This article is about Machine Learning
Real World Examples of Machine Learning
By NIIT Editorial
Published on 25/02/2021
Machine Learning is a sub-domain of Artificial Intelligence wherein algorithms are programmed to learn through experiences. It uses complex, high-level statistical means to impart reasonable intelligence to computers. Once ML training sets have had enough throughput in terms of data, they can automate large portions of analytics. The code is smart enough to backpropagate upon observing new patterns and adjust its overall understanding of the concept. This principle reduces human intervention.
The fact that machines can learn without explicitly being told to do so increases machine learning applications in real life. Below we state top ML use cases that you would come across most often.
Machine learning experts deploy Convolutional Neural Networks (CNN) that designate pixels into numbers, that are readily consumed by algorithms. Using CNNs, the code compiles varying image data sets to make sense of the outcome. After ample practice (i.e. after processing millions of images) ML algorithms can identify image details/patterns most likely to be overlooked by humans. Depending upon the industry, these could range from closely examining X-ray scans, to identifying vehicles, and handwriting. Image recognition is the underlying principle guiding the workings of facial recognition cameras.
It is also referred to as Automatic Speech Recognition (ASR), Speech to Text (STT), and computer speech recognition. ML-assisted speech recognition is used to convert audio files into text format and inputted to the code. It is used by voice assistants like Siri and Alexa as well as for voice search, and voice dialing among other ML-applications.
Stock traders are not unbeknownst to the practice of arbitrage. It refers to purchasing financial instruments from one market, then identifying a secondary market where the same is being sold at a higher price. The profit is the price difference. Without deep-diving into the working details, commonly used ML-approaches for arbitrage include (first) the Factor Model and (second) Convolutional Auto Encoders (CAE). They enable traders to make real-time decisions and optimise profits.
Phishing mailers continuously tweak their content patterns to skirt being detected by email filters. A powerful machine learning model can nevertheless detect a 2-10% variation and empower the filter to block the same. ML is used to demarcate emails based on the following filtration techniques:
- Content-based Filtering
- Sample-based Filtering
- Heuristic-based Filtering
- Memory-Based Filtering
- Adaptive Filtering
How does Google know a probable search query even before you’ve completed typing it? Search engines, like Google, consider a multi-factor approach to their Search Engine Results Page (SERP). Initially, the results in tandem with recommended SEO practices show up at the top. Based on user clicks and website sessions, bounce rate, etc. the search engines estimate the usefulness of a result. The next time a different user types a related query, only those results pop up that have been validated by ML-algorithms as being eligible.
Product Recommendation Models
Machine learning encapsulates retargeting practices that run on purchase history patterns of customers. E-commerce websites record the slightest movements of your cursor along with the products that you eventually buy (or forfeit at the payment gateway). Such data sets are factored in while running re-targeting ad campaigns, notifying customers of price drops, and recommending matching products the next time a user signs in.
Believe it or not even anomalies have a pattern, encumbered from human sight, but detectable by computers. Financial institutions bogged down with the responsibility to regulate millions of transactions each day use machine learning to determine the true nature of one-off transactions. Paypal is one company in the list of many bigwig enterprises that rely on machine learning for such purposes.
Greater strides are being made in this field than ever thought possible. Granular skill management of the most basic level is needed for job seekers to begin aligning their resume towards machine learning. NIIT’s Python programming and data exploration programme is a step in that direction. Enrol now, to build a perspective ML-career on a solid founding.
Python Programming and Data Exploration in Python
Get Ready for New age job roles by learning the programming language that is most popular for Data Analytics – Python.
Most popular programming language
Cutting Edge Curriculum