Machine Learning is a fanatic term you frequently hear from many people nowadays. They often argue it's a new concept and is popular in the last decade or so. But what they don't know is, it's very much popular among the scientist or researchers in the past fifty or sixty years. Logistic Regression the standard Supervised Machine Learning algorithm, Joseph Berkson developed in 1944. We discuss Supervised Machine Learning in the latter part of the article.
What is Machine Learning? How it impacts our daily life? We discuss the mysteries of Machine Learning and find out the answers to it.
What is Machine Learning?
The bookish definition of Machine Learning is to train the system or program to learn automatically. The system learns from the data and then explores the solution. It is not wrong to say that instead of a solution provider, Machine Learning is the seeker of the solution.
Speech Recognition one of the ML applications recognize speech and text. It must acknowledge all the sounds a user said. A perfect example is an English word schedule. Its pronunciation is different in the US and UK, but the system recognizes it precisely.
Now, with the counter-arguments, we can use traditional programming and achieve the same results ML can produce. The short answer is, we can. But there is a problem we face in conventional programming. We only talked about the one-word schedule and there are several words having different pronunciation in the US and UK. If we think of managing everything through conventional programming, our source code will grow big and unmanageable (Just think about it).
How does it impact our routine lives?
Inadvertently, we use mobile apps that enrich our user experience. Without instantly realizing, in the backend servers, these apps trained in ML to make our lives easier. Imagine without the spam filter number of emails we receive in our inbox. Local maps to accurately analyze the continuous movement of motorized traffic or the chatbots deployed on the bank or any other website. At present, mobile app developers typically using ML to provide a rich user experience. If you conventionally use a modern smartphone once or twice a day or consume a few decent hours on your mobile, you can't go without ML.
Types of Machine Learning
There are several types of different Machine Learning, but in this ML blog, we restrict ourselves to four.
- Supervised Learning
- Unsupervised Learning
- Semi-supervised Learning
- Reinforcement Learning
In Supervised Learning, we know the possible outcome. We adequately provide historical data to the standard algorithm and get the desired result. We trained the model on a labeled dataset. The labeled dataset means we have both input and output parameters.
Image Classification represents a classic example of Supervised Learning. The algorithm trained enough to identify the image accurately. When the user provides the image to the system, based on its data, it will recognize the type of image.
Supervised Learning Algorithms
- Linear Regression
- Logistic Regression
- Nearest Neighbor
- Decision Trees
- Support Vector Machine (SVM)
- Random Forest
- Neural Networks (most of them)
In unsupervised learning, the model trained using unlabeled data. The trained algorithm model acts on the learned information it has and proceeds without explicit guidance. Unsupervised learning algorithms intentionally allow the model to perform complex tasks compared to supervised learning. It also allows the system to self-improve and provides more accurate results over the period.
Let's understand it with the help of an example. Let's say you have a car. And your kid recognizes it. One day you decided to go on a long drive. During the journey, the child yet identifies the vehicles driving on the way without your help based on the information he accumulates from home (like four tires, steering, wipers, etc.).
It is unsupervised learning.
Unsupervised Learning Algorithms
- Anomaly Detection
- Association rule learning
- Dimension reduction
Supervised learning is often time-consuming because it expects to label the data every time. In practical scenarios, it is not possible to always have a labeled dataset. We have partially labeled data. When we train the model using the partially labeled data points, it is traditionally called semi-supervised learning.
Voice Recognition is a classic example of semi-supervised learning models. Labeling audio files remain a time-consuming and highly resource-intensive task. Using the semi-supervised learning improves the accuracy of the voice recognition model.
In Reinforcement Learning system learns from experience. For every action, the system gets the rewards or penalties based on the move. If it has an adverse effect, then the system avoids it next time. All advanced robots implement Reinforcement Learning Algorithms.
In a typical Machine Learning Project, we need data and a model to train. Eventually, we applied the model to predict, whenever a new case or data is feed into the system.