There is much conversation about Machine Learning, what it brings to the table in the case of AI, how we can use it for statistical analysis, data-driven decision making, and the application to various industries. There are very few organizations that aren’t scrambling to integrate Machine Learning into their daily processes and functions. But the issue is, while there is access to Machine Learning and many of its applications, there is a gap between what people know about Machine Learning and where it can be implemented.
There are a few key areas that you should understand
Machine Learning is the science of teaching a machine to learn by itself. Moreover, there are many benefits to this. For example, if a person does a repetitive job daily – like cleaning the floor, without questioning if it needs to be done. That person may also get tired and not perform the task to the highest quality. If a machine could detect where needs the most cleaning, how it should be cleaned based on factors like how clean the floor already is, the materials on the floor – they could (and would) perform the same job to a better standard — using what the machine understands to make the right decisions. In other words, you would enable a machine to learn enough information to make the right decisions like:
- How long the floor should be cleaned for
- Does the floor need to be cleaned
Learning models are what is used to enable the machines to think in the way that benefits us most. The machines capture data from the environment around them, then deliver it to the Machine Learning model. The data can then be used to make predictions. In this case, it would answer the two questions above.
How Do They Learn
While humans do many things intuitively, machines cannot. While you could teach a person to bake a cake, or again – clean a floor and within a few lessons, they would understand and be able to perform the task. Machines need to be fed the data and given the desired outcomes before they will be able to perform the task. This is where true Machine Learning comes into its own. Machine Learning would tell the machine to know what kind of cake or the type of cleaning, floor type, the chemicals in the suds used, and the duration of cleaning required.
Machine Learning You Already See
So while that basic overview gives an insight, you should question where Machine Learning is already an application in your daily life. Here is where you are regularly impacted by Machine Learning:
- Amazon recommends products to you, based on your browsing history
- Smartphones using facial recognition to unlock themselves, or for taking photos
- Many banks use Machine Learning to detect fraud in real-time
Those are just a few that scratch the surface of where Machine Learning is in play.
Gaining Popularity?
Thanks to the internet, we heard about new technologies and advances more often. However, that does not mean that they have not already been around for a while. Machine Learning has technically been around for decades – although you may have heard it referred to as Three Laws of Robotics. However, three main drivers are actively increasing the implementation of Machine Learning:
- The share increase in data generation
- Reduction in the cost of sensors
- Storing data has seen a reduction in costs
- The cost of computing has reduced significantly
- Cloud computing is more popular than ever
Exciting Breakthroughs?
Scientists have presented measurements on a quantum dot device, performed by machine algorithms in real-time. Machine Learning was applied to the productive estimations in Gallium Arsenide quantum dots.
Dr. Natalia Ares from the University of Oxford’s Department of Materials, said, “We have trained the machine with data on the current flowing through the quantum dot at different voltages. Like facial recognition technology, the software gradually learns where further measurements are needed to achieve the maximum information gain. The system then performs these measurements and repeats the process until effective characterization is achieved according to predefined criteria, and the quantum dot can be used as a qubit.”
Machine Learning Isn’t Automation
An issue that many people face when learning to understand what Machine Learning is that it gets confused with automation. And while Machine Learning can be applied to automation, it isn’t the same. Here is an example:
- Arranging a set of rules in your inbox means those rules will be followed and the emails will go to where you have specified – Automation
- Emails that have no rules applied will be judged on by if you have previously opened and interact or ignore and deemed spam and be moved there – Machine Learning.
Machine Learning enables better automation.
Tools
This is where Machine Learning can really get exciting if you have a passion for programming. The tools that you choose will depend heavily on the scale of operations. Here are some of the commonly used tools for Machine Learning:
Basics
- Excel
- Powerpoint
Databases
- SQL
- Oracle
- Hadoop
Visualization Tools
- Tableau
- QlikView
- D3.js
Languages
- Java
- Javascript
- Scala
- SAS
- Julies
- Python
- R
Where Does Deep Learning Fit It?
AI, Machine Learning, and Deep Learning are all linked. Deep Learning is a sub-field of Machine Learning. “Machine Learning uses algorithms to parse data, learn from that data, and make informed decisions based on what it has learned — deep learning structures algorithms in layers to create an “artificial neural network” that can learn and make intelligent decisions on its own” – ZenDesk.
Machine Learning Models
Two data types can be fed to a machine.
- Unstructured data – which is data that gets captured but not stored in a table
- Structured data – All data that is captured and stored in databases
For machines to use unstructured data, it must be converted to structured data first.
Building the Machine Learning Model
When you are building a Machine Learning model, you will be looking at the following points:
- Problem definition: taking any business problem and converting it to a Machine Learning problem
- Hypothesis: Creating a hypothesis and potential features for the model
- Data: Collecting data for testing the hypothesis
- Exploration: A data exploration will help you refine. Removing outliers, missing values and then put the data into the required format
- Building the Machine Learning model
- Deploying the model
Drawbacks of Machine Learning
Like with any technological breakthrough, there will be drawbacks while the process is refined and improved. Currently, a machine that is taught to clean wooden floors can only clean wooden floors. When faced with carpet, the machine will not be able to work. Whereas a person could do both.
Achievements
There have been many achievements in Machine Learning, and here are a few of them.
- Winning DOTA2 against the professional players
- Beating Lee Sidol at the traditional game of GO, Google DeepMind’s algorithm
- Google saving up to 40% of electricity in its data centers by using Machine Learning
- Writing entire essays and poetry and creating movies from scratch using Natural Language Processing techniques.