*This article was written by V Sharma.*

Astonishing Hierarchy of Machine Learning Needs – Artificial intelligence and machine learning are used interchangeably often but for they are not the same. Machine learning is one of the most active areas and a way to achieve AI. Why ML is so good today; for this, there are a couple of reasons. Machine Learning entirely depend upon algorithms of two kinds

- Learning style
- Symmetry & similarity

This post only depicts the high-level summary to gain simple, easy and layman idea. This blog post is not for PhD students or for experienced professionals in the industry but for sure it might just give them some smiling effects.

**The Brief**

Machine Learning is the hottest subject of today’s time, DataScientist is the sexiest job of today but implementing these buzzwords in real life business is the most important need. The real need for today’s time and business is to clarify, demonstrate and extract real values to benefit everyone from this golden keyword “Machine Learning”. Why ML is so good today; for this, there are a couple of reasons like below but not limited to though.

- The explosion of big data
- Hunger for new business and revenue streams in this business shrinking times
- Advancements in machine learning algorithms
- Development of extremely powerful machine with high capacity & faster computing ability
- Storage capacity

As on date sadly most of the machine learning methods are based on supervised learning. Which means we still have a long long way to go. In Fintech domain we say #MachineLearning is the future (actually that future is now) of #Ecommerce & #DataScientist will work as a batman for #FinTech & #InsureTech. Today’s machines are learning and performing tasks; that was only be done by humans in the past like making a better judgement, decisions, playing games etc.

**Lets Define Machine Learning**

Arthur Samuel coined in 1959. He called it a “field of study that gives computers the ability to learn without being explicitly programmed.” In our words, Machine learning is a subject for real-life work outside of PhD or scholar books. At AILabPage we define ML as below.

Machine Learning is a focal point where business needs and experience (Mathematics, Statistics & Algorithmic logic/thinking) meet emerging technology and decides to work together to put useful results on the table for real business.

**Types of Machine Learning**

The approach of developing ML includes learning from data inputs based on “What has happened”. Evaluating and optimizing different model results remains focus here. As on date Machine Learning is widely used in data analytics as a method to develop algorithms for making predictions on data. It is related to probability, statistics, and linear algebra.

Machine Learning is classified into three categories at a high level depending on the nature of the learning and learning system.

- Supervised learning: Machine gets labelled inputs and their desired outputs. The goal is to learn a general rule to map inputs to the output.
- Unsupervised learning: Machine gets inputs without desired outputs, the goal is to find structure in inputs.
- Reinforcement learning
**:**In this algorithm interacts with a dynamic environment, and it must perform a certain goal without a guide or teacher.

In a hypothetical situation or most of the time (At least from our personal experience), the amount of data anyone will find may look like the picture above. We are talking about the volume of the data. The volume of data for Supervised Learning is highest and for reinforcement learning its the lowest almost all the times.

*To read the whole article, with illustrations,* *click here.*

© 2019 Data Science Central ® Powered by

Badges | Report an Issue | Privacy Policy | Terms of Service

**Most Popular Content on DSC**

To not miss this type of content in the future, subscribe to our newsletter.

- Book: Statistics -- New Foundations, Toolbox, and Machine Learning Recipes
- Book: Classification and Regression In a Weekend - With Python
- Book: Applied Stochastic Processes
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- How to Automatically Determine the Number of Clusters in your Data
- New Machine Learning Cheat Sheet | Old one
- Confidence Intervals Without Pain - With Resampling
- Advanced Machine Learning with Basic Excel
- New Perspectives on Statistical Distributions and Deep Learning
- Fascinating New Results in the Theory of Randomness
- Fast Combinatorial Feature Selection

**Other popular resources**

- Comprehensive Repository of Data Science and ML Resources
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- 100 Data Science Interview Questions and Answers
- Cheat Sheets | Curated Articles | Search | Jobs | Courses
- Post a Blog | Forum Questions | Books | Salaries | News

**Archives:** 2008-2014 |
2015-2016 |
2017-2019 |
Book 1 |
Book 2 |
More

**Most popular articles**

- Free Book and Resources for DSC Members
- New Perspectives on Statistical Distributions and Deep Learning
- Time series, Growth Modeling and Data Science Wizardy
- Statistical Concepts Explained in Simple English
- Machine Learning Concepts Explained in One Picture
- Comprehensive Repository of Data Science and ML Resources
- Advanced Machine Learning with Basic Excel
- Difference between ML, Data Science, AI, Deep Learning, and Statistics
- Selected Business Analytics, Data Science and ML articles
- How to Automatically Determine the Number of Clusters in your Data
- Fascinating New Results in the Theory of Randomness
- Hire a Data Scientist | Search DSC | Find a Job
- Post a Blog | Forum Questions

## You need to be a member of Data Science Central to add comments!

Join Data Science Central