The new technologies like Machine Learning, Internet of Things, Deep Learning, NLP, Artificial Intelligence, Cloud, Big data and Predictive analytics are having a massive impact in India. While plenty of jobs are being created in these fields, these new technologies are also taking away the traditional and boring human jobs. So, it’s quite important for the new generation to understand the new technologies, terms, and be aware of the required skills to get jobs in the future. This post is a Beginners Guide to Machine Learning, Artificial Intelligence, Internet of Things (IoT), Natural Language Processing (NLP), Deep Learning, Big Data Analytics and Blockchain. Additionally, I have also listed some of the Best Online Courses and Master’s Programs (US & Canada) for Data Science, Machine Learning, Statistics, IoT, and Big Data Analytics.

Beginners Guide to Machine Learning, Artificial Intelligence, Internet of Things (IoT), Natural Language Processing (NLP), Deep Learning, Big Data Analytics, and Blockchain

What is Machine Learning?

Machine learning is a field of study that applies the principles of computer science and statistics to create statistical models, which are used for future predictions (based on past data or Big Data) and identifying (discovering) patterns in data. Machine learning is itself a type of artificial intelligence that allows software applications to become more accurate in predicting outcomes without being explicitly programmed.

The basic objective of machine learning is to build algorithms that can receive input data and use statistics for prediction of an output value within an acceptable range. It provides the ability to automatically obtain deep insights, recognize unknown patterns, and create high performing predictive models from data, all without requiring explicit programming. Machine learning can be applied to detect fraudulent credit card transactions or to predict the pricing.

Machine learning algorithms can be categorized as being supervised, semi-supervised or unsupervised. Supervised algorithms require humans to provide feedback about the accuracy of predictions along with input and desired output. Unsupervised algorithms do not need any training or human involvement. They use an iterative approach called deep learning (explained later in this post) to review data and making conclusions. Know the top 10 contemporary machine learning algorithms of importance that every engineer should understand.

Best Online Courses for Machine Learning:

Certificate Course in Machine Learning – Stanford University

Machine Learning Specialization – University of Washington

Algorithms Specialization – Stanford University

Python for Data Science and Machine Learning – Udemy

Data Science and Machine Learning with Python: Hands On – Udemy

What is Artificial Intelligence (AI)?

Artificial intelligence is the field of study by which a computer (and its systems) develop the ability for successfully accomplishing complex tasks that usually require human intelligence such as visual perception, speech recognition, decision-making, and translation between languages. In other words, artificial intelligence is concerned with solving tasks that are easy for humans but hard for computers.

While artificial intelligence typically concentrates on programming computers to make decisions, machine learning emphasizes on making predictions about the future. If you use an intelligent program that involves human-like behavior, it can be artificial intelligence. However, if the parameters are not automatically learned (or derived) from data, it’s not machine learning.

As per Bernard Marr, AI and ML are often seemed to be used interchangeably. But, they are not quite the same. Artificial Intelligence is the broader concept of machines being able to carry out tasks in a way that we would consider “smart”. Whereas, Machine Learning is a current application of AI based on the idea that we should really just be able to give machines access to data and let them learn for themselves. Know more about the difference between artificial intelligence and machine learning.

What is Natural Language Processing (NLP)?

One of the core goals of artificial intelligence is natural language processing (NLP). NLP is a field of computer science that is at the intersection of artificial intelligence and computational linguistics. NLP deals with programming computers to process large natural language corpora. In simple words, NLP involves intelligent analysis of written language.

For example, you have got a lot of data written in plain text. NLP techniques can reveal the insights from it for you. These insights typically include sentiment analysis, information extraction, information retrieval, search etc. NLP usually deal with research papers, blogs, social media feed text messages (including smileys); it doesn’t deal with images.

What is Deep Learning?

Deep learning is another aspect of artificial intelligence that is concerned with matching the learning approach used by humans to gain certain types of knowledge. In other words, deep learning is a way to automate predictive analytics. Unlike NLP, Deep Learning algorithms do not exclusively deal with text. Deep learning involves mathematical modeling, which can be thought of as a composition of simple blocks of a certain type, and where some of these blocks can be adjusted to better predict the final outcome.

Read: Demystifying Neural Networks, Deep Learning, Machine Learning and AI

The word “deep” means that the composition has many of these blocks stacked on top of each other – in a hierarchy of increasing complexity. The output gets generated via something called Backpropagation inside of a larger process called Gradient descent which lets you change the parameters in a way that improves your model. Know more about the differences between AI, Machine Learning, NLP and Deep Learning.

Let’s go a little deep now. Traditional machine learning algorithms are linear. Deep learning algorithms are stacked in a hierarchy of increasing complexity. Imagine a baby is trying to learn what a dog is by pointing the finger to objects. The parents will either say “Yes, that is a dog” or “No, that is not a dog”. As the baby continues to point to objects, s/he becomes more aware of the features and characteristics that all dogs possess. In this case, the baby is clarifying a complex abstraction (the concept of dog) by building a hierarchy of increasing complexity created. In each step, the baby applies the knowledge gained from preceding layer of hierarchy. Software programs use the deep learning approach in a similar manner. The only difference is that the baby might take weeks to learn something new and complex; a computer program could do that in few minutes.

Top Online Course on Deep Learning:

Deep Learning with Andrew Ng

Course Components (Master Deep Learning, and Break into AI)

Data Science, Big Data & Big Data Analytics

In order to achieve a certain level of accuracy and speed, deep learning programs require access to immense amounts of training data and processing power. Now, this is very much possible in today’s age of big data (and big data analytics) and the internet of things. Big data is a broad and evolving term for a large number of datasets. The data could be structured, semi-structured or unstructured (non-structured). Know more about Careers, Key Skills, and Jobs in Big Data Analytics and top platforms & resources for learning data science and machine learning.

Big data analytics is the process of analyzing big data to identify hidden patterns, popular trends, unique correlations and other critical and useful information. For example, an e-commerce company will apply big data analytics to investigate customer or consumer behavior & mindset, and buying patterns. While big data is all about data, patterns (or trends) insights & impacts, internet of things is about data, devices, and connectivity.

Related Post: Scopes of Big Data and Data Science in the Banking & Financial Services Sector

Recommended Online Courses for Data Science & Big Data Analytics:

Data Science Specialization – Johns Hopkins University

Big Data Specialization – UC San Diego

Excel to MySQL: Analytic Techniques for Business Specialization – Duke University

Data Science at Scale – University of Washington

Data Structures and Algorithms – UC San Diego

Statistics with R – Duke University

Applied Data Science with Python – University of Michigan

Data Analysis and Presentation Skills – PwC

Data Warehousing for Business Intelligence – University of Colorado

Data Visualization with Tableau – UC Davis

Probabilistic Graphical Models – Stanford University

AWS Certified Solutions Architect

AWS Certified Developer

Python for Data Analysis and Visualization

PHP for Beginners

Introduction PHP & MySQL

Complete Python Bootcamp

What is the Internet of Things (IoT)?

The Internet of things (IoT) is the inter-networking of physical devices (also termed as connected devices or smart devices), vehicles, buildings and other objects (which could be smart wearable, diagnostic device, kitchen appliances etc.) embedded with electronics, software, sensors, actuators, and network connectivity that enables these “smart objects” to collect and exchange data. In other words, Internet of things is a global infrastructure for the information society. IoT allows advanced services by interconnecting (physical and virtual) things based on existing and evolving interoperable information and communication technologies.

For example, the smart refrigerator in your kitchen (at home) can send you an alert (or notification) on your smartphone (while you are leaving office) when you’re out of milk or gas. Your wearable or smartwatch can warn you if there is something wrong with your pulse or heart-rate. Additionally, all this information gets recorded. Later, the software after looking at the data can provide you information like: you are likely to run of milk on Wednesday, run out of gas in two weeks, or likely to get a heart attack in three months (so, time for a check-up and take precautions).

Related Post: IoT and Smart Home Automation for Sustainability

Since the idea of networking appliances and other objects is personalized and confidential, security is a major concern. IoT security comes into play here. IoT security is the area of endeavor concerned with safeguarding connected devices and networks in the Internet of things. IoT is expanding at an exponential rate. Like Big Data, IoT is creating new opportunities and providing a competitive advantage for businesses in current and new markets. The Internet of Things (IoT) is an ecosystem of ever-increasing complexity. It’s the next wave of innovation that is bound to humanize every object in our life, and it is the next level of automation for every object we use. It keeps adding more and more devices to the digital fold every day to improve process and growth. It touches everything—not just the data, but how, when, where and why you collect it. One of the ways to look at IoT is as multiple blocks – such as connected objects, gateways, network services, and cloud services. As mentioned earlier, security is of paramount importance. Know about the 6 Hot Internet of Things (IoT) Security Technologies.

Recommended Course: An Introduction to Programming the Internet of Things (IoT)

Blockchain Technology

The current IoT ecosystems rely on centralized communication models. All devices are identified, authenticated and connected to cloud servers that sport huge processing and storage capacities. The connection between devices needs to go through the internet. A decentralized approach to IoT networking would solve many of the security issues.

Here arrives the Blockchain technology. The blockchain is a database that maintains a continuously growing set of data records. It is distributed in nature; there is no master computer holding the entire chain. Instead, the participating nodes have a copy of the chain. It’s also ever-growing — data records are only being added to the chain. Blockchain is public. So, everyone participating can see the blocks and the transactions stored in the database. However, it’s protected by a private key.