Top Machine Learning Algorithms
Introduction
Ever since the third industrial revolution, there is a massive increase in data. So, the need for organizing and managing data becomes imperative.
We can process this data through machine learning algorithms to do some predictions. For example:-
- Movies Recommendation
- Weather Forecasting
- Medical diagnosis
- Predicting Sports Outcomes
- Better Inventory Planning
And then one question arises “What is Machine Learning”?
Basically, a machine works according to the instructions given to it. But, if a machine starts learning from its past data and operates according to it then this is called Machine Learning.
Machine learning is a subset of Artificial intelligence and it uses computer algorithms and statistical models that permit your device to learn and adapt changes from past events without being explicitly programmed. Meaning that we train our algorithms with the help of previous data.
Machine learning focuses on the development of computer programs that can change when exposed to new data. The accuracy of the result depends on the amount of data and better model.
You know what! Machine Learning is the future.
It seems like a game-changer for every field of life. Just imagine a world where intelligent machines are working for us. That’s why Machine Learning Engineers are in high demand at the moment. It means that you can easily get a high-paying job at big tech companies. So, in my opinion, everyone should learn it to boost their career.
By the way, I and 739,000+ students learned it from this “Machine Learning Course” which is taught by some of the best data scientists out there. You could also try it if you don’t want to spend years on YouTube tutorials!!!
Anyways, today we’ll have a look at the available machine learning algorithms.
There are basically three types of machine learning algorithms.
- Supervised learning
- Unsupervised learning
- Reinforcement learning
Supervised learning
Supervised learning is the basic subset of machine learning. Newbie machine learners began their practice with supervised learning algorithms.
In supervised learning, you can classify and process your data using a labeled data set. It means that you first train your model using labeled data before using it. Labeled data contains both the input and correct output. You must train your model with labeled data until it gives the right result.
Supervised learning is categorized into two main categories.
- Classification
- Regression
Classification
It is a supervised learning technique, and in classification problems, your output is classes or categories such as predicting the color of the car.
Regression
In a regression problem, your output is a real number such as age or weight.
List of common Supervised Learning Algorithms:-
- Nearest Neighbors
- Naive Bayes
- Decision Trees
- Support Vector Machine
- Neural Networks
- Linear discriminant analysis
Unsupervised learning
In unsupervised learning, data does not have any label and we don’t know what these data points do.
Here, the machine uses unlabeled data and allows the algorithms to discover the unknown patterns and information in data that was previously undetected. The task of the machine is to categorize or classify data.
Unsupervised learning is computationally complex and is less accurate as compared to supervised learning.
Types of unsupervised learning
- Clustering
- Association
Clustering
Clustering is a type of unsupervised learning where you find the patterns of data on which you are working.
Association
Association is a type of unsupervised learning where we have to find the dependencies of one data item on another data item.
List of common Unsupervised Learning Algorithms:-
- K-means for Clustering Problems
- Principal Components Analysis
- Singular value decomposition
- Independent Components Analysis
Reinforcement learning
Reinforcement learning is a subfield of machine learning. It uses a reward-based learning algorithm.
It is derived from supervised learning and the difference between both is that in supervised learning we already know that what will be the output but in Reinforcement learning, we don’t have any knowledge about the output.
Reinforcement learning enables the agent to interact with the environment by performing actions and its learning is based on the trial and error method. It does not use any predefined data.
Common approaches for implementing Reinforcement Learning Algorithms:-
- Value-Based
- Policy-based
- Model-Based
I hope that you now have some know-how about Machine Learning and its different algorithms. In the near future, I’ll be sharing a step by step guide on how to implement these algorithms in real-life projects.
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