What are the Different Types of Learning in Machine Learning

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If you are a professional working in the field of Information Technology (IT), you must be aware of the plethora of opportunities available across the world. From mobile development, front-end development, technical support, networking, to cybersecurity, or even cloud computing, professionals can choose the career path they are interested in. Adding to these existing fields is Artificial Intelligence (AI) and Machine Learning which are gaining traction and have become of the most sought-after career domains among IT pros. 

As we all know that Machine Learning and AI is one of the top trending technologies of this era. Professionals and new-age learners are tending towards them to make a career in these domains. So, if you want to learn more about Machine Learning and Artificial Intelligence, then Intellipaat is one of the best options for the Artificial Intelligence and Machine Learning Course.

How is your engagement with digital devices today? You must have a virtual assistant, probably Alexa or Siri, at home that understands what you say. You must be getting personalized advertisements and product recommendations based on your interests. You are also easily able to use carpooling and sharing the cab with people going on the same route. As much as these tasks, requiring some simple clicks on a smartphone, seem quite easier for the user, involves a complex machine learning algorithm working behind. 

Jeff Bezos, the founder, and CEO of e-commerce giant Amazon, has to say this about machine learning – “Much of what we do with machine learning happens beneath the surface. Machine learning drives our algorithms for demand forecasting, product search ranking, product and deals recommendations, merchandising placements, fraud detection, translations, and much more. Though less visible, much of the impact of machine learning will be of this type — quietly but meaningfully improving core operations.” 

So, What Exactly is Machine Learning?

Machine Learning, a subset of AI, is the process of enabling machines to make accurate predictions when data is fed to it. Now, this data takes various forms like text, audio, numbers, images, or more. Machine learning helps AI systems to extract patterns from data and learn from it. Generally, machine learning requires a huge amount of training data and includes repetitive training to enhance the learning process and decision making of algorithms. By adding more data, we can automate the machine learning training letting it learn new data patterns and adapting the algorithm. 

Some of the benefits of using machine learning include powerful and quicker processing, affordable data management, less cost, speedier analysis of big data, and better decision making. Today, machine learning is being leveraged in areas like image processing, data mining, robotics, game development, text analysis, and voice recognition.  

Types of Machine Learning

When you come across any machine learning tutorial, it broadly classifies machine learning into three categories, namely:

  • Supervised learning
  • Unsupervised learning
  • Reinforcement learning

Supervised Learning

It is a type of machine learning where we use labeled training data to train the algorithm. It can be described as a system where both input and desired output are provided so that it can be used for future data processing. After the algorithm is trained, we test it using the test data. If the algorithm is well-trained, its prediction regarding the test data will be correct. 

For example, virtual assistants like Siri or Cortana are trained to comprehend human speech and then take appropriate action. Similarly, the Gmail application is able to filter any new email into Inbox or Spam based on what emails you marked as Spam in the past.

Supervised learning is further subdivided into:

  • Classification – here the output value is a category like true or false, animal or no animal, etc.
  • Regression – where the output variable is a real value like rupees, weight, etc. 

Unsupervised Learning 

In contrast to supervised learning, unsupervised learning is a subset of machine learning where information is extracted from datasets that have input data without labeled response. Algorithms in unsupervised learning are allowed to function freely and learn more about the data fed into the system and identify new patterns.  

Unsupervised learning is further subdivided into:

  • Clustering – here the identification of the inherent groupings in the data is done
  • Association –  here rules are discovered that describe large portions of the data sets

Reinforcement Learning

It is an important type of machine learning where a system is allowed to observe an environment and learn how to behave in it by doing actions and taking the feedback from the outcomes. If the outcomes are as desired, the system is rewarded. The aim here is to identify a suitable strategy that could result in an increase in the total cumulative reward of the system. Reinforcement learning comprises of three components – the agent (decision maker), the environment (everything that the agent interacts with), and the actions (what the agent performs).  

Apart from this classification of the types of machine learning, you may also come across the term Semi-Supervised learning. This kind of learning involves a training dataset with labeled as well as unlabeled data. It is particularly useful when giving a range of labeling examples is time-consuming and identifying relevant features from the data becomes difficult. 

Explore the World of Machine Learning

Do you think gaining machine learning skills and facing machine learning MCQs in an interview is worthy of your career? Well, here is something that can persuade you. Machine learning is one of the fastest-growing domains and knowing its market size would surprise you. Grand View Research has reported that the global machine learning market size is expected to be worth USD 96.7 billion by 2025, expanding at a Compound Annual Growth Rate (CAGR) of 43.8 percent from 2019 to 2025. 

In another report, Gartner also highlighted that the demand for AI talent in top countries has tripled from 2015 to 2019 in the IT sector. Not only this but the strongest demand for talent with AI skills has rather come from non-IT business units. So even if you are not from the IT field, you can have a promising career when you are ready to be a part of the global AI talent pool. So why wait! Take up a machine learning course and pave your way to becoming a successful machine learning engineer.