We all are aware of Machine Learning and its basics. How many of you have heard about the term Supervised Machine Learning? Its importance, types, and application. It is one of the few fundamental terms that you should know while you embark on your journey towards Machine Learning. At Crafsol, we have clearly defined every concept of Machine Learning as we excel at providing Machine Learning Consulting Services. Let me help you understand the basic concepts first.
As you know in ML, you teach machines about data and algorithms. Here, you can create programming models that perform certain tasks with no human intervention.
Based on the way they create algorithms, we can classify Machine Learning into 3 types:-
- Supervised Learning
- Unsupervised Learning
- Semi-supervised Learning
- Supervised Learning: For this kind of learning, the computer is taught by illustration. To elaborate more, the data is known to you and labelled. So you know the exact input that can be mapped with the output. This makes it easy for the algorithms to learn from the past and predict future events.
- Unsupervised Learning: This is just the opposite of Supervised Learning, as the data collected here has no labels. So you are unclear about the outputs. However, you can inherit structures and patterns through algorithms from the input data.
- Semi-Supervised: Data used here is a mix of labelled as well as unlabeled. So a mix of supervised and unsupervised techniques are used.
Advantages of Supervised Learning
- Learning gives the algorithm experience, which helps in predicting the result for new data. The input data is well-known and labelled while the output can be predicted.
- Using the experience, it is easy to optimize the performance of the algorithm.
- You can solve various types of real-world computation problems with the help of Supervised Learning.
- The output produced is accurate and trustworthy when compared to unsupervised techniques.
Challenges in Supervised Learning
- The biggest challenge is the volume of data. If the volume of data is big and growing, then you are not sure about the data labels. This makes it difficult to pre-define the rules.
- In this, you need to understand the key labels and inputs. This can make the entire process complex when compared to the unsupervised method.
- This kind of learning does not happen in real time since it keeps learning from previous experiences.
- Supervised Learning requires a lot of time for training as the computation time is high.
Application of Supervised Learning
There are many applications across the industry, since it provides the best algorithms for finding accurate results.
- Fraud Detection in Banking and Finance Sector: It helps in identifying whether the transactions made by the users are genuine.
- Spam detection: With the help of specific keywords and different content, Supervised Learning can easily detect emails if it is spam. It recognizes certain keywords and sends them into the spam category.
- Bioinformatics: The biggest application is to store the biological information of human beings. This could be information related to fingertips, eyes, swabs, iris textures, and a lot more.
- Object recognition: Another application is “Recatch” (prove you are not a robot). Here, choose multiple images to confirm if you are a human. You can access certain information only if you can identify it correctly. If not, keep trying until you get the right identifications.
Conclusion: Supervised learning is used to train machines so you can develop predictive data models. They have multiple applications across all domains and industries. It requires expertise to build, scale and update the models. Connect with us at Crafsol Services to learn more about the application of Supervised Learning.