a well-known open-source data visualization language with a strong emphasis on statistical computing that dominates the machine learning space. The R Foundation and R development core team are in charge of its administration. The USP of R is that it is preferred by professionals like analysts, statisticians, and data miners who are not well-versed in coding. It supports a command line and other IDEs, makes coding simple, and has various tools for better library management and graphing.

Thanks to its distinctive characteristics that help develop machine learning apps, R has a good resource pool. It has been widely used for data and statistics. With its powerful computational capabilities, effective machine learning solutions can be provided. It is a graphics-based language used by large corporations, particularly in the biomedical industry, and data scientists for graph-based data analysis.

Machine learning techniques like classification, regression, decision tree building, etc., are often implemented in R. It has been a dynamic, imperative, functional language because of its statistical and functional properties. It supports a variety of OSes, including Windows, Linux, and OS X.

Java and JavaScript

Java and JavaScript are general-purpose programming languages that have established value for machine learning methods and applications. These languages, renowned for their stability and dependability, are object-oriented and support intensive data processing skills. Strong Java frameworks that handle machine learning algorithms, decision trees, regression approaches, etc. include Weka, Rapid Miner, and others. When used with enterprise-based systems, it has been quite effective. Because it is a simple language to learn, there are many resources available for JavaScript.

Finding loopholes with machine learning techniques

Giant firms frequently use Java and JavaScript in their high-profile initiatives. These technologies, thought to be efficient for machine learning applications, rely on the numerous machine learning libraries connected to them. They are being used by experts to strengthen network security and to identify frauds and cyberattacks.

The Java and JavaScript programming languages enjoy a large following in the machine learning community as a result of their features, including package services, graphical representation, and greater user involvement. When creating algorithms and interpreting them on dashboards and reports, they ensure they are quick, accurate, and precise. That is why, depending on the project, Java can be the best language for machine learning models.

C++

Powerful, adaptable, and well-liked, C++ is one of the world’s most widely used and favored programming languages. And there is no turning back when it comes to creating machine learning algorithms. These are established languages that have long dominated the developer community and, thanks to regular upgrades, have kept up with the most recent technological developments.

Since they are regarded as low-level languages, computers can easily understand them. Offering hardware-level features is simple, making it simple to integrate machine learning apps on IoT devices. It is perfect for these applications due to its quick execution and delivery times.

Many powerful libraries, including Torch, TensorFlow, and others, are created in C/C++. They have demonstrated their value for applications that depend on performance. C++ can perform meticulous memory management at a detailed level and manipulate algorithms. It provides extensive control over various performance parameters, which makes it the best language for machine learning in some situations.

Shell

The Unix shell, a command-line interpreter, was designed to run the shell programming language. Shell is a great option for creating machine learning models, algorithms, and applications because of its scripting languages and wrappers, which use its straightforward syntax.

Through the use of mathematical models, Shell, a user interface for performing processes, can be very useful in gathering and preparing data. All operating systems, including Windows, Linux, and macOS, can use Shell, which provides extremely high mobility.

Data collection and preparation for further computation are accomplished using shell commands and scripts. It offers a simple and user-friendly method of processing data.

Golang

Go (Golang) has gained popularity because of its key characteristics, including its open-source status, Google ownership, and execution-lightness. With several processes being completed simultaneously, it has the ability to include massive data sets more easily. Its positive aspect is its concurrency. It is a programming language for systems and comes with a built-in vocabulary.

It is one of the languages that is GitHub’s fastest-growing, and cloud computing services often accept it well. It is well-liked in the serverless computing infrastructure due to characteristics like garbage collection, dynamic typing, etc., similar to C.

Go is seen as relatively easier to learn, and developers quickly accept it thanks to its clear syntax and security. It might be the best language for machine learning if you are a starter.

Lisp

An older programming language called Lisp has recently gained popularity for use in AI and machine learning projects. Developers like it because of its methods and design, particularly for projects involving artificial intelligence and machine learning. It provides its developers with countless opportunities.

It is well-known for its salient features, including domain-specific language interwoven with code and building owners. Developers have enjoyed using its features to build machine learning applications because there are several opportunities to do so.

John McCarthy, the father of artificial intelligence, created lisp, which has its own benefits. It has been shown to be effective for prototyping and makes it simple and quick to create new items. An automated garbage collection feature is available to help keep things running smoothly.

Julia

A well-known high-level, dynamic programming language called Julia is the best language for machine learning to produce the efficient model analytics required for building ML applications. Developers prefer it because it has a straightforward syntax and solid performance language. It offers a variety of benefits, including numerical accuracy, a modern compiler, distributed parallel execution, and a sizable library of mathematical functions.

It operates without a hitch across various platforms and is regarded as interactive regarding scripting. It has a sizable fan base and is regarded as the best option for creating machine learning apps because it is functional and object-oriented. It is approachable and simple to comprehend. It is free and open source under the MIT license.

Julia can operate at its best on the server and the client sides. When performing computational statistics and numerical calculations, it is highly effective. As a result, it is thought to be perfect for statisticians working in the fields of analytics and bioinformatics.

TypeScript

Microsoft created TypeScript, an object-oriented programming language, in 2012. For application scale development, it is JavaScript. It is regarded as an excellent option for creating machine learning apps thanks to the TypeScript-written, browser-based Kalimdor library. JavaScript is the foundation of TypeScript and is supported throughout by JavaScript libraries.

It is a strongly typed, compiled language. It is regarded as a language and a collection of tools that, in essence, are extensions of JavaScript. The language, TypeScript Compiler, and TypeScript Language Service make up the heart of TypeScript.

It is the best language for machine learning if you are utilizing TypeScript because it is a condensed version of JavaScript, making it simpler to comprehend and debug. It provides efficient JavaScript IDE development tools as well as other programming techniques. It becomes much easier to understand and read the code.

FAQ

Is Java good for machine learning?

Java has a history and is widely used in enterprise applications, but it also offers other advantages that make it a good development platform for machine learning. Since it has strong typing, programmers are forced to be explicit and precise about variables and data types. The management of huge data applications is made simpler, and the codebase is easier to maintain thanks to this highly typed feature.

Comprehending a machine learning pipeline architecture

Java is also quicker than other programming languages for machine learning. It is a fantastic solution for creating larger and more intricate machine learning applications due to its inherent scalability. Software developers can write code for several platforms and produce unique tools for any machine learning solution since it uses the Java Virtual Machine. So depending on the situation, it can be the best language for machine learning.

Is C++ a good language for machine learning?

Yes, machine learning algorithms must be quick and cleanly coded, which is the short response. With C++, you can build complex computer vision and machine learning systems from the ground up.

Direct pointer manipulations are available in C++, along with many other low-level capabilities, including the option of the memory management system. To manage memory allocations and deallocations, you can create your own algorithms.

In general, you don’t like thinking of algorithms as “black boxes,” so people try things out for themselves to see how they work and to understand how systems operate so they can develop and implement better systems.

It all comes down to how much knowledge you actually need. Break things down and look inside if you want to learn more about machine learning at a practical expert level.

Conclusion

Machine learning is significant because it aids in developing new goods and provides businesses with a picture of trends in consumer behavior and operational business patterns. A significant portion of the operations of many of today’s top businesses, like Facebook, Google, and Uber, revolve around machine learning. For many businesses, machine learning has emerged as a key competitive differentiation. If you want to implement a machine learning project into your business, choosing the best language for machine learning is vital.

What programming language for artificial intelligence is suitable for you? It is a crucial question for your company’s future. Every major tech business and even startups are working on artificial intelligence (AI), which has emerged as one of the hottest issues and largest study disciplines. It’s a tremendously broad topic that covers anything from simple calculators and self-driving cars to intelligent robots that could fundamentally alter the course of human history.

The core of AI is creating machines that are as intelligent as or more intelligent than humans. But how?

Better AI solutions are continuously being sought after by businesses. IDC projects that the market for artificial intelligence will reach $500 billion by 2024, with a five-year CAGR of 17.5% and total revenue of $554.3 billion. However, picking the right programming languages for AI software is the first step in developing effective solutions.

What programming language for artificial intelligence is the best?

The need for AI practitioners is rising as a result of this digital change. In fact, between 2020 and 2021, recruiting for AI-related positions surged by 165 percent. You must learn how to use AI programming languages that are supported by powerful machine learning and deep learning libraries if you want to work in the industry.

Artificial intelligence career paths are expanding, with artificial intelligence designers being one of them.

Programming languages come in a wide variety, from Java to Julia, so where do you begin? You have arrived at the right place if you are an AI enthusiast who is unsure about which coding language to use for your upcoming major project.

There are many AI programming languages. However, none of them can legitimately be referred to as “the best ai programming language.” Each has advantages and disadvantages. Yet these five are typically well-liked:

• Python
• JavaScript
• Java
• Scala
• R

Later, we shall dive into them and the others. But first, let’s examine the rationale behind their adoption by artificial intelligence engineers.

What programming languages do artificial intelligence engineers use?

A programming language is a computer language used to write instructions and transmit them to computers and other computer-based devices. To communicate with computers, software engineers and developers employ programming languages, which can be divided into five main types.

These subcategories include:

• Logic,
• Procedural,
• Object-oriented,
• Functional,
• Scripting programming languages.

AI engineers frequently use programming languages for scripting. When designing learning algorithms and automating processes that often require human involvement, this type of language offers a significant benefit over alternatives. Scripting programming languages are often interpreted—not compiled—into machine-readable languages.

So, let’s explore them!

Top programming language for artificial intelligence (2022)

There aren’t always universal, one-size-fits-all solutions in the realm of artificial intelligence. The needs and scale of your project will determine the AI programming language you use. If your project calls for substantial data analysis, consider using R, which was created to handle large data sets easily. However, Python would be better for implementing machine learning models in production than R.

Don’t be scared of AI jargon; we have created a detailed AI glossary for the most commonly used artificial intelligence terms and explain the basics of artificial intelligence as well as the risks and benefits of artificial intelligence.

Each programming language has its own special strengths when it comes to programming for artificial intelligence. Others are built to perform numerical analysis, while some are excellent at natural language processing. Let’s analyze the numerous uses and benefits of the top AI programming languages and find what programming language for artificial intelligence suits you.

Python

As you can imagine, Python will be where we begin. Right now, Python can be thought of as the precursor to all other languages. The Python language’s straightforward syntax is the cause of its explosive success. Python is a great choice for Machine Learning engineering because of its easy syntax, freeing up a lot more time to prepare the fundamental structure. It is one of the best answers to the question of “What programming language for artificial intelligence is the best?”

The most popular usage of Python, an all-purpose, object-oriented, high-level programming language, is for scripting small to very large projects. Python has several applications in numerous industries on its own. Many engineers pick Python as their favorite language due of its straightforward syntax and use of English-language terms.

These technologies can also be used for sophisticated mathematical expression evaluation and natural language processing (NLP), in addition to machine learning.

Smart assistants like Google and Alexa use natural language processing (NLP) to comprehend what you’re saying and answer properly.

The nicest feature is how well Python integrates with other languages, like Java, for increased convenience. One of the greatest programming languages for artificial intelligence projects right now is Python, thanks to its ease of use and the large open source community that supports it.

The extensive range of support provided by AI libraries is one of the main benefits of Python for AI over other programming languages. Libraries are collections of tools that facilitate the application of specific ideas. The general-purpose Python languages can gain specialized AI features from these modules. Let’s examine the most widely used Python AI libraries in more detail.

TensorFlow

Google created and released the Python library TensorFlow for use in AI applications. The library is used to create machine learning-based AI applications. It comprises the majority of Google’s production AI services and supports the implementation of neural networks. Due to its capacity to parallelize workloads and scale quickly, TensorFlow is frequently employed by many AI practitioners. It has strong Google support and a vibrant developer community.

SciKit-Learn

Another Python module that manages the data, SciKit-Learn, is a crucial component of the AI workflow. Functions for classification, model selection, and data pre-processing are available in SciKit-Learn. Applications involving data mining and analysis frequently use it. Data management and organization are handled using this open-source framework in a way that makes it simple for the algorithm to consume.

Pybrain

A modular library designed for AI newcomers is called Pybrain, which stands for Python-Based Reinforcement Learning, Artificial Intelligence, and Neural Network Library. It includes Python-compatible neural network and reinforcement learning algorithms that are easy to combine. It is also frequently used to train and implement popular AI algorithms quickly.

Python is used by many organizations, including Google, NASA, Amazon, Instagram, Reddit, JP Morgan Chase, Intel, IBM, Netflix, Facebook, and Pinterest.

Java

Java is a crucial language for AI, as should be obvious. The language’s widespread use in the creation of mobile apps is one explanation for this. And it makes great sense given how many mobile apps use AI. It is one of the oldest answers to the question of “What programming language for artificial intelligence is the best?”

Because of its “write once, run anywhere” programming philosophy, Java, which was initially introduced more than 20 years ago in 1995, is employed by AI programmers. Java is meant to have the fewest dependencies possible, so it can be run on any platform with little effort.

The most significant benefits of Java as an AI language include its ease of use, quick debugging, portability, and autonomous memory manager. Swing and SWT have been integrated into Java, which gives the interfaces and graphics a sleek, contemporary appearance.

Along with supporting TensorFlow, Java also provides other libraries and frameworks made especially for AI:

• Deep Java Library
• Kubeflow
• OpenNLP
• Java Machine Learning Library
• Neuroph

JavaScript’s AI features enable seamless interaction and operation with other source codes, including HTML and CSS. Like Java, JavaScript has a sizable developer community that aids in development. AI programming is made easier by libraries like jQuery, React.js, and Underscore.js. JavaScript can control front and backend operations, including multimedia, buttons, and data storage.

The FaceApp and the practical Google Assistant are two instances of Android apps featuring Java-based artificial intelligence.

R

Data science, a field that heavily relies on AI, frequently uses the computer language R. Data science is the discipline of processing and analyzing data with the aid of AI, utilizing statistics and math, in order to identify trends. Data transformation, preparation, and analysis are just a few of the data science tasks that the software’s libraries can be used for.

Most of R’s advantages come from its ability to process statistical data. They comprise time-series analysis, grouping, visualization, and linear and nonlinear modeling. It is a wonderful option for creating machine learning algorithms since it can store and access data efficiently.

Beginners in programming or AI shouldn’t use R because of its challenging learning curve. The benefits of learning R are substantial in business environments where huge amounts of data are handled.

LISP

Now, LISP deserves a lot of the credit for what we see from AI. It is the second-oldest language overall and the oldest language still in use for AI processes. With its expanded scope for transforming ideas into reality, LISP has traditionally been regarded as a tool for top AI businesses. By emphasizing precision, the language sets itself apart from existing AI languages.

Who are the precursors of artificial intelligence?

What was the first AI programming language called?

LISP is the answer. The 1958 invention known as Lisp takes its name from one of its initial implementations, “List Processing.” By 1962, Lisp had developed enough to meet the demands of artificial intelligence.

However, because of its numerous shortcomings, LISP language use is declining. The fact that the language is still utilized for logical solutions and is well-known for achieving miracles adds it to the list.

Scala

In addition to using object-oriented programming, Scala is a functional programming language. The building of websites and web-based applications, as well as data processing, make use of one of the industry’s most succinct high-level programming languages. With a very difficult syntax, this popular programming language is incredibly adaptable.

Scala is scalable, as its name suggests. It is a strong choice for AI engineers because it supports parallelism and has several excellent artificial intelligence libraries. Scaladex, a database of all Scala libraries, including those for artificial intelligence, will be available once you have learned Scala for AI.

JULIA

While Julia lacks a sizable user base and community, it provides a wealth of premium tools for creating superior AI. Julia is one of the best development tools for handling data analysis and numbers.

Julia gives you the necessary capabilities for flawless execution if you need to create a dynamic interface, eye-catching graphics, or data visualizations. This language develops AI simply because of its debugging, memory management, and metaprogramming features.

Julia is the finest option for AI tasks using machine learning. It includes a variety of packages, including Flux.JL, Turing.JL, MLJ.JL, and Metahead.JL.

Haskell

Based on the semantics of the Miranda programming language, Haskell is a functional programming language. Haskell provides safety and speed over all else in scenarios involving machine learning. Haskell has found a niche in academia due to its support for embedded, domain-specific languages that are essential to AI research; however, tech behemoths like Microsoft and Facebook have enlisted Haskell to create frameworks that manage schematized data and combat malware, respectively.

The HLearn library in Haskell provides deep learning support through its Tensorflow binding and algorithmic implementations for machine learning. Haskell is the best language for projects involving abstract math and probabilistic programming because it enables users to design extremely expressive algorithms without losing efficiency.

Users of Haskell can interpret their code like mathematical equations and utilize a small amount of code to express a model.

C++ 

Although it is low-level and has been around for a while, C++ is still widely used. It is one of the best answers to the question of “What programming language for artificial intelligence is the best?”

This indicates that C++ performs well with hardware and machines but less so with software’s more theoretical aspects.

However, C++’s ability for low-level programming makes it ideal for managing production AI models.

Using C++, you can create neural networks from scratch and convert human code into something that computers can understand.

C++ was used to develop several of the most infamous machine learning libraries.

These factors contribute to the continued popularity of C++ in the artificial intelligence community. Don’t undervalue what C++ is capable of.

Prolog

Look at Prolog for a more logical technique to program an AI system. Instead of following a series of coded instructions, software that uses it adheres to a fundamental set of facts, rules, goals, and questions.

Prolog has the ability to recognize patterns and match them, locate and organize data logically, and automatically go backward in a process to discover a better route. The strongest application for this language in AI is problem-solving, where Prolog looks for a solution—or several—to the situation.

The usage of it in chatbots and virtual helpers like IBM’s Watson is the result. Consider how straightforward but useful these clever communication methods are. Although Prolog may not be as flexible or user-friendly as Python or Java, it can be of great use.

GO

Go is an open-source programming language that makes it simple to create trustworthy, effective, and efficient software. It is a recent arrival in the field of programming. It is one of the newest answers to the question of “What programming language for artificial intelligence is the best?”

In an era of multicore processors, computer networks, and big codebases, Google adopted Go in 2012 after it was first conceptualized in 2007. Go was designed to increase programming productivity. The goal of the designers was to address common complaints about other languages while maintaining many of their beneficial traits.

To assist you in creating scalable applications, GO combines the performance of classic C++ & Java with all the simplicity of Python.

This language has given a new generation of coding geeks a lot of optimism. It is new and lags behind Python, JS, and Java programs. However, it is steadily rising to the top of the list of languages that can be learned and used soon.

RUST

High-performance, quick, and secure software development is what everyone wants, and Rust makes it possible. Developers adore using it as a general-purpose programming language for the creation of AI. Rust’s syntax is comparable to C++’s, but Rust also provides memory safety and forgoes garbage collection.

Many well-known systems, like Dropbox, Yelp, Firefox, Azure, Polkadot, Cloudflare, npm, Discord, etc., use Rust as their backend programming language. Rust is the ideal language for developing AI and using it in scientific computing because of its memory safety, speed, and ease of expression.

How to choose an artificial intelligence programming language?

Outline the objectives and outputs of your project before choosing an AI language. Determine the resources needed to finish the activities at hand by evaluating the tasks at hand. Consider each AI programming language’s accompanying tools and libraries before deciding which is most appropriate for your project.

You should start the process of integrating one or more of these languages if your business needs to integrate artificial intelligence. There is no end to what AI can achieve to speed up your company’s growth with the correct development team.

For beginners, popular mainstream languages like Python, Java, and C++ are frequently good options. However, you should consider each language’s specific benefits and drawbacks in light of your objectives.

Artificial intelligence programming for beginners

If you want to work as an AI engineer, the first computer language you should learn is Python. You can start learning other programming languages once you have learned Python and its uses in AI development. The most important programming language for AI developers is Python, and most of them never learn any other languages during their careers.

The majority of these resources are open source, so anyone can use them for nothing. You may install Python packages straight on your computer for little to no money, and there are a ton of online forums where you can get instructional materials. The best method to learn Python is through bootcamps.

Conclusion

“What programming language for artificial intelligence is the best?” is a hard question. But thanks to many libraries and easily accessible instructional resources, AI programming is now more accessible than ever. A beginner’s introduction to AI programming is made even simpler by the abundance of online training resources for well-known languages like Python and Java.

The best programming languages for artificial intelligence projects are those that were covered above. It just comes down to selecting a project that best meets your needs. With a basic grasp of the project, you may quickly select the best language and increase your company’s efficiency.

Something more than a hype, artificial intelligence is here to stay. AI is present in everything, from autonomous driving to phrase correction. This popularity has created an excellent environment for businesses trying to generate additional AI improvements like artificial intelligence customer services.

Python is one of the most popular programming languages worldwide. It often ranks high in surveys: for instance, it claimed the first spot in the Popularity of Programming Language index and came second in the TIOBE index. The chief focus of Python was never web development. However, a few years ago, software engineers realized the potential Python held for this particular purpose and the language experienced a massive surge in popularity. But data engineers couldn’t do their job without Python, either.

Since they have a heavy reliance on the programming language, it’s as important now as ever to discuss how using Python can make data engineers’ workload more manageable and efficient. 

Cloud platform providers use Python for implementing and controlling their services

Run-of-the-mill challenges that face data engineers are not dissimilar to the ones that data scientists experience. Processing data in its many forms is a key focus of attention for both of these professions. However, from the data engineering perspective, we concentrate more on the industrial processes, such as ETL (extract-transform-load) jobs and data pipelines. They have to be strongly built, dependable, and fit for use. 

The serverless computing principle allows for triggering data ETL processes on demand. Thereafter, physical processing infrastructure can be shared by the users. This will allow them to enhance the costs and consequently, reduce the management overhead to its bare minimum.

Python is supported by the serverless computing services of prominent platforms, including AWS Lambda Functions, Azure Functions, and GCP Cloud Functions.

Parallel computing is, in turn, needed for the more ‘heavy duty’ ETL tasks relating to issues concerning big data. Splitting the transformation workflows among multiple worker nodes is essentially the only feasible way memory-wise and time-wise to accomplish the goal.

A Python wrapper for the Spark engine named ‘PySpark’ is ideal as it is supported by AWS Elastic MapReduce (EMR), Dataproc for GCP, and HDInsight. As far as controlling and managing the resources in the cloud is concerned, appropriate Application Programming Interfaces (APIs) are exposed for each platform. Application Programming Interfaces (APIs) are used when carrying out job triggering or data retrieval. 

Python is consequently used across all cloud computing platforms. The language is useful when performing a data engineer’s job to set up data pipelines along with ETL jobs to recover data from various sources (ingestion), process/aggregate them (transformation), and conclusively allow them to become available for end-users.

Using Python for data ingestion 

Business data originates from a number of sources such as databases (both SQL and noSQL), flat files (for example, CSVs), other files used by companies (for example, spreadsheets), external systems, web documents, and APIs.

The wide acceptance of Python as a programming language results in a wealth of libraries and modules. One particularly fascinating library is Pandas. This is interesting considering it has the ability to enable the reading of data into “DataFrames”. This can take place from a variety of different formats, such as CSVs, TSVs, JSON, XML, HTML, LaTeX, SQL, Microsoft, open spreadsheets, and other binary formats (that are results of different business systems exports).

Pandas is based on other scientific and calculationally optimized packages, offering a rich programming interface with a huge panel of functions necessary to process and transform data reliably and efficiently. AWS Labs maintains an aws-data-wrangler library named “Pandas on AWS” used to maintain well-known DataFrame operations on AWS. 

Using PySpark for Parallel computing 

Apache Spark is an open-source engine used to process large quantities of data that controls the parallel computing principle in a highly efficient and fault-tolerant fashion. Whilst initially implemented in Scala and natively supporting this language, it is now a universally used interface in Python: PySpark supports a majority of Spark’s features, this includes Spark SQL, DataFrame, Streaming, MLlib (Machine Learning), and Spark Core. This makes developing ETL jobs easier for Pandas experts.

All of the aforementioned cloud computing platforms can be used with PySpark: Elastic MapReduce (EMR), Dataproc, and HDInsight for AWS, GCP, and Azure, respectively. 

Moreover, users are able to link their Jupyter Notebook to accompany the development of the distributed processing Python code, for example, with natively supported EMR Notebooks in AWS.

PySpark is a useful platform for remodeling and aggregating large groups of data. As a result, this makes it easier to consume for eventual end-users, including business analysts, for example.

Using Apache Airflow for job scheduling 

By having renowned Python-based tools within on-premise systems, cloud providers are motivated to commercialize them in the form of “managed” services that are, therefore, simple to set up and use.

This is, among others, true for Amazon’s Managed Workflows for Apache Airflow, which was launched in 2020 and facilitated using Airflow in some of the AWS zones (nine at the time of writing). Cloud Composer is a GCP alternative for a managed Airflow service.

Apache Airflow is a Python-based, open-source workflow management tool. It allows users to programmatically author and schedule workflow processing sequences and subsequently keeps track of them with the Airflow user interface.

There are various substitutes for Airflow, for instance, the obvious choices of Prefect and Dagster. Both of which are python-based data workflow orchestrators with UI and can be used to construct, run, and observe the pipelines. They aim to address some of the concerns that some users face when using Airflow.

Strive to reach data engineers’ goals with Python

Python is valued and appreciated in the software community for being intuitive and easy to use. Not only is the programming language innovative, but it is also versatile, and it allows engineers to elevate their services to new heights. Python’s popularity continues to be on the rise for engineers, and the support for it is ever-growing. The simplicity at the heart of the language means engineers will be able to overcome any obstacles along the way and complete jobs to a high standard. 

Python has a prominent community of enthusiasts that work together to better the language. This involves fixing bugs, for instance, and thereby opens up new possibilities for data engineers on a regular basis. 

Any engineering team will operate in a fast-paced, collaborative environment to create products with team members from various backgrounds and roles. Python, with its simple composition, allows developers to work closely on projects with other professionals such as quantitative researchers, analysts, and data engineers.

Python is quickly rising to the forefront as one of the most accepted programming languages in the world. Its use for data engineering, therefore, cannot be underestimated. 

Meet investors, Blockchain and crypto enthusiasts, a talent pool of developers and programmers  as they solve three Blockchain challenges over two days in Berlin. Here is why you should be a part of LongHash Cryptocon Vol2.

Berlin has been recognised as the cryptocurrency capital of Europe for more than half a decade. The city emerged as one of the first in Europe to accept digital currencies back in 2013 and the crypto revolution is now backed by over 100 blockchain companies based in Berlin. Jasmine Zhang, CEO, LongHash Germany, which is organising its second Blockchain Hackathon (part of LongHash Cryptocon Vol2) in the city on May 18-19 this year, rightly puts this in perspective, “Berlin, as many people have commented already, is a great place with infrastructure and talented, international people. We would like to leverage the strength and expertise we have from the East, and bridge with the West to make a positive impact on blockchain ecosystem. Our aim is to further accelerate the understanding and development of blockchain technology globally.”

LongHash is a platform for accelerating the development and understanding of Blockchain technology. LongHash incubators provide a full-range of support for start-ups working on blockchain-related projects.

As an early-stage blockchain investment and incubation firm, Longhash supports its portfolios long-term. Zhang says, “We are hosting different events including hackathons worldwide, like in Germany, Japan, Vietnam since last year to help their ecosystem grow. This edition’s three projects come from U.S, China and Germany with big potential and a healthy, strong developer community is what they are seeking at the moment and this belongs exactly to the post-investment management that LongHash is providing.”  

Back in Berlin:  With more challenges and ETH prizes for developers and Blockchain Geeks

The first edition of the Hackathon was last year during The Longhash Crypto Festival Berlin, which took place between October 26 and October 29 and promoted innovation among programmers, attracting participants from Asia, eastern Europe and the US. And this being the second edition, the competition will be more challenging yet rewarding at the same time. Winners of the second edition of hackathon have an opportunity to win upto 30 ETH equivalent prizes. Here is a look at the categories:

• Cybex Prize: 5 Eth
• MXC Prize: 5 Eth equivalent amount of MXC Token
• Taraxa Prize: 5 Eth

On top of this, one chosen winner will be  awarded Euro 2,000 equivalent amount of VET powered by VeChain and more prizes are to be announced soon!

The challenges have been carefully designed considering the needs of the Blockchain ecosystem and where the innovation is most desired. Here is a look:

Challenge 1: How to implement an Algo Order in Cybex Dex？

Cybex.io is a blockchain based decentralized exchange that supports crypto trading. When a user has an intention to perform a large trade, it is useful to have an algorithm to split the order into smaller slices and trade it over a longer period. This feature is referred to as ‘Algo Order’ and is widely adopted in regular exchanges.

In decentralized exchange, each sliced order must be signed by the user’s private key. This provides a new challenge to algo orders. In order the place orders automatically, while keeping the private key safe, a user typically has to write its own program and run it in its own machine. This makes it difficult for normal users to use algo orders due to the lack of programming skills.

Design a solution that allows a normal user to execute and manage algo orders.  

The following are some basic Algo order types:

• TWAP :http://empirica.io/strategies-catalog/twap
• VWAP :http://empirica.io/strategies-catalog/vwap
• Iceberg:https://www.investopedia.com/terms/i/icebergorder.asp

The solution should be using Cybex API, which is available at the following locations:

• Cybex API server documentation: https://apidoc.cybex.io
• Sample program to place an order:https://github.com/CybexDex/cybex-python

Solutions will be graded on :

• User interface friendliness
• The ideal solution should be easy enough to attract people without programming skills
• Security
• As trading involves using private key, the management and storage of the key is a crucial consideration.
• Framework Coding quality

Challenge 2: How do we automate the Smart Machine Bidding procedure for the LPWAN devices in order to reduce the costs of an IoT network?

MXC foundation focus on connecting Low Power Wide Area Network (LPWAN) technology with the blockchain as an infrastructure for Internet of Things (IoT). MXC automates machine-to-machine (M2M) transactions and provides a device data economy. The pricing policies of data transmissions through gateways in LPWAN are determined by MXC Smart Machine Bidding (SMB). In the SMB, based on the bidding strategies provided by the device owners, and the gateway owners, the payments for using downlink / uplink LPWAN resources will be determined.The following parameters are set by the device in bidding strategies of the SMB:

• max_bid: the maximum bidding price defined by the device owner shows the upper payment threshold of the device (in MXC tokens) for the downlink request.
• max_delay: this parameter defines, under certain circumstances, the maximum acceptable_delay (in seconds) for the packet to be sent. If max_delay is reached, the packet will not be sent and the cloud will notify the client about the rejection of the downlink request.
• accepted_delay: the tolerable delay defined by the client (or device owner) to indicate the time period a packet is willing to wait for the lowest possible price.
• Lowest possible bidding price is the current lowest bid of the available gateways for the device.

Each gateway provides a value on using its resources called min_bid. The device in order to use the gateway downlink resource, should bid at least min_bid value. If multiple devices in a same time wants to use a downlink resource of a gateway, the one which define more max_bid will be the winner. More details about bidding procedure are provided in MXC Smart Machine Bidding white paper (available in the repository stated below and the MXC website). Based on downlink / uplink data flow of the device owners and their requests, MXC cloud can provide data driven automated smart machine bidding.  max_delay parameter is mainly related to the application and the priority of the data which is known by the device owner/client and is defined by the requirement of the provided application by the device.

On the other hand, accepted_delay, and max_bid parameters should be provided by the device owner (or the client) in some way to make a balance between the priority of the related uplink/downlink data and the corresponding data transmission cost. These two parameters (accepted_delay, and max_bid) can be automatically provided for the device owner to make this balance. Your task is to develop an automated solution (e.g. based on Machine learning methods, dynamic algorithms or greedy algorithm) which provides near-optimum value for accepted_delay and max_bid parameters to reduce the total cost of the LPWAN for the user.

In the input file, you will receive max_delay, payment limit which the data owner wants to pay in total (for all of the transactions), and the downlink resource usage history of the device and other devices. Your program (preferably in Go) will be evaluated by output efficiency (based on the test cases of LPWAN data simulation), and solution explanation (provided in your documentation). Note that you can provide multiple solutions and do the implementation as much as you want/can. A sample input file and its details will be provided in the below repository:

https://gitlab.com/mxc-hackathons/smb

Challenge 3: How to implement an anonymous data collection scheme that allows the manufacturer to anonymously collect data from its end devices without knowing exactly which device it came from?

Data privacy and security has become an increasingly urgent concern worldwide. Large corporations cannot simply collect data from its end users without their knowledge or explicit consent. However, it would be nice if a manufacturer could still collect data generated by its devices without user consent, but do so in a way that’s cryptographically-guaranteed to be anonymous. In this scenario, the manufacturer would like to collect data from anonymous devices, but it would want to be sure the data it’s receiving is not garbage and is guaranteed to have come from a device it has manufactured. The end user would not mind that its device’s data is being harvested as long as it is impossible to trace that device’s data directly to the end user’s identity.This problem is broadly defined as direct anonymous attestation, and more narrowly defined as a membership proof. An earlier paper (https://infoscience.epfl.ch/record/128718/files/CCS08.pdf ) had been published with an open-source implementation published ( https://github.com/ing-bank/zkproofs ).

Assumptions :  We assume that the manufacturer has embedded within each device it made with a pair of asymmetric encryption keys, and that hacking the device on-premise to obtain these keys is prohibitively expensive to do. We further assume that the manufacturer is willing disclose the public keys associated with all of its products to the open.

The challenge: HOW to implement an anonymous data collection scheme that allows the manufacturer to anonymously collect data from its end devices without knowing exactly which device it came from?

• A device can prove to the manufacturer that it is indeed one of the devices it has created
• The manufacturer will construct a temporary X.509 certificate so that a proof does not need to be provided every time, temporary because the end user might want to stop even anonymous data collections

Bonus:

• Manufacturer & device both anchor the challenge & proof onto the blockchain
• Device anchors its data transmissions onto the blockchain with the temporary certificate

Does this interest you as well? Apply here for the Hackathon before the 12th of May – the event is free for all developers. Also, there is more. If you are a developer or aspiring entrepreneur in the blockchain/crypto space and  want to know about the investment perspectives from Top Asian & European Funds in the Blockchain segment or business use cases in real word adoption, get your free tickets for Hash Talk which will be an afternoon-long summit focused on discussions and creating insights on investment, business, and tech in blockchain curated and brought by LongHash Germany. More details here.

In a world dominated by big data, it stands to reason that many organizations are eagerly seeking out the best data scientists. With all this high demand, data scientists now have their work cut out for them to truly stand out from what is an increasingly crowded field. While it’s true that finding a data scientist job is not the most difficult task, it’s up to each individual data scientist to prove themselves. That means being the best at what they do and showing why they deserve a highly sought after position. With this in mind, data scientists can help themselves out tremendously by adopting certain skills and talents that will help differentiate them from others in the same career.

Programming

While it might not come as a surprise that data scientists need to know their way around computers, it still needs to be said that programming skills are an absolute must if they want to truly excel. Some data scientists can get by with only rudimentary understanding of programming techniques, mainly devoting themselves to viewing and analyzing the data that is collected, but for data scientists that want to get to the next level, they need to be able to manipulate that data as well. Programming skills give data scientists more control over the data collection and analysis process. At the same time, they can increase their worth by learning and mastering multiple programming languages such as SAS, R, Python, and many others.

Business Skills

While programming skills can certainly be valuable, data scientists can do harm to themselves if they ignore the business aspect of their careers. Every data scientist should take the time to develop business skills depending on the type of company they are working for. This includes gaining detailed knowledge on how the businesses they are in work and what role they play in improving them. Those with business knowledge will be able to work more closely with company executives. They can also show a greater level of dedication and interest in the success of a business. Developing the right blend of business skills often requires extra work, such as attending training sessions and reading up on company material. All that hard work can pay off in the end though.

Communication

Of equal importance is the development of excellent communication skills. Big data analytics can be a complicated and complex concept for the layman to understand, so it’s up to data scientists to possess the communication skills necessary for those within the business to grasp how it can be used and the roles big data can play. Data scientists need to learn how to translate findings from big data analytics into how it meets specific business needs. Many executives won’t care much for the minutiae of analytics tools, and much of the big data jargon will simply fly over their heads. Framing big data in its impact on marketing or sales, though, makes it that much easier to understand. Talking about big data is only part of the equation, however. Data scientists also need to learn how to listen to what the business side is saying and learn how to meet their expectations.

Intellectual Curiosity

Data scientists can also show their increasing value by demonstrating a desire to learn and grow. Intellectual curiosity is always looked upon positively, and this can extend not just to getting to know more about business but about data processes as well. The average data scientist will look at data on the surface and use it, but the data scientist that wishes to stand out will always look deeper into the data, finding new patterns and trends that may be missed by only casual observance. Data scientists seeking to improve this skill will be best served by keeping an open mind about big data analytics and always going one step further than might be required.

When all is said and done, data scientists that take the time to develop added skills and talents will find themselves head and shoulders above their peers. Some data scientists may even become tech experts within their own companies, with people coming to them with questions ranging from what is flash storage to the finer details of big data analytics and cloud computing.

Right now, data scientists find themselves in an envious position where their skills are in high demand while their numbers are in low supply. It won’t be this way forever, though, so they need to work now to gain the skills that put them at a different level than the rest.

Rick Delgado- I’ve been blessed to have a successful career and have recently taken a step back to pursue my passion of freelance writing. I love to write about new technologies and keeping ourselves secure in a changing digital landscape. I occasionally write articles for several companies, including Dell.

(image credt: Davidlohr Bueso)

Both the TIOBE Index and PYPL (PopularitY of Programming Language) Index for October 2014 are out and the ranking that has been making heads turn is Google’s Dart – for its apparent jump and not. Touted as the successor to JavaScript, Dart has moved up from last year’s 81 to 17 on this year’s TIOBE index.

TIOBE notes that after its release in late 2011 engineers were sceptical since browsers other than Chrome did not support Dart. “And they were right. But now that the Dart to JavaScript compiler is mature and claims to generate even faster code than hand-written JavaScript, the Dart language seems to have a bright future. It is interesting to note that at the same time that Dart enters the top 20, JavaScript is losing some positions,” writes TIOBE. Other competitors of Dart such as CoffeeScript (position 133) and TypeScript (position 122) are yet to move up.

However, Dart didnt fare such rise in PYPL. PYPL remarked that “Tiobe criteria for the search “Language + programming” is not particularly meaningful,” reports Jaxenter.

As Jaxenter points out regarding Dart, “Whether this actually emerges as a trend, remains to be seen.”

TIOBE rankings are based on the number of skilled engineers world-wide, courses and third party vendors. Popular search engines such as Google, Bing, Yahoo!, Wikipedia, Amazon, YouTube and Baidu are used to calculate the ratings. It is important to note that the TIOBE index is not about the best programming language or the language in which most lines of code have been written, explains TIOBE.

PYPL on the other hand analyzes how often language tutorials are searched on Google : the more a specific language tutorial is searched, the more popular the language is assumed to be. The raw data comes from Google Trends, so that anyone can verify it, or make the analysis for his own country or other languages.

Swift, the language from Apple has impressed too, with a 19th position on TIOBE and an 11th position on PYPL. JAVA holds a steady number one and two position in PYPL and TIOBE respectively while Javascript holds a 7th and 12th.

Read more here

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(Image Credit: Michael Himbeault)

The use of statistical language tools like R, SAS and SPSS is growing at unprecedented rates. Forums are full of questions about which program to learn, is one easier than the other, how popular are they, etc. However, what is the current state of these programming languages? – what companies are using them? How easy is R to learn in comparison to SPSS or SAS? How marketable are they?

The infographic below gives a nice overview of the these questions, comparing R, SAS and SPSS against one another.

(Image Credit: Tim Lucas)