Machine Learning has been an integral part of our lives. There are so many aspects in our life that are impacted by Machine Learning which we don’t even notice.
This can be as simple as Facebook news stories. This branch of Artificial Intelligence has the power of using data in a new way. It involves working on computer program development that can access data and automatically perform tasks through detections and predictions.
It also enables the computer systems to learn from the data and continuously improve from experience. When you feed more data into the machine, you enable the algorithm to learn from the data and deliver improved results.
So, when you ask Alexa on the Amazon Echo to play your favorite music station, she will play the one that you have listened to most in the past. This was able to happen because of Machine Learning.
What is Machine Learning?
Machine Learning is a branch of Artificial Intelligence (AI) that involves teaching computer systems to think like humans – using past experiences to learn and improve upon them.
Computers explore data and identify patterns; all of which happens with minimal human intervention. Almost all the tasks that can be finished using a set of rules or a data-defined pattern can be automated through machine learning. Machine Learning has allowed organizations to transform their processes, which could be completed only by humans.
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A few examples of this include reviewing resumes, responding to customer service messages and calls, and bookkeeping.
In the coming years, it is expected that applications of machine learning in different industry verticals will rise exponentially as it will continue to enable organizations to deliver faster results and scale their production capacity, generating significant business value
So, if you want to take advantage of the multitude of opportunities offered by the field, now is the perfect time to enroll yourself in a Machine Learning course.
There are two main techniques used in Machine Learning:
- Supervised learning – This form of learning involves using a previous ML deployment for collecting data and producing a data output. It is an exciting type of learning as it works in almost the same way as humans learn. For supervised tasks, the computer system is fed a training set (collection of data points that are labelled). An example of this is a set of readouts from a train terminals and markers system that had delays in the past three months.
- Unsupervised learning – This form of machine learning helps in finding unknown patterns in the data. The algorithm uses only unlabeled examples for learning some inherent structure present in the data. Dimensionality reduction and clustering are two common unsupervised machine learning tasks. In dimension reduction models, the number of variables present in the dataset is reduced by grouping correlated or similar for more effective model training and better interpretation. In clustering, data points are grouped into meaningful clusters so that the elements in one cluster are similar but dissimilar to elements from other clusters. It is considered useful for tasks like market segmentation.
How does Machine Learning work?
In the early stages of Machine Learning, experiments included theories of computers processing data, recognizing patterns in them, and learning from them. Now, Machine Learning has become more complex than that after years of building upon the foundational experiments.
Even though machine learning algorithms are not a new development, the ability to use those complex algorithms in big data applications effectively and rapidly are relatively new. If a company is able to use machine learning with a degree of sophistication, it can leave its competitors behind.
Machine Learning has several applications from automating tedious data entry work to complex use cases like fraud detection or insurance risk assessments.
It can also be used for client-facing functions like product recommendations (Spotify’s playlist algorithms or Amazon’s product suggestions), customer service, and internal applications that are used for organizations to reduce manual workloads and speed up processes.
One of the crucial parts of machine learning that makes it so valuable is the ability to detect things that the human eye can miss. Through machine learning models, you can catch complex patterns which would have been missed during a human analysis.
Cognitive technologies such as natural language processing, deep learning, and machine vision have helped machine learning free up human workers so that they can focus on other tasks like perfecting the efficiency and quality of their service and product innovation.
So, you might be good at looking through an organized, massive spreadsheet and identifying patterns, but thanks to artificial intelligence and machine learning, algorithms are capable of examining large datasets and quickly identify patterns.
If you work in the field of machine learning or have even learned the basics of it, you must be familiar with the Python and R programming languages that are used in the field.
However, depending on the type of project and model, there are several other languages that can be used as well. You can also use AI and Machine Learning tools like suites, software libraries, and toolkits that help in executing tasks.
In fact, the reason why Python is one of the most popular programming languages used for machine learning is the multitude of libraries available to choose from and its widespread support. According to Gitbug, it is on the top of their list of top languages for machine learning on their site.
Python can also be used for data analysis and data mining and it supports the implementation of several machine learning algorithms and models. Python can support algorithms like classification, clustering, dimensionality reduction, and regression.
However, even though Python is the most commonly used machine learning language, there are several other languages that are popular as well. Since there are some ML applications that use models that are written in a different language, using tools such as Machine Learning Operations (MLOps) can be helpful.
According to the research conducted by the University of Sharjah, UAE, the machine learning market is expected to grow to USD 8.81 billion by 2022, with a compound annual growth rate of 44.1% from 2016.
Machine Learning has enabled solutions that can enhance customer experience and are adopted by organizations all over the world. Through these solutions, they are able to increase their ROI and get an edge over their competitors.