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Everything You Need to Know About Machine Learning

A calculator can solve complex problems which would take even the most savvy mathematicians an incomparable amount of time. Artificial intelligence has become one of the most hotly debated and highly funded aspects of technology because the speed at which machines can process information yields innumerable possibilities and applications which can and will benefit humanity. One of the first popular incarnations of AI is Machine Learning.

Machine Learning is the ability for a computer to learn without being explicitly programmed. Machine learning focuses on computer programs which can identify patterns and create its own algorithms when exposed to new data. It is used in self-driving cars, in newsfeed algorithms on social media, in evaluating job candidates, in recognizing faces on your phone, and more.

The most powerful form of machine learning currently active is called “deep learning”. “Deep learning” builds a complex mathematical structure known as a neural network out of vast quantities of data. Machine learning’s ability to handle mass amounts of data makes it crucial to the advancement of IoT. The IoT collects enormous amounts of data which require computers with machine learning to recognize patterns and create algorithms.  In self-driving cars, IoT cameras and sensors in each autonomous vehicle absorb their surroundings and turn them into huge amounts of data. The data is then sent to the cloud where it is accessible to all autonomous vehicles on the road. Thus, when one self-driving car makes a mistake, all of them learn. In conjunction with the Internet of Things, machine learning will be vital to the building of a smartworld.


Machine learning requires a great deal of statistical analysis; it demands an intelligent programming language which can process a number of complex issues and general paradigms.

R: Considered a statistical workhorse, R has emerged as one of the top programming languages for machine learning. R is intended for advanced users because of its complex nature and wide learning curve.

Python: A rising star for machine learning, Python is a data science book which has been in use in the manufacturing industry for awhile. Python gives users direct access to predictive analytics, making it the foremost data science language. Developers turn to Python when they are looking to frame better questions or expand the capabilities of their existing machine learning systems.

MATLAB/Octave: Millions of engineers are already using MATLAB, a matrix-based language, to analyze and develop cutting edge systems. MATLAB has emerged as the simplest way to demonstrate computational mathematics.


Machine learning laid much of the groundwork for the biggest upgrade in iOS 10. It is very difficult for computers to comprehend the intricacies of the human language. Machine learning has enabled iPhones to sense contextual clues with increasing confidence, improving iMessage’s ability to autocorrect and for Siri to understand the particulars of your vernacular. In the iPhone 7 camera, machine learning allows the device to separate the background from the foreground to achieve amazing portraits once possible only with DSLR cameras.


Google is among the dominant forces in machine learning. Much of Google Search’s prominence is owed to advances in the machine learning field. In November 2015, Google released TensorFlow, an open-source software library for machine intelligence. TensorFlow effectively simulates “deep learning” neural networks across different computer hardware and offers a straightforward way for users to train computers to perform tasks by feeding them large amounts of data.

Google uses Tensorflow in many of their internal processes, including RankBrain for information retrieval, image classification, SmartReply, and more.


Now that mobile devices have the high productive capacity level to perform tasks to the same degree as a traditional computer, the question of what machine learning can offer apps has arisen. Large retailers like Amazon and eBay use machine learning in their mobile apps to improve customer experience with smarter product search and recommendation features, along with the ability to forecast buying trends with analytics.

While Machine Learning algorithms require a high level of programming experience and a ton of data to be effective, integrating apps with Siri & iMessage for iOS 10 allows developers to take advantage of the vast deep learning neural networks embedded into Apple’s 1st-party apps.

While the future of machine learning  on a commercial level remains to be seen outside of tech titans like Facebook, machine learning algorithms will be crucial in conjunction with the IoT in building a new SmartWorld with unparalleled predictive capabilities.

Get Fluent in IoT: Top Programming Languages for the Internet of Things

As we explored in our previous blog, the Internet of Things is shaping our future. With Internet of Things development on the rise and potentially $11.1 trillion in economic value generated per year due to IoT, many companies are creating strategies to develop for the platform.

To all the decision-makers out there looking to develop for the loT platform, getting familiar with the programming languages and how they relate to the platform will have a major impact on the budget and quality of any given IoT project. IEEE, the largest technical professional organization dedicated to advancing technology for human benefit, recently ranked the top programming languages of 2015. Bearing in mind embedded devices present their own programming difficulties, here are the top programming languages for the IoT:

Java: James Gosling, Mike Sheridan, and Patrick Naughton began developing the Java language project in June 1991. Java has become the most popular programming language and many choose Java when developing for IoT. Java is an object-oriented language designed for portability. With few hardware dependencies, Java is a great choice from an economic standpoint. Java code can be transmitted to multiple platforms and hardware-support libraries give Java developers the ability to control specific pieces of hardware. Developing for Java can be deterred by the hardware-support libraries available for control functions.

Python: In December 1989, implementation of Python began. Designed by Guido van Rossum, Python is a multi-paradigm programming language which has become one of the go-to languages for web developers. Python’s flexibility and emphasis on readability have caused it to rise in the ranks of top languages used for embedded control and IoT. Readability increases workflow as programmers who have attempted to decipher other programmer’s optimized C code would know.

C: With development beginning in 1972 on the PDP-11 Unix system, C is one of the most popular programming languages. C has influenced many languages, including C++, Go, Java, JavaScript, & Python. Due to its long history, C functions as a common language for many software developers. C’s popularity and lack of built-in hardware bias toward a graphical interface make it a good choice for IoT development.

C++: Created in 1979 by Danish computer scientist Bjarne Stroustrup, C++ was designed as an object-oriented pre-processor for C, keeping the spare nature of the language but adding data abstraction, classes and objects. C++ is commonly used to write embedded and IoT code for Linux systems.

Assembler: Assembler is the simplest method intended to keep projects as compact as possible. Assembler is a low-level language which maintains a high correspondence between language and the hardware’s machine code instructions. Assembler minimizes overhead, making a popular choice despite how it doesn’t allow a safety net. Silly mistakes are easy to make and some hardcore programmers may be frustrated by its simplicity.

Go: Announced by Google in 2009, Go is an open-source, embedded-specific programming language gaining traction in the IoT world. Go supports concurrent input, output, and process different channels, an asset to gathering data from and sending data to separate sensors. Go was created in the tradition of C, but with specific changes to make it simpler, safer & more concise.

ParaSail: ParaSail was created in 2009 as an embedded-specific language. ParaSail stands for Parallel Specification and Implementation Language. ParaSail was created to support safe, secure, highly parallel applications which can be mapped to multicore, many core, heterogenous, or distributed architecture.

Choosing the right programming language will have a major impact on the budget and functionality of any IoT project. Doing the proper research on the subject will pay off in the long run. Stay tuned for more blogs on this subject and learn more about best IoT development practices via this awesome article by InformationWeek.