Have you ever used a search engine? If yes, you must have seen a number of searches that show up after you’ve typed in your keywords. These accurate results are the by-product of the machine learning applications. Any website or software which answers your query is able to do that because of the work of a machine learning engineer.
What is Machine Learning?
Machine learning is a subset of AI in the field of computer science that often uses statistical techniques to give computers the ability to “learn” with data, without being explicitly programmed. By ‘learn’, we mean that over time the system gets better in carrying out the task it is designed for.
This is achieved by introducing the system to a large amount of data. By studying the data, the system is able to improve itself – in other words, learn. Today, machine learning applications are everywhere.
A simple example of a working machine learning system is the checkers player software created by Arthur Samuel – the man who coined the term ‘Machine Learning’ in 1959
Arthur’s Checker Playing Program!
Created by Arthur Samuel, the program was not only capable of playing checkers, but also of remembering the games it played. Over the span of several games, the program became capable of recognizing good board positions and moves that could boost its winning chances. The program’s progress was so remarkable that with time it became better at the game than Arthur himself!
The only downfall to this program was the large amount of data it required. Arthur, who wasn’t even a good checkers player, played about a thousand games against the program. Only then, it became better than him.
Machine Learning Applications
There are plenty of Machine Learning applications!
[1] Search Engine Result Refining:
Every time you execute a search, the search engine records your response to the results. Did you open only the top few links or did you read through all the links until you found the right one? Depending on your response, the search engine improves its results.
[2] Improving Social Media Sites:
Machine learning has allowed social media websites to improve their interaction with their users. Due to machine learning, social sites can now target ads better and provide users with a more relevant news feed. By relevant news feed, we mean that if a particular user is interested in fashion, the content that will appear on their feed will be more fashion-centric.
Even Facebook’s facial recognition is an outcome of machine learning! When a Facebook user with either “tag suggestions” or “face recognition” turned on is tagged in a photo, the social network’s machine learning systems suggests a tag if there’s a match!
[3] Detecting Spam Emails:
By observing your response to your emails, for instance the emails you mark as ‘spam’, computers are able to improve their own understanding of spam. In a way, your own filtering actions provide them with the required data. This experience improves the system’s security response in detecting spam emails.
[4] Detecting Fraud Transactions:
Transaction fraud in the banking and finance sector has lessened due to machine learning. The bank’s security systems can observe and record a client’s regular transactions, notifying the account holder and the bank in case there is any unusual activity.
[5] Personalizing Online Shopping Sites:
Popular online shopping sites such as Alibaba, Amazon, Flipkart and several more are using machine learning algorithms. Notice those accurate product recommendations? How did Amazon know you needed hair oil? It’s all thanks to machine learning
[6] Virtual Personal Assistants: Hello Google! Hi Siri! Hey Alexa!
Virtual assistants use machine learning to improve their interactions with their user. They observe and refine themselves to provide their users with a better experience. For instance, observing your daily travel routes and suggesting ones with less traffic to decrease your commute time.
Now that we know the Machine Learning Applications, let’s take a look at a day in the life of a Machine Learning Engineer!
Different Job Roles
Depending on the firm you are working in, your job description will vary. For instance, if you’re working at a law firm, your job might involve developing a software that can group together lawsuit-related documents, analyze those documents and find similar documents. The software will also help the lawyers clear out documents and papers unrelated to the case. On the other hand, if you work as a Business Analyst, you will have to provide solutions to enhance business processes, such as distribution or productivity.
Data Management
Data munging is one of the important Machine Learning applications. For the non-techie, data munging is the process of converting ‘raw, unprocessed’ data into a more appropriate or applicable format that allows for more convenient consumption of the data. The final data can then be sent to the relevant data analyst or stored, ready to be analysed at a later date.
Client Handling
Client handling is also an important part of a machine learning engineer’s job. Only when you understand what your client wants from a software, will you be able to design it to cater to his needs. Since most of the software programs created are components to the organization’s final product, most machine learning engineers also need to have a good grasp of the company’s APIs and libraries.
Learning New Things Daily
Machine learning, as a field, is growing. Experts in the field are constantly researching new methods and techniques. Neural networks, which started developing in the 1950s, lost popularity because of computing demands. But with the advent of new technology, neural networks have again climbed onto the pedestal as a ‘State-of-The-Art Machine Learning Technique.’ In order to stay updated with the latest developments, a machine learning engineer must read, learn and grow!
Machine Learning is an thriving field that has become a part of our daily lives. It is now difficult to imagine a world without Machine Learning!
Since you are here, take a look at the Top 5 Machine Learning Courses you need to check out!
