- Have you ever thought about how your email inbox is so smart🤔 that it can filter spam, tag important emails or conversations, and segregate promotional, social, and primary messages🧐??
- So here I will explain how Machine Learning algorithms work and how we can take advantage of them for the benefit of app development companies.
- There is a complex algorithm for this type of prediction and this algorithm is within the broad spectrum of Machine Learning.
- But first, let’s define what exactly Machine Learning (ML) is.
What is Machine Learning💡?
- Arthur Samuel, a pioneer in the field of artificial intelligence and computer gaming, coined the term “Machine Learning”. He defined machine learning as — “Field of study that gives computers the capability to learn without being explicitly programmed”.
- Machine Learning(ML) can be explained as automating and improving the learning process of computers based on their experiences without being actually programmed i.e. without any human assistance.
- The process starts with feeding good quality data and then training our machines(computers) by building machine learning models using the data and different algorithms.
- The choice of algorithms depends on what type of data do we have and what kind of task we are trying to automate.
The importance of algorithms
- Machine Learning uses a process where the computer algorithm finds a pattern in the data and predicts the probable results.
- Machine learning patterns are highly adaptable in the way that they are constantly updated when new data is entered.
Basic Difference in ML and Traditional Programming?
- Traditional Programming: We feed in DATA (Input) + PROGRAM (logic), run it on the machine, and get output.
- Machine Learning: We feed in DATA(Input) + Output, run it on the machine during training and the machine creates its own program(logic), which can be evaluated while testing.
Machine Learning Use Cases
👉🏻Manufacturing🏭 : Predictive maintenance and condition monitoring.
👉🏻Retail🛍️ : Upselling and cross-channel marketing.
👉🏻Healthcare👩🏻⚕️ and life sciences🧪 : Disease identification and risk satisfaction.
👉🏻Travel✈️ and hospitality🏥 : Dynamic pricing.
👉🏻Financial services🏦🏧 : Risk analytics and regulation.
👉🏻Energy⚡: Energy demand and supply optimization.
Applications of Machine learning in Big MNC’s :
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 Mumbai to Dubai”, 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 information.
✏️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 adapted as per your preferences.
2. Predictions while Commuting
=>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 for example Google Map
=>This data is then used to build a map of the current traffic. While this helps in preventing the traffic and does congestion/overcrowding analysis, the underlying problem is that there are less fewer cars that are equipped with GPS.
=>Machine learning in such scenarios helps to estimate the regions where overcrowding can be found on the basis of daily experiences.
✏️Online Transportation Networks:
=>When booking a cab, the app estimates the price of the ride. When sharing these services, how do they minimize the bypass? The answer is machine learning.
=>Jeff Schneider, the engineering lead at Uber ATC reveals in an interview that they use ML to define price surge hours by predicting the rider demand.
=>In the entire cycle of the services, ML is playing a major role.
3. 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 are powered by AI 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.
4. Social Media Services
✏️From personalizing your news feed to better ad targeting, social media platforms are utilizing machine learning for their own and user benefits.
✏️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 are suggested that you can become friends with.
✏️Face Recognition: You upload a picture of you with a friend and Facebook instantly recognizes that friend. Facebook checks the poses and projections in the picture, notice the unique features, and then match 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.
5. Email Spam and Malware Filtering
✏️There are a number of spam filtering approaches that email clients use. To ascertain that these spam filters are continuously updated, they are powered by machine learning.
✏️Decision Tree Induction are some of the spam filtering techniques that are powered by ML.
✏️Over 325, 000 malware are detected every day and each piece of code is 90–98% similar to its previous versions.
✏️The system security programs that are powered by machine learning understand the coding pattern. Therefore, they detect new malware with 2–10% variation easily and offer protection against them.
6. Online Customer Support
✏️A number of websites nowadays offer the option to chat with customer support representatives while they are navigating within the site. However, not every website has a live executive to answer your queries.
✏️In most cases, you talk to a chatbot. These bots tend to extract information from the website and present it to the customers.
✏️They tend to understand the user queries better and serve them with better answers, which is possible due to its machine learning algorithms.
7. 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.
8. 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 cart, brand preferences, etc., the product recommendations are made for example Flipkart, Amazon..and many more.
9. 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 legal or illegal transactions taking place between the buyers and sellers.
Thank you for reading my article !! Good Day 😊