Ever since computers have been created, we are progressively trying to make them advanced and powerful. From the room-sized machines to the gadgets in our pockets, And are now designing artificial intelligence to automate every task that would need human intelligence. Machine Learning is a subset of Artificial Intelligence. Computers, software and devices perform by experience likewise the way the human brain does.

The possibility is that Artificial Intelligence is omnipresent and we may not know about it. Machine learning plays a key role in tagging people on Facebook or Instagram. Furthermore, it also recommends the next video to watch on YouTube or NetFlix. Every time we use Google Search, we are using a system that has many machine learning systems at its core, from understanding the text of our query to further adjusting the results based on our personal interests.

Definition of Machine Learning

Machine Learning is all about tools and technology that certainly uses data to answer our questions. “Machine learning is the scientific study of algorithm and statistical model that allow software applications to make decisions autonomously without being explicitly programmed by programmers.” 

Types of Machine Learning

Machine Learning is broadly divided into three parts. ML Engineers and data geeks mostly use Supervised Machine Learning and Unsupervised Machine Learning. Also, these algorithms are simple to learn and the easiest to implement. In contrast, Reinforcement Learning is very powerful and complex to apply. Let’s check out what do they mean!

Supervised Machine Learning

In this type of Machine Learning, we need to train the model with lots of training data. It is the easiest to learn and simplest to implement, similar to teaching a child with the use of flashcards. After training the model with new data and the logic we got before we predict the output. When fully trained, this algorithm will be able to understand new data and would predict a good output for it. Some of the algorithms that come under Supervised Machine Learning are:

  • Random Forest
  • Decision Trees
  • Logistic Regression
  • Decision Trees
  • Linear Regression
  • Gaussian Naive Bayes
  • K- Nearest Neighbor (KNN)
  • Support Vector Machine (SVM)

Examples of Supervised Learning

Social Media Services like Facebook, Instagram is utilizing machine learning for their own and user benefits, they have personalized our news feed and better ads targeting. Some other features which are the application of machine learning:

  • People You May Know: Facebook continuously observes the friends that we connect with, our mutual friends, most often visited profiles and pages, your interests, workplace etc. As a result, Facebook suggests us a list of Facebook users that we can become friends with.
  • Face Recognition: When we upload a picture with our friend, Facebook instantly recognize that friend by checking the poses and projections in the picture, the unique features, and then match them with the people in our friend list which is a very complicated process at the backend. 
  • Similar Pins: With Computer Vision, we can extract useful information from images and videos. Pinterest uses computer vision to identify the pins in the images and recommend similar pins.

Unsupervised Machine Learning

Unsupervised Machine Learning is sufficient in learning itself. It is opposite to supervised learning since the data is unlabeled. From the data and accurate tools, it can learn to group, cluster, or organize the data. So that a human or other intelligent algorithm can make sense of the newly organized data. This type of learning is data-driven as the outcomes are controlled by the data and the way it’s formatted. Some areas used in Unsupervised Algorithm are:

  • Hierarchical clustering
  • Probabilistic Clustering 
  • K-means
  • Principal Component Analysis
  • Anomaly Detection

Examples of Unsupervised Learning

  • Product Recommendation: We may have noticed, when we do online shopping, we keep receiving emails for shopping suggestions or might have noticed that the website or the app recommend us some items that somehow matches with the previous item we kept in our cart all this magic is done by the Machine Learning. Our past purchases, items liked or added to cart, brand preferences etc helps in filtering the product recommendation.
  • Buying Habits: These buying habits of customers are stored somewhere in the database which further can be used to group customers into similar purchasing segments. These can help to resemble recommender systems and even help companies to group things accordingly.

Reinforcement Learning

Reinforcement Learning is very much different from Supervised and Unsupervised Learning. It is behaviour driven, which learns from its mistakes. This algorithm will make a lot of mistakes during the onset but over time it learns to make fewer mistakes than it used to. We provide some sort of signal to the algorithm that associates good behaviours with a positive signal and bad behaviours with a negative one. Further, we can reinforce the algorithm to select good behaviours over bad ones. Important learning algorithms are:

  • Deep Q Network (DQN)
  • State-Action-Reward-State-Action (SARSA)
  • Q learning

Let us imagine that our RL Agent is learning to play Super Mario.

  • The RL agent (one who takes the action) receives state S⁰ from the environment (where the agent takes the action) i.e. Mario
  • Based on that state S⁰(situation in which the agent is)the RL agent takes an action A⁰(work done by the agent), say — our RL agent moves right. Initially, this is random.
  • Now, the environment is in a new state  (new frame from Mario)
  • The environment gives some reward R¹(the measurement of success or failure) to the RL agent.

The loop to work continues until we are dead or we reach our destination. It continuously loops around state, action and reward. Above all, the aim of the RL agent is to maximize the reward.

Examples of Reinforcement Learning

  • Game Playing: If we were to code using traditional techniques, we need to mention a large number of rules to cover all the possible possibilities. Meanwhile, with reinforcement learning, we do not need to manually write any rules. The agent will learn to play the game. It is also used in Manufacturing, Inventory Management, Delivery Management, and Finance Sector.
  • Robotics and Industrial Automation: RL has contributed significantly to Industrial Automation. For example, Google has reduced energy consumption (HVAC) in its data centres. RL allows the robot to autonomously discover optimal behaviour through trial-and-error interactions with its environment. The agent provides constructive feedback that measures the one-step performance of the robot.

Our world is drastically changing with machine learning becoming more popular in everything we use each day. The day will soon come when we can expect that our technology will be personalized, insightful, and self-correcting.