Machine Learning : An Outlook
Machine Learning is somehow like the latest Corona virus. Everyone is talking about it, many (just the medical staff) know what is actually going on and only a few (the ones making the vaccines) know what it really is. If you ever tried to read articles about machine learning on the Internet, most likely you stumbled upon two types of them: thick academic trilogies filled with theorems (I couldn’t even get through half of one) or fishy fairy tales about artificial intelligence, data-science magic, and jobs of the future.
I decided to write a post I’ve been wishing existed for a long time. A simple introduction for those who always wanted to understand machine learning. Only real-world problems, practical solutions, simple language, and no high-level theorems. One and for everyone. Whether you are a programmer or a manager.
So coming to it’s definition first…
What is Machine Learning?
It is an application of artificial intelligence (AI) that provides systems the ability to automatically learn and improve from experience without being explicitly programmed. Machine learning focuses on the development of computer programs that can access data and use it learn for themselves.
Undoubtedly, Machine Learning is the most in-demand technology in today’s market. Its applications range from self-driving cars to predicting deadly diseases such as ALS. The high demand for Machine Learning skills is the motivation behind this blog.
It is indeed very correctly stated that machine learning will automate jobs that most people thought could only be done by people. And although still in its infancy, machine learning will be a game changer in supply chain and we have all made that prediction even with the basic knowledge that we have.
Types of Machine Learning :
Supervised Learning
Supervised Learning is the one, where you can consider the learning is guided by a teacher. We have a data set which acts as a teacher and its role is to train the model or the machine. Once the model gets trained it can start making a prediction or decision when new data is given to it.
Unsupervised Learning
The model learns through observation and finds structures in the data. Once the model is given a data set, it automatically finds patterns and relationships in the data set by creating clusters in it. What it cannot do is add labels to the cluster, like it cannot say this a group of apples or mangoes, but it will separate all the apples from mangoes.
Suppose we presented images of apples, bananas and mangoes to the model, so what it does, based on some patterns and relationships it creates clusters and divides the data set into those clusters. Now if a new data is fed to the model, it adds it to one of the created clusters.
Reinforcement Learning
It is the ability of an agent to interact with the environment and find out what is the best outcome. It follows the concept of hit and trial method. The agent is rewarded or penalized with a point for a correct or a wrong answer, and on the basis of the positive reward points gained the model trains itself. And again once trained it gets ready to predict the new data presented to it.
These are further subdivided into the following parts:-
Supervised learning classified into two categories of algorithms:
· Classification: A classification problem is when the output variable is a category, such as “Red” or “blue” or “disease” and “no disease”.
· Regression: A regression problem is when the output variable is a real value, such as “dollars” or “weight”.
Unsupervised learning classified into two categories of algorithms:
· Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior.
· Association: An association rule learning problem is where you want to discover rules that describe large portions of your data.
Why is Machine Learning important?
Re-surging interest in machine learning is due to the same factors that have made data mining and Bayesian analysis more popular than ever. Things like growing volumes and varieties of available data, computational processing that is cheaper and more powerful, and affordable data storage.
All of these things mean it’s possible to quickly and automatically produce models that can analyze bigger, more complex data and deliver faster, more accurate results (even on a very large scale). And by building precise models, an organization has a better chance of identifying profitable opportunities or avoiding unknown risks.
And finally coming to the most interesting and important part…
Applications of Machine Learning :
Netflix’s Recommendation Engine: The core of Netflix is its infamous recommendation engine. Over 75% of what you watch is recommended by Netflix and these recommendations are made by implementing Machine Learning.
Facebook’s Auto-tagging feature: The logic behind Facebook’s DeepMind face verification system is Machine Learning and Neural Networks. DeepMind studies the facial features in an image to tag your friends and family.
Amazon’s Alexa: The infamous Alexa, which is based on Natural Language Processing and Machine Learning is an advanced level Virtual Assistant that does more than just play songs on your playlist. It can book you an Uber, connect with the other IoT devices at home, track your health, etc.
Google’s Spam Filter: Gmail makes use of Machine Learning to filter out spam messages. It uses Machine Learning algorithms and Natural Language Processing to analyze emails in real-time and classify them as either spam or non-spam.
Web Search Engine: One of the reasons why search engines like Google, Bing etc work so well is because the system has learnt how to rank pages through a complex learning algorithm.
Photo tagging Applications: Be it Facebook or any other photo tagging application, the ability to tag friends makes it even more happening. It is all possible because of a face recognition algorithm that runs behind the application.
Spam Detector: Our mail agent like Gmail or Hotmail does a lot of hard work for us in classifying the mails and moving the spam mails to spam folder. This is again achieved by a spam classifier running in the back end of mail application.
Speech recognition : Speech recognition is a process of converting voice instructions into text, and it is also known as “Speech to text”. Machine learning algorithms are widely used by various applications of speech recognition. Google assistant, Alexa and many more are using speech recognition technology to follow the voice instructions.
Self-driving cars : Machine learning plays a significant role in self-driving cars. Tesla, the most popular car manufacturing company is working on self-driving car. It is using unsupervised learning method to train the car models to detect people and objects while driving.
Online fraud detection : Machine learning is making our online transaction safe and secure by detecting fraud transaction, there may be various ways that a fraud transaction can take place such as fake accounts, fake ids, and steal money in the middle of a transaction.
Image recognition : Image recognition is one of the most common applications of machine learning. It is used to identify objects, persons, places etc.
Some general fields where Machine Learning has a huge impact are as follows:-
Financial Services: Banks and other businesses in the financial industry use machine learning technology for two key purposes: to identify important insights in data, and prevent fraud. The insights can identify investment opportunities, or help investors know when to trade. Data mining can also identify clients with high-risk profiles, or use cyber surveillance to pinpoint warning signs of fraud.
Government services: Government agencies such as public safety and utilities have a particular need for machine learning since they have multiple sources of data that can be mined for insights. Analyzing sensor data, for example, identifies ways to increase efficiency and save money. Machine learning can also help detect fraud and minimize identity theft.
Health Care services: Machine learning is a fast-growing trend in the health care industry, thanks to the advent of wearable devices and sensors that can use data to assess a patient’s health in real time. The technology can also help medical experts analyze data to identify trends or red flags that may lead to improved diagnoses and treatment.
Retail services: Websites recommending items you might like based on previous purchases are using machine learning to analyze your buying history. Retailers rely on machine learning to capture data, analyze it and use it to personalize a shopping experience, implement a marketing campaign, price optimization, merchandise supply planning and for customer insights.
Oil and Gas services: Finding new energy sources. Analyzing minerals in the ground. Predicting refinery sensor failure. Streamlining oil distribution to make it more efficient and cost-effective. The number of machine learning use cases for this industry is vast — and still expanding.
Transportation services: Analyzing data to identify patterns and trends is key to the transportation industry, which relies on making routes more efficient and predicting potential problems to increase profitability. The data analysis and modeling aspects of machine learning are important tools to delivery companies, public transportation and other transportation organizations.
Thank you for giving it your time, I hope to write more on this topic covering all the other aspects as well.
Until then… stay tuned!
