In part one, we have covered a lot taking a gentle approach to what machine learning entailed. This part will dive into the category of machine learning. Also, machine learning algorithms use in solving problem related to each category will be mentioned without digging into the mathematical part.
Categories of Machine Learning
Machine learning is categorized into three and depending on whether they have been trained with humans or not, they can learn incrementally, or If they work simply by comparing new data points to find data points, or can detect new patterns in the data, and then will build a model. Majorly, they are
- Reinforcement Learning
In this type of machine-learning system, the data that you feed into the algorithm, with the desired solution, are referred to as “labels.”
Supervised learning groups together tasks of classification (Spam or Not Spam). The above diagram is about spam filter program and it is a good example of classification because it’s been trained with many emails at the same time as their class.
Another example is to predict a numeric value like the price of a flat, given a set of features (location, number of rooms, facilities) called predictors; this type of task is called regression.
These are some of the supervised learning algorithms
- K-nears neighbours
- Linear regression
- Neural networks
- Support vector machine
- Logistic regression
- Decision trees and random forests
You should keep in mind that some regression algorithms can be used for classifications as well, and vice versa.
In this type of machine-learning system, you can guess that the data is unlabeled.
Unsupervised algorithms include the following:
- Clustering: k-means, hierarchical cluster analysis
- Association rule learning: Eclat, apriori
- Visualization and dimensionality reduction: kernel PCA, t-distributed, PCA
As an example, suppose you’ve got many data on customers patronising your retail store, using the algorithms for detecting groups with similar visitors. It may find that 52% of your male customers pay in cash, 30% male customers pay using a credit card, 10% female customers pay with credit card etcetera, by using a clustering algorithm, it will divide every group into smaller sub-groups.
Visualization and dimensionality algorithms are also unsupervised learning algorithms which are important and helpful. You’ll need to give them many unlabeled data as an input, and then you’ll get 2D or 3D visualization as an output.
The goal here is to make the output as simple as possible without losing any of the information. To handle this problem, it will combine several related features into one feature: for example, it will combine a car’s make with its model. This is called feature extraction.
Reinforcement learning is another type of machine-learning system. An agent
“AI system” will observe the environment, perform given actions, and then
receive rewards in return. With this type, the agent must learn by itself.
You can find this type of learning type in many robotics applications that learn
how to walk. Reinforcement learning involved exploration/exploitation tradeoff, Markov Decision Processes (MDPs), the classic setting for RL tasks, Q-learning, policy learning, and deep reinforcement learning and lastly, the value learning problem.
Self-driving cars, Game AI (bots), Robot navigation etc. are some examples of reinforcement learning.
And that’s it. Now you know about machine learning, types, and some of the algorithms.
- Machine Learning for Humans, Vishal Maini and Samer Sabri
- Machine Learning Step-by-Step Guide To Implement Machine Learning Algorithms with Python, Rudolph Russell