1. What is Machine Learning?
Machine learning is the ability of a computer to derive rules and patterns from given set of data. To derive these insights statistical tools are deployed through machine learning algorithms. Machine Learning algorithms break down the data (aka training data) to formulate best-fit mathematical models. Machine Learning can be broadly classified into two types
- Unsupervised Machine Learning
- Supervised Machine Learning
2. Unsupervised Machine Learning:
Unsupervised Machine Learning is also known as clustering. In this, the learning algorithm tries to group given set of data points into different groups based on similarities in predetermined features.
Example: if you feed a clustering algorithm with 1000 images, it will try to group them based similarity in the image features. These features can be: faces, colors, dimensions etc.
The name “unsupervised” because you don’t need to specify any outputs with corresponding inputs in this type of learning. All you need to do is dump the available data for the algorithm to learn from. This will become clearer when you look at Supervised Machine Learning next.
Below info-graphic will give you an indication of clustering algorithm at work
3. Supervised Machine Learning
In a supervised machine learning the learning algorithm tries to identify a relationship between given set of inputs and their corresponding outputs. There are two types of supervised machine learning algorithms:
Regression is used for systems where the value being predicted falls somewhere on a continuous real number range. These systems help us with questions of “How much?” or “How many?”.
Example: Assume you have historical data of “House prices” vs “House area”
Supervised Machine Learning (Linear Regression) can be used to determine relation between a given house’s area and it’s price. This equation can be used to predict prices of newer houses in the market.
Classification is used for systems where output value being predicted is a category. Classification algorithms help us answer questions like “Is this tumor cancerous?”, “Does this cookie meet our quality standards?” etc.
Example: You have historical data of engine noise and vibrations from a car manufacturing company along with whether each of the engines cleared quality checks or not. You can use this data to build a machine learning model that can predict if a given engine with certain noise and vibration levels will clear quality checks.
In conclusion below info-graphic will give you an easily understandable view