Why Machine Learning is the Future?

Jitesh Khurkhuriya
A free video tutorial from Jitesh Khurkhuriya
Data Scientist and Digital Transformation Consultant
4.5 instructor rating • 5 courses • 18,573 students

Lecture description

Why machine learning is the future? The Data explosion. We will also see some common examples of ML as well as discuss couple of case studies of Machine Learning.

Learn more from the full course

A-Z Machine Learning using Azure Machine Learning (AzureML)

Azure ML (Machine Learning): Azure Machine Learning Studio, Machine Learning on cloud, Machine Learning without coding

10:47:59 of on-demand video • Updated May 2020

  • Master Data Science and Machine Learning Models using Azure ML.
  • Understand the concepts and intuition of Machine Learning algorithms
  • Build Machine Learning models within minutes
  • Choose the correct Machine Learning Algorithm using the cheatsheet
  • Deploy production grade Machine Learning algorithms
  • Deploy Machine Learning webservices in the simplest form possible including excel
  • Bring in great value to business you manage
English Hello and welcome to the Azure Machine Learning course. In this lecture, we will see why machine learning is the future? But before we do that let's just try to understand some measurement of data. We know that one terabyte is equal to 1000 of gigabytes. Many of us have a hard disk worth one TB. One PB, in fact, is equal to 1000 TB. That's equivalent of having one thousand hard disks of 1TB. Going even farther, One Exabyte is equal to 1000 PB or one million terabytes. Now you must be wondering why am I telling you all this. Well, if you look at this graph of IDC that gives the prediction of data growth along with the data that has been generated so far, you will be amazed. We have generated 7,900 exabytes of data until 2015. And if you see the growth pattern, we will be generating more than 40,000 exabytes of data by 2020 It is almost impossible to make sense of this amount of data using traditional means of hardware and software and that is where the machine learning can help us in understanding the data as well as making intelligent business decisions. One more important statistics is that 90 percent of the entire data in the world has been produced in the last two years alone. And it has been done by various industries such as Biotech, Banking and Finance, Manufacturing, Telecom, e-commerce and of course social media which is the biggest contributor of this. No wonder then that some of the leaders in the industry are calling Big Data and the machine learning which powers it as the next big thing. Harvard Business Review called Data scientist as the sexiest job of the 21st century. Also Microsoft CEO Satya Nadella called out machine learning as the key development in his memo to Microsoft in last July. No wonder data is the new Oil, that we all should be mining as it is going to drive critical business decisions and help companies save cost as well as find out new business avenues. Let's see how some companies have used this to their advantages. During Brexit the Pound Sterling was down by 11% just two days after the vote. Stock markets across the world crashed. This left millions of traders all over the world scrambling to find safer investment positions. however, there was this one company called Omega Point whose customers saw little change in the value of their investments even after the Brexit vote and the root of investing success during this time by Omega Point's customer was a data driven strategy based on machine learning models that took into account more than 50 economic indicators including company specific reports and broader macroeconomic data. Thus saving millions of dollars for their customers. Another example that all of you must be familiar with is the people you may know ads from Linkedin. That is also a great example of machine learning. When it was launched it achieved a click through rate 30 percent higher and generated millions of new page views. And thanks to this one new feature Linkedin's growth trajectory shifted significantly upward. Why did it happen? Thanks to the machine learning algorithms running at the background who would find out who are the people who could possibly be associated with this one professional and hence giving him the ad of people you may know. All right. Self-driving car, Product recommendations, Uber's face identification of the drivers, Google Home, Netflix movie recommendation as well as Siri in the Apple iPhone are all very familiar examples of how machine learning is being used in our day to day products. Well what are the benefits of machine learning? If so many industries are using them. Well, it can be used to pick the best result and help us reach decisions faster. It can also develop insights that are beyond human capabilities and which is based on various patterns derived from the Big Data. At the same time it can act at the right time and take advantage of the sales opportunities converting them into closed deals which basically means it can have an impact on the revenue as well as the profits of a company. The next question that you may have is why you should learn AzureML among so many other products and technologies that are available. Right. Allow me to explain that using a very fascinating case study of Hershey's. Hershey's leveraged the power of machine learning without hiring a data scientist. Unbelievable but true. Hershey's products are sold in over 60 countries worldwide with the combined revenue of USD 7.4 Billion and one of the best selling product for Hershey's twizzlers. At the same time you must remember that due to the sheer size of their operations even a 1 percent change in the sizing for Twizzlers can result in a savings of half a million dollars for Hershey's and consider this as a sugary product. For the products which are based on chocolate the gains would be much much more for a similar gain in percentage. Now how and why do you think Hershey's could achieve this without hiring a data scintist? Well the answer lies in Azure Machine Learning capabilities Let's look at the capabilities of Azure machine learning. It's a complete drag and drop interface and absolutely no programming is required for some of the basic machine learning models. You can follow a workflow and build a great ML model. It's as simple as that. Microsoft has also made available a large variety of algorithms as modules. All you need to do is simply drag and drop them on your canvas to build your model And as it requires no coding, deployment etc. you can literally go from experiment to production API in a matter of minutes for simple experiments. Now this is a very good news for those who know R and Python as well. Because if you have built some models using those languages you can simply put that code into AzureML and make a model out of that. AzurML provides a complete flexibility of data storage. This is also one of the key strengths of AzureML as it supports variety of data storage options. Apart from that Microsoft also has a large number of pre-built APIs available as a service. Some of the large companies such as Tata Motors the makers of Landrover, Uber, Rolls-Royce aircraft engines, X-Box and many many more have already started using the Azure Machine Learning. CIO.com recently published an article that says Microsoft Azure machine learning is a one stop shop for cloud based machine learning. I'm sure by now you are ready to take this step of learning AzureML and I'm equally excited. In the next lecture of what is machine learning, we will understand the definition of machine learning, how the machines learn, how machine learning is different than the traditional rule based systems as well as we will look into what is supervised, Unsupervised and reinforcement learning. Thank you so much for your time and I'll see you in the next lecture. Until then have a great time ahead.