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.
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 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.
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 around 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.
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.
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.
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 amount of data sets. The data could be structured, semi-structured or unstructured (non-structured).
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.
Author: Tanmoy Ray
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