One of the hottest trends across all industries today, machine learning is driving an explosion in the capabilities of artificial intelligence. Gartner, in its most recent Hype Cycle for Emerging Technology, put machine learning at the peak. The firm predicted that by 2020, artificial intelligence technologies, including machine learning, will be virtually pervasive in almost every new software product and service.

Moreover, according to the analysts at IDC, machine learning will continue to grow at more than 50% rate per year through 2020, when the total AI revenue could top $46 million. Research director of cognitive systems and content analytics at IDC, David Schubmehl, said, “since AI/ Cognitive systems are becoming a crucial element of enterprises and IT infrastructures, companies need to understand their importance and plan to adopt them and make use of these technologies as much as possible.”

All that being said, what exactly is machine learning? Does it deserve all the hype it is receiving? What is its relation to AI and what technologies should you know about to make the most of machine learning?

Here are the above and beyond answers to all the questions, you have related to machine learning!

What is machine learning & why it matters

When it comes to the history, the first person ever to use the phrase “machine learning” was likely Arthur Samuel, who developed one of the first computer programs for playing checkers. Back in 1959, he defined machine learning as the technology that enables computers to learn with being programmed. The definitions given for machine learning by other computers scientists are mathematical in nature but the one given by Samuel’ still remains to be easiest to understand.

Machines learning is a big part of AI more like a subfield. The goal of machine learning generally is to understand the structure of data and fit that data into models that can be understood and utilized by people.

In spite of machine learning being a part of computer science, it does differ from traditional computing. Algorithms are sets of programmed instructions used by computers to solve problems in traditional computing. While in machine learning, algorithms allow computers to rely on data inputs and use statistical analysis to get output values falling in a specific range. As a result, machine learning enables computers to build sample data and models for automated decision-making processes on the basis of data inputs.

Machine learning examples for beginners

Machine learning can achieve some pretty impressive feats in AI, but it’s also responsible for simpler, but still incredibly useful applications.

The most popular presentation of machine learning would be the “spam” filter that email systems use to differentiate between junk and useful emails. In such cases, the filters include rules put in by programmers, which then allows adding numbers which give a good indication if the software considers the email good for it to be visible to you.

The problem is that rules are subjective. A rule that filters out emails with a low ratio of the image to text isn’t so useful if you’re a graphic designer, who is likely to receive lots of useful emails that meet these parameters. This is the reason why machine learning allows each user to adapt to the software based on each of their requirements. When a user responds to spam emails, it triggers the AI agent to deal with such emails. 

Apple’s Siri, Amazon’s Alexa, the Google Assistant, and Microsoft Cortana are the obvious demonstrations of the growing power of machine learning. All of these primarily rely on machine learning for various factors like the ability to understand natural language, voice recognition and the requirement to respond immediately to raised questions. 

Additionally, any technology user today has benefitted from machine learning.

Such as all the social media platforms benefit majorly from facial recognition technology.   

Facial recognition technology allows social media platforms to help users tag and share photos of friends. Optical character recognition (OCR) technology converts images of text into movable type. Recommendation engines, powered by machine learning, suggest what movies or television shows to watch next based on user preferences. Self-driving cars that rely on machine learning to navigate may soon be available to consumers.

Introduction to the different types of machine learning

Machine learning classifies tasks into various categories. These categories depend on upon the receiving action of learning or even how feedback is given to the systems developed.  

There are primarily two majorly adopted machine learning methods, that are, supervised learning and unsupervised learning. The former method trains algorithms based on example input and output data that is labeled by humans while the latter one provides the algorithm with no labeled data in order to allow it to find structure within its input data. But, besides these types there are other categories involved. Listed below is a detailed introduction to each of its types. 

Supervised Learning

This type requires a programmer or teacher who offers examples of which inputs line up with which outputs. For example, if you wanted to use supervised learning to teach a computer to recognize pictures of cats, you would provide it with a whole bunch of images, some which were labeled as “cats” and some of which were labeled as “not cats.” For the system to identify cats in images, the machine learning algorithms will assist the system learn to generalize concepts.

Unsupervised Learning

 The learning algorithm is left to find commonalities among its input data as the data is unlabelled in unsupervised learning. As unlabelled data are more abundant than labeled data, machine learning methods that facilitate unsupervised learning are particularly valuable.

Discovering hidden patterns within a dataset is the actual goal of unsupervised learning. Along with that, it may also aim of feature learning which will lead to the automatic discovery of representation needed to classify raw data by computational machine. 

Unsupervised learning methods have the ability to recognize complex data that is more expansive and seemingly unrelated. This makes it possible  to organize it in potentially meaningful ways. anomaly detection is usually done by unsupervised learning. For example, fraudulent credit card purchases, recommender systems, etc. As an input for algorithm, untagged photos of dogs can be used to classify dog photos together and find likenesses for them. 

Semi-Supervised Learning

The importance of huge sets of labeled data for training machine-learning systems may diminish over time, due to the rise of semi-supervised learning.

As the name suggests, the approach mixes supervised and unsupervised learning. The technique relies upon using a small amount of labeled data and a large amount of unlabeled data to train systems. The labeled data is used to partially train a machine-learning model, and then that partially trained model is used to label the unlabelled data, a process called pseudo-labeling. The model is then trained on the resulting mix of the labeled and pseudo-labeled data.

The viability of semi-supervised learning has been boosted recently by Generative Adversarial Networks ( GANs), machine-learning systems that can use labeled data to generate completely new data, for example using existing images to create Pokemon images to help train a machine learning model.

Reinforcement Learning

This involves a system receiving feedback analogous to punishments and rewards. A gambler sitting in front of a row of slot machines is the most relevant example of reinforcement learning which is also applicable to machine learning. 

At first, the gambler doesn’t know which slots will pay off or how well, so he tries them all. Over time, he discovers that some of the machines are set “looser,” so that they pay off more frequently and in higher amounts. With time, the gambler or to put it correctly, the computer program increases his earnings by playing the loser machines more often than not.

The Difference Between AI & Machine Learning

Machine learning may have enjoyed the enormous success of late, but it is just one method for achieving artificial intelligence.

AI and Machine Learning are related to each other but are not interchangeable. You could have AI without machine learning but AI machines would take forever to develop and cost an enormous amount of time and money. Machines can learn on their own and complete tasks with the assistance of machine learning. In order for AIs to rapidly develop and accurately complete tasks, the machine must be able to learn for any and all situations.

The easiest way to think about the difference between AI and machine learning is that AI is the broader concept of machines with the capability of completing tasks in a human-like manner. Machine learning enables machines to access and interpret data on their own.

The Role of Neural Networks in Machine Learning

Neural networks are a very important group of algorithms for both, supervised as well as unsupervised learning.  While simple models like linear regression used can be used to make predictions based on a small number of data features,  neural networks prove to be useful when dealing with large sets of data with many features. 

Neural networks have a structure that is inspired by brain. There are neurons which are interconnected layers of algorithms that feed data into each other. In these networks, output of the preceding layer is the  input of the subsequent layer. The recognition of each layer can be done as different features of the overall data.

There are different types of neural networks. The designs of these networks is also changing. Recently researchers have developed an efficient design for an effective type of  deep neural network called long short-term memory or LSTM. This network can operate fast enough to be used in on-demand systems like Google Translate. Besides, there are the usual recurrent neural network well suited to language processing and speech recognition and convolutional neural networks that are commonly used in image recognition

SEE ALSO: DeepMind AI can Detect 50 Types of Eye Diseases By Viewing Scans

The Most Popular Machine Learning Languages

While not used for consumers, if you are looking to take up a course in machine learnings, you will have to learn the various languages associated with this phenomenon. Here are some of the popular languages you’d come across:


The Python machine learning language is no less than a data science book used to bring production systems into operations in the manufacturing industry. Python is known to the world’s foremost data science language as it gives users direct access to predictive analytics. Developers prefer Python over other machines languages as it helps them frame better questions as well as expand their capabilities of their existing machine learning systems.

Python is a comprehensive language that covers a range of libraries, including those of Teano, Keras and sci-kit-learn. It has certain features that allows users to find answers to complicated issues as well as useful tips from opinion analysis to neural networks and more. The scientific popularity of Python is only increasing and its user-friendliness only adds to its appeal. Python is also a useful communication tool that takes us one step closer to a future of reproducibility. On the flip side, Python reduces productivity because it is fragmented than other machine languages. 

Java Family

Machine learning is a good example of how good design and user-centric features can automate sequences. It is a sequence of complex algorithms.The C-family allows users to customize implementations of project-specific algorithms by providing them with a robust library. 

The Java/C-family machine learning language is a gift to more experienced developers who have the time to make minor tweaks with the assistance of comprehensive libraries. This is one of the main reasons of machine learning algorithms being written in Java. This is a functional programming language which will develop machine learning systems with precision, speed and accuracy. 


This is an open-source programming language used primarily for statistical computing. Many in academia favor this and has gain a lot of popularity in recent years. R is not typically used in industrial production environments but has risen in industrial applications due to increased interest in data science. Popular packages for machine learning in R is Classification And REgression Training used for creating predictive models, randomForest for classification and regression.  E1071 includes functions for statistics and probability theory.


C++ language of choice for machine learning and artificial intelligence in game or robot applications (including robot locomotion). C++ or C have a high level of proficiency and control in the language which is why it is most preferred by embedded computing hardware developers asc well as electronics engineers.  The modular and open-source Shark, scalable mlpack and  Dlib offering wide-ranging machine learning algorithms are some of the  machine learning libraries you can use with C++ .

Machine learning Tools | iTMunch

Machine Learning Tools to Make Your Software Intelligent

Tech giants like Apple, Google, Microsoft,  Facebook are actively investing in the democratization of artificial intelligence. Lately these companies have open-sourced many AI/ML libraries as well as tools. They have also offering these solutions as a part of their commercial offerings and cloud services. Listed below are some of tools to use to make your software intelligent!

TensorFlow Object Detection API

TensorFlow has been integrated with a new feature named Object Detection API. 

The API provides a convenient way for ML developers and researchers to identify objects in images using optimized computer vision algorithms developed at Google. Object Detection API functionality comes with the MobileNets single shot detector optimized to run on mobile devices. Designed for the limited computational and power resources of smartphones, MobileNets makes it easier for mobile developers to integrate ML functionality into their mobile applications. If you want to use AI/ML functionality in your desktop software, Object Detection API provides a heavy-duty Inception-based CNN (Convolutional Neural Network) that is optimized for heavy data processing. In both cases, with Object Detection API, it becomes easier to integrate image recognition functionality into your software thus proving to be  a great alternative to using cloud-based ML services.

Google’s Cloud Video Intelligence API

Video Intelligence API is part of Google Cloud Platform (GCP) ML services along with Google Natural Language API and Google Speech API.Video Intelligence API a suit of REST API helps users to make videos searchable as well as identify objects in videos. 

This functionality can be used to detect changes in scenes and objects and identify contexts to power video marketing, introduce interactivity into video content, detect pornographic content in the video streaming apps or social networks as well as  label videos to generate meta-information. There is no need to download any library or software for Video Intelligence API  since it is a REST service. There is only a registration required on the Google Cloud Platform.  

Apple’s Core ML

In June 2017 Apple released its Core ML API with the aim to make AI faster on its iPad, iPhone,as well as Apple Watch products. The API covers all sorts of ML operations such as image and face recognition, object detection, NLP (natural language processing) and NLG (natural language generation). Core ML supports popular ML tools and models, including neural networks (deep, convolutional, recurrent), linear models and decision trees. It is possible to integrate it into an Xcode development environment and become a part of your iOS app functionality. By making pre-trained ML models available for iOS developers, Apple’s Core ML promises to increase the scope of iOS applications with core AI/ML functionality available to users of Apple products. In addition, since Core ML is designed for on-device processing, it secures the privacy of user data and ensures that your app is running even if a network connection is broken. Core ML strongly establishes AI/ML as a part of Apple’s ecosystem while giving an efficient efficient on-device performance that saves memory and power consumption.

Machine learning is a continuously developing field. Because of this, it is best to have preconceived considerations if you plan to work with machine learning methodologies or even analyze the impact of machine learning processes.

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