Machine Learning is the field of study that gives computers the ability to learn without being explicitly programmed. It works on learning algorithms called neural networks which mimic how the human brain works. It is one of the most exciting recent technologies as it is capable of building machines as intelligent as humans.
Machine learning which is so prevalent today has grown out of the field of AI, or Artificial Intelligence. We can program a machine to do few basic things only but when it comes to more complex and interesting things, these programs are very hard to write because we don’t know how it is done in our brain. Even if we did know what program to write, it might be that it was horrendously complicated program. In that case, the most suitable way is to have a machine which learns to do it by itself.
The approach is to instead of writing each program by hand for each specific purpose, we collect a lot of examples and specify the correct output for given input. A machine learning algorithm then takes these examples, and produces a program that does the job .Initially, it was developed as a new capability for computers but today it touches many other segments of industry and basic science. We use machine learning a dozen of times a day without even knowing it. Each time we do a web search on Google or Bing, it works out so well only because their machine learning software has figured out how to rank what pages.
When Facebook’s or Apple’s photo application recognizes your friends, that’s also machine learning. Each time you read your E-mail and a spam filter saves you from having to wade through tons of spam, again that’s because your computer has learned to distinguish spam from non spam mails.
FUTURE OF INTELLIGENT APPLICATIONS
We see machine learning everywhere in our daily lives. The most personalized example is your smart phone as it contains many smart applications which are personalized to you particularly as a user.
Every time when you visit a website most likely there is a machine learning algorithm behind the scenes analyzing the data, analyzing interactions and really changing your experience. There is a lot more machine learning can do to change our lives.
We can already see the world changing with self driving cars, personalized medicines, with interactive things where you have got sensors and smart machines everywhere. It is one of the core technologies behind the intelligent applications which are changing the world.
Companies using machine learning algorithms have disrupted the market. For example, retail markets have been disrupted by bringing in product recommendations in the company’s website. Google has disrupted the advertising market by targeting advertising with machine learning to figure out what different people would like to click on. Movie distribution companies have a recommending system to help you find the movies according to your likes and dislikes and you can listen to music as per your mood.
Social media is connecting you to people with whom you might want to be friends with. And also companies like Ola, Uber are disrupting the taxi industry by really optimizing how to connect drivers with people in real time.
You should also read : What is IOT?
Applications of Machine Learning
How about getting an intelligent robot that reasoned about the things to tidy up your house. The robot watch you demonstrate the task and learn from that. The robot watches what objects you pick up and where you put them and try to do the exactly same thing even when you are not there.
2. DATABASE MINING
One of the reasons machine learning has so pervaded is the growth of the web and the growth of the automation. All this means that we have much larger data sets than ever before. So, for example tons of Silicon Valley companies are today collecting web click data, also called click stream data, and are trying to use machine learning algorithms to mine this data to understand the users better and to serve the users better.
3. MEDICAL RECORDS
With the advent of automation, we now have electronic medical records, so if we can turn medical records into
medical knowledge, then we can start to understand the disease better.
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4. COMPUTATIONAL BIOLOGY
With automation again, biologists are collecting lots of data about gene sequences, DNA sequences and so on and machine learning algorithms are giving us a much better understanding of the human genome and what it means to be human.
5. HANDWRITING RECOGNITION
It turns out one of the reasons it’s so inexpensive today to route a piece of mail across other countries because there is a learning algorithm that has learned how to read your handwriting so that it can automatically route envelope on its way, and so it costs us a few cents to send this thing thousands of miles.
6. VIRTUAL PERSONAL ASSISTANT
It is software that can perform tasks or services for an individual. There are various assistants available nowadays. Most widely used are Apple’s Siri, Google Assistant, Amazon Alexa and Microsoft Cortana. In these products the strong emphasis is on voice user interface. These assistants are AI based and uses the concept of machine learning. These are intended to replace some of the day to day functions and responsibilities of people. They decrease the time you have to spend on mundane tasks in order to allow you to free up your time to do more interesting, strategic and hopefully impactful things.
7. RECOGNIZING PATTERNS AND ANAMOLIES
The things which are best done using a learning algorithm are recognizing patterns, so for example objects in real scenes or the identities and expressions of people’s faces, or spoken words. There’s also recognizing anomalies. For example, fraudulent credit card transaction would be an anomaly. To detect this, you really need to combine a very large number of rules. And also those rules change every time because people change the tricks they use for fraud. So, we need a complicated program that combines rules that we can change easily.
Another example of an anomaly would be an unusual pattern of sensor readings in a nuclear power plant. And you wouldn’t really want to have to deal with those by doing supervised learning.
8. PHOTO OCR
Photo OCR stands for Photo Optical Character Recognition. With the growth of digital photography and more recently the growth of camera in our cell phones we now have tons of visual pictures that we take all over the place.
And one of the things that has interested many developers is how to get our computers to understand the content of these pictures a little bit better.
The photo OCR problem focuses on how to get computers to read the text to the purest in images that we take.
We have seen that Machine Learning is one of the most highly sought after skills in tech which is transforming multiple industries such as healthcare, autonomous driving , sign language reading, music generation , speech and object recognition, image segmentation, modelling language and natural language processing . It is also a new superpower that will let you build AI systems that just weren’t possible a few years ago.
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Below are some Good Books to learn Machine Learning :
- Machine Learning For Absolute Beginners: A Plain English Introduction (Machine Learning For Beginners)
- Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
- Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition
- Machine Learning with R – Second Edition: Expert techniques for predictive modeling to solve all your data analysis problems 2nd Edition