What is Machine Learning? | Types of ML

Machine learning (ML) is the process of using one or more computer programs to find an answer to a specific question in an unstructured way, i.e., without explicitly giving the questions and answers to the computer. It requires a large and fast network of computers in order to perform the task.

Types of Machine Learning

There are two major types of machine learning: supervised machine learning and unsupervised machine learning.

Supervised Learning

Supervised learning takes as input a large amount of data and can then extract patterns and predict the behaviour of the data. The features are set to maximize the amount of the signals.

Unsupervised Learning

Unsupervised learning is carried out without an explicit set of features, but by observing existing data. It is used for applications where the characteristics of the unknown entity(s) need to be discovered using relatively few examples.

Supervised learning is the bread and butter of machine learning as it helps in the development of better algorithms for object detection, speech recognition and face detection. However, unsupervised learning offers the ability to identify patterns in data that may not be present in the data obtained from supervised learning.

Examples of Machine Learning

• eBay, Flipkart and Amazon recommendations based on the customer’s browsing and purchasing behaviour. The way you search and purchase on Amazon, the better the recommendations it gives.
• Google’s search engine, that ranks the websites by relevancy. 
• Self-driving cars. The more situations the self-driving car navigates, the better it drives. 

Machine Learning Algorithms

ML algorithms are so powerful they can learn about the external world without being given a lot of external information, like a computer. All they need is a big data set and maybe some idea of what some of the factors might be.

Machine learning algorithms usually use some form of pre-processing and then predictions can be made. They usually have a very good way to learn from data given what has been pre-processed. Even before the pre-processing, they can discover a bunch of patterns in a data set that we couldn’t have known or found by ourselves.

They can even make predictions from data that the human has not had time to process. One thing that sets them apart from other kinds of algorithms is that they typically have a lot of flexibility to take in various data.

Example Machne Learning Algorithms

Some important ML algorithms are listed below:

• Linear Regression
• Logistic Regression
• Decision Tree
• SVM
• Naive Bayes
• kNN
• K-Means
• Random Forest
• Dimensionality Reduction Algorithms
• Gradient Boosting algorithms
  • GBM
  • XGBoost
  • LightGBM
  • CatBoost

Future of Machine Learning

Building machine learning systems is becoming more and more complicated in today’s world. While it’s easier than ever to write code to teach computers to perform specific tasks, working with large amounts of raw data is harder than ever.

There’s much to learn from the real world and the real problems people face. It’s not only our imagination that drives progress, the technology we create today must go further and faster than what’s out there today. People can’t talk with computers, but they can write computer programs that do. Therefore, we can expect more influence of machine learning in our data to day life in the future. 

Key Takeaways:

• ML gives computers the ability to learn without being explicitly programmed. 
• Similar to how we humans learn. The process of learning begins with observations or data, such as examples, direct experience, or instruction, in order to look for patterns in data and make better decisions in the future based on the examples that we provide.                                                         
• When computers learn automatically without human intervention or assistance machine will adjust actions accordingly.
• It is the ability for computer programs to analyze data, extract information automatically, and learn from it. 
• Implementing machine learning means creating algorithms that can learn and make predictions on data.