An Introduction to Machine Learning

Machine Learning (ML) is coming into its own, with growing recognition.ML can play a key role in a wide range of critical applications. Such as data mining, natural image recognition, and expert systems. Similarly, Machine learning provides potential solutions in all these domains and is set to be a pillar of our future civilization.

The supply of able ML designers has yet to catch up to this demand. A major reason for this is that ML is just plain tricky. This Machine Learning tutorial introduces the basics of ML theory, themes, and concepts. Making it easy to follow the logic and get comfortable with the machine learning basics.

What is Machine Learning?

So what exactly is “machine learning” anyway? Machine Learning is actually a lot of things. The field is pretty vast and is increasing rapidly. Being constantly partitioning and sub-partition ad nauseam into different sub-specialties.

There are some basic common threads, however, and the overarching theme is best summed up by this oft-quoted statement made by Arthur Samuel way back in 1959: “[Machine Learning is the] field of study that gives computers the ability to learn without being explicitly programmed.”

And more recently, in 1997, Tom Mitchell gave a “well-posed” definition that has proven more useful to engineering types:

“A computer program is said to learn from experience E with respect to some task T and some performance measure P, if its performance on T, as measured by P, improves with experience E.”

So if you want your program to predict, for example, traffic patterns at a busy intersection (task T), you can run it through a machine learning algorithm with data about past traffic patterns (experience E ) and, if it has successfully “learned”, it will then do better at predicting future traffic patterns (performance measure P).

Machine Learning:

The highly complex nature of many real-world problems. It often means that inventing specialized algorithms will solve them perfectly every time. Examples of machine learning problems include, “Is this cancer?”, “What is the market value of this house?”, “Which of these people are good friends with each other?”, “Will this rocket engine explodes on take off?”, “Will this person like this movie?”, “Who is this?”, “What did you say?”, and “How do you fly this thing?”. After that, All of these problems are excellent targets for an ML project. ML has been applying to each of them with great success.

Machine Learning solves problems that cannot be solved by numerical means alone:

For Instance, Here we’ll get in touch with these learnings.

Major Classes of Machine Learning:

Supervised Learning:
Supervised Learning

Supervised learning as the name indicates the presence of a supervisor as a teacher. Basically supervised learning is learning in which we teach or train the machine using data that is well labeled which means some data is already tagged with the correct answer. After that, the machine is provided with a new set of examples(data) so that the supervised learning algorithm analyses the training data(set of training examples) and produces a correct outcome from labeled data.

Unsupervised Learning:

Unsupervised learning is the training of a machine using information that is neither classified nor labeled and allowing the algorithm to act on that information without guidance. Here the task of the machine is to group unsorted information according to similarities, patterns, and differences without any prior training of data.

Unlike supervised learning, no teacher is provided that means no training will be given to the machine. Therefore the machine is restricted to find the hidden structure in unlabeled data by our-self.

Reinforcement learning :
Reinforcement learning

Reinforcement learning, in the context of artificial intelligence, is a type of dynamic programming that trains algorithms using a system of reward and punishment. A reinforcement learning algorithm, or agent, learns by interacting with its environment.

Machine Learning Examples:

Herein, we share a few examples of machine learning that we use every day and perhaps have no idea that they are driven by ML.   

1. Virtual Personal Assistants :

Siri, Alexa, Google Now are some of the popular examples of virtual personal assistants. As the name suggests, they assist in finding information, when asked over voice. All you need to do is activate them and ask “What is my schedule for today?”, “What are the flights from Germany to London”, or similar questions. For answering, your personal assistant looks out for the information, recalls your related queries, or sends a command to other resources (like phone apps) to collect info. You can even instruct assistants for certain tasks like “Set an alarm for 6 AM next morning”, “Remind me to visit Visa Office the day after tomorrow”.

Machine learning is an important part of these personal assistants as they collect and refine the information on the basis of your previous involvement with them. Later, this set of data is utilized to render results that are tailored to your preferences.

Virtual Assistants are integrated into a variety of platforms. For example:
. Smart Speakers: Amazon Echo and Google Home.
. Smartphones: Samsung Bixby on Samsung S8.
. Mobile Apps: Google Allo.

2. Predictions while Travelling:

We all have been using GPS navigation services. While we do that, our current locations and velocities are being saved at a central server for managing traffic. This data is then used to build a map of the current traffic. While this helps in preventing the traffic and does congestion analysis, the underlying problem is that there are fewer cars that are equipped with GPS. Machine learning in such scenarios helps to estimate the regions where congestion can be found on the basis of daily experiences.

3. Search Engine Result Refining:

Google and other search engines use machine learning to improve the search results for you. Every time you execute a search, the algorithms at the backend keep a watch on how you respond to the results. If you open the top results and stay on the web page for long, the search engine assumes that the results it displayed were in accordance with the query. Similarly, if you reach the second or third page of the search results but do not open any of the results, the search engine estimates that the results served did not match the requirement. This way, the algorithms working at the backend improve the search results.

4. Videos Surveillance:

Imagine a single person monitoring multiple video cameras! Certainly, a difficult job to do and boring as well. This is why the idea of training computers to do this job makes sense.

The video surveillance system nowadays is powered by Artificial Intelligence that makes it possible to detect crimes before they happen. They track unusual behavior of people like standing motionless for a long time, stumbling, or napping on benches, etc. The system can thus give an alert to human attendants, which can ultimately help to avoid mishaps. And when such activities are reported and counted to be true, they help to improve the surveillance services. This happens with machine learning doing its job at the backend.

5. Online Fraud Detection:

Machine learning is proving its potential to make cyberspace a secure place and tracking monetary frauds online is one of its examples. For example, PayPal is using ML for protection against money laundering. The company uses a set of tools that helps them to compare millions of transactions taking place and distinguish between legitimate or illegitimate transactions taking place between the buyers and sellers.

6. Product Recommendations:

You shopped for a product online a few days back and then you keep receiving emails for shopping suggestions. If not this, then you might have noticed that the shopping website or the app recommends you some items that somehow match your taste. Certainly, this refines the shopping experience but did you know that it’s machine learning doing the magic for you? On the basis of your behavior with the website/app, past purchases, items liked or added to the cart, brand preferences, etc., the product recommendations are made.

7. Social Media Services:

From personalizing your news feed to better ads targeting, social media platforms are utilizing machine learning for their own and user benefits. Here are a few examples that you must be noticing, using, and loving in your social media accounts, without realizing that these wonderful features are nothing but the applications of ML.

People You May Know: Machine learning works on a simple concept: understanding with experiences. Facebook continuously notices the friends that you connect with, the profiles that you visit very often, your interests, workplace, or a group that you share with someone, etc. On the basis of continuous learning, a list of Facebook users is suggested that you can become friends with.

Face Recognition: You upload a picture of yourself with a friend and Facebook instantly recognizes that friend. Facebook checks the poses and projections in the picture, notices the unique features, and then matches them with the people in your friend list. The entire process at the backend is complicated and takes care of the precision factor but seems to be a simple application of ML at the front end.

Similar Pins: Machine learning is the core element of Computer Vision, which is a technique to extract useful information from images and videos. Pinterest uses computer vision to identify the objects (or pins) in the images and recommend similar pins accordingly.

How do you Use Machine Learning Daily?

Except for the examples shared above, there are a number of ways where machine learning has been proving its potential. Let us know how machine learning is changing your day-to-day life and share with us your experience with it in the comments below.  



We’ve covered much of the basic theory underlying the field. Machine Learning here, but of course, we have only barely scratched the surface.
Clearly, Machine Learning is an incredibly powerful tool. In addition, In the coming years, it promises to help solve some of our most pressing problems. Similarly, as well as open up whole new worlds of opportunity for data science firms. After that, The demand for Machine Learning engineers is only going to continue to grow. Above all, It offers incredible chances to be a part of something big. In conclusion, I hope you will consider getting in on the action!

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