From when did exactly machine learning (ML) become the apple of every corporate giant’s eyes?
The name machine learning (ML) was introduced by Arthur Samuel in 1959. From that time until now, ML had been polished and improved over and over again. And the 2020 prediction says ML is going to be at large for the next five years at least. But what is it that makes Machine Learning so unique? Where it can be used? What is its importance really?
In this blog, we are going to introduce you to machine learning (ML) thoroughly. If you have any more queries after you are done reading hit us up in the comment section below.
Do you know how many companies are using machine learning to enhance their business? The names will surprise you for sure! Companies like Google, IBM, Microsoft, Twitter, Intel, Apple are literally building their base on ML. When so many big companies are making such good use of it, then there must be something really special about ML. Don’t you think? So let’s start digging for the special element from the introduction itself!
What is Machine Learning (ML)?
Machine learning (ML) is the scientific study of algorithms and statistical models that computer systems use to perform a specific task without using explicit instructions, relying on patterns and inference instead. It is seen as a subset of artificial intelligence.
The algorithms used in machine learning (ML) are built by following a mathematical model based on sample data known as “training data” in order to make predictions or decisions without being explicitly programmed to perform the task. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop a conventional algorithm for effectively performing the task.
You might want to know a little bit more about how machine learning (ML) works. Right?
So the process starts when you input the training data into selected algorithms. The type of training data you are putting into the algorithm impacts it in some ways. Afterward, you can test the functionality of the algorithm by inputting new data into it. After you check the predictions and the results, if the predictions don’t stand up to your expectations, then the algorithm is trained enough to run it a multiple numbers of times until you find the desired output.
So from this, we can say, machine learning algorithms are capable enough to learn on their own and generate the most optimal answers that will gradually increase in accuracy over time. Doesn’t it sound like a dream that is too good to be true?
To simplify the complex pattern of ML, experts categorized it into a few categories:
Supervised Learning:
In supervised learning, people use known data as training data. And when you are dealing
with a known dataset, the learning is supposed to be supervised. Isn’t it?
Algorithms generally used in supervised learning are:
- Polynomial regression
- Random forest
- Linear regression
- Logistic regression
- Decision trees
- K-nearest neighbours
- Naïve Bayes
Unsupervised Learning:
The training data is unknown and unlabelled here. That means no one has looked at that data before. Since the input can’t be guided to the algorithm without the details of known data.
Algorithms that are currently being used for unsupervised learning are:
- Partial least squares
- Fuzzy means
- Singular value decomposition
- K-means clustering
- Apriori
- Hierarchical clustering
- Principal component analysis
Now, let’s see how Machine Learning is being used by organizations. Shall we?
Machine Learning (ML) Use Cases:
As we said earlier when we talk about Machine Learning use cases some huge names come up. Let’s see what the top two of them are experiencing for the same!
Google:
When it comes to AI, Machine Learning and Deep Learning, Google is probably making the best out of this. It launched an AI chatbot with the help of ML that answers any question you ask. It kind of works like a sophisticated auto-response email in a range of contexts including Skype, Twitter, and Slack direct messages.
Although these chatbots are having some difficulties in using profane languages, they work very well in order to filter out spams.
Microsoft:
LinkedIn provides the best platform for Microsoft to showcase the enterprise applications it builds based on Machine Learning.
The most significant acquisition made by Microsoft with the help of Machine Learning space is Maluuba that has the world’s one of the most impressive deep learning research labs for natural language understanding.
Why Machine Learning Is Important?
We all dream of getting rid of all the mundane tasks when technology takes care of it. Don’t we? This is where Machine Learning (ml) comes into the scene. These are the few things that we can expect from ML:
- It can be used to automate a lot of different tasks that we always thought only humans can perform. That includes Image Recognition, Text A generation or playing games
- It is going to have huge effects on the economy and living in general. Entire work tasks and industries can be automated and the job market will change forever.
- It has several applications that enhance your real business results like time and money savings. It has the potential to dramatically impact the future of your organization.
Summary:
We hope these facts could make you believe in this fact that the revolutionary platform of ML going to be ‘The Trend’ of this era? At last but not the least, a certified Machine Learning professional is earning $112,095 per year. It can rise to $160,00 as well as per your experience.
Ready to shape your career in the same? Apply for Machine Learning Certification right away then, instead of waiting around!
Also read our previous blog on When Career In Data Science Is In Top Trend!
