This is an introductory course on Deep Learning. The students will get to know the evolution of deep neural network and their application in areas like image recognition, natural language processing etc.
Have you ever wondered what is Deep Learning and how it is helping today in powering Artificial Intelligence?
This basic course in Deep Learning may unravel some of them. You dont need any technical or coding background to know the basic fundamentals of Neural Network. This course is designed for functional consultants, product managers as well as developers and architects.
Contents of the course:
1. Inspiration for Deep Learning
2. Key Concepts of Deep Learning
3. Improving the model
4. Convolutional network
5. Recurrent network
6. Word representation
Who this course is for:
All Developers, Business Managers, Functional leads, AI enthusiasts
Machine Learning, coupled with the ever elusively defined field of “Artificial Intelligence” promises to revolutionise the way businesses work, cars drive, factories plan — pretty much every area of our life today. It’s a promise that the technology behemoths — Google, Amazon, Apple and Microsoft — have poured millions of dollars of research into. We know that machine learning is at the centre of key technologies such as voice recognition (Alexa, Siri), face recognition (Face ID) and autonomous driving (Tesla) — but where’s the rest of the technology application? Can we point to real world machine learning applications outside the big tech behemoths? And if not, why not?
One interesting application of machine learning is in predictive scoring — whereby an algorithm can begin to make predictions regarding an outcome. Salesforce Cloud’s Einstein product is a good example of this. Their algorithms claim the ability to predict success factors on Leads held in Salesforce Cloud. The idea here is very attractive — Einstein should be able to predict which leads are likely to close allowing Sales Managers to focus on the top 10% of their opportunities and nurture those potential clients through to closing. It should signal the end to the ‘shotgun’ approach to lead acquisition and management. Initial reviews when the tech launched back in 2016 were mixed, with some claiming the hype outstripped the reality of its usefulness. Fast forward to 2017 and the technology has picked up, with an example case study from Silverline quoting 30% higher close rates since going live with Einstein.
One of the difficulties with truly assessing the success of AI tech, however, is really understanding the driving force behind that success. Let’s imagine we take a company like Silverline who decide to embark on adding AI to their sales teams processes. The first thing that machine learning demands is data: good data and lots of it. Second to rollout is instigating change: “Listen up sales team! We’re rolling out AI to your sales software. We want you to focus on the leads that have a predicted 90%+ closure rate”. Finally, this change demands focus and adjusting the way the team operates: don’t do what you used to do, follow this systemic process because the technology demands it.
The tech goes live and close rates go up 30%. A resounding success. But pausing for a moment, and thinking objectively, can we conclusively say that the machine learning suggestions drove that improvement? Who’s to say that the mere razor sharp focus on a subset of sales (those predicted at 90%+ closure rate) wasn’t actually responsible for the improvement? Would it have mattered which leads had that focus applied or could the same level of success be attributed to the mere act of focusing on some leads and not others? How did improvements in data contribute to the success? Did the introduction of a systemic process help the chaotic sales people operate more effectively?
The real crux of the issue and certainly where this author feels we are, is that the gamut of “Artificial Intelligence” technology is very much at a handholding stage. Humans can still routinely outperform machine learning algorithms in almost every single application of the technology today. Many technologists won’t admit this but the evidence is clear — how many times does Siri fumble to understand your meaning compared to day day to conversations with other humans? Does your partner ever fail to recognise your face the way Face ID sometimes does? Does your Tesla Auto Pilot drive as well as you do? Does Salesforce Einstein outperform a seasoned sales professional?
So where does that leave us? Consensus seems to be that “AI” and particularly machine learning is currently highly effective — and impressive — at very narrowly focused tasks. This is obvious to anyone who understands how a machine learning matrix actually operates. Even deep learning is about training a network to generate predictions or outcomes that operate on a constrained set of input data, solving a highly specialised task. This is why the Salesforce Einstein technology will work well for some, and terribly for others. The training input for the model depends on the ‘law of averages’ across, presumably, all of Salesforce’s customer dataset. So if you have a slightly different sales approach it’s very difficult for an algorithm to accommodate you.
Machine Learning today, therefore remains highly impressive at highly specific tasks, such as voice recognition, facial recognition and image recognition. The question therefore is — where can this technology be applied in the real world? The answer: in highly specific tasks. And the business benefit? Automating those tasks. If it’s a highly specific, but complex task then automating that task through machine learning is the way to go. We are working with customers on projects such as classifying damage to objects and training a machine learning model to categorise and price damage using the same judgement a human operator does. How useful would it be to present a machine learning algorithm with 50 random photos of an object and for that algorithm to suggest a cost to fix it? This is completely within the realms of what is possible with machine learning today.
The best times for this nascent technology are certainly ahead. We believe that as new approaches from academia and research move to real world application, coupled with the democratisation of technology through Cloud Computing infrastructure, the number of real world machine learning applications is going to increase exponentially in the coming years.
The term machine learning was first coined in the 1950s when Artificial Intelligence pioneer Arthur Samuel built the first self-learning system for playing checkers. He noticed that the more the system played, the better it performed.
Fueled by advances in statistics and computer science, as well as better datasets and the growth of neural networks, machine learning has truly taken off in recent years.
Today, whether you realize it or not, machine learning is everywhere ‒ automated translation, image recognition, voice search technology, self-driving cars, and beyond.
In this guide, we’ll explain how machine learning works and how you can use it in your business. We’ll also introduce you to machine learning tools and show you how to get started with no-code machine learning.
Read along, skip to a section, or bookmark this post for later:
What Is Machine Learning?
Types of Machine Learning
Machine Learning Use Cases
How Machine Learning Works
Get Started With Machine Learning Tools
What Is Machine Learning?
Machine learning (ML) is a branch of artificial intelligence (AI) that enables computers to “self-learn” from training data and improve over time, without being explicitly programmed. Machine learning algorithms are able to detect patterns in data and learn from them, in order to make their own predictions. In short, machine learning algorithms and models learn through experience.
In traditional programming, a computer engineer writes a series of directions that instruct a computer how to transform input data into a desired output. Instructions are mostly based on an IF-THEN structure: when certain conditions are met, the program executes a specific action.
Machine learning, on the other hand, is an automated process that enables machines to solve problems with little or no human input, and take actions based on past observations.
While artificial intelligence and machine learning are often used interchangeably, they are two different concepts. AI is the broader concept – machines making decisions, learning new skills, and solving problems in a similar way to humans – whereas machine learning is a subset of AI that enables intelligent systems to autonomously learn new things from data.
Instead of programming machine learning algorithms to perform tasks, you can feed them examples of labeled data (known as training data), which helps them make calculations, process data, and identify patterns automatically.
Put simply, Google’s Chief Decision Scientist describes machine learning as a fancy labeling machine. After teaching machines to label things like apples and pears, by showing them examples of fruit, eventually they will start labeling apples and pears without any help – provided they have learned from appropriate and accurate training examples.
Machine learning can be put to work on massive amounts of data and can perform much more accurately than humans. It can help you save time and money on tasks and analyses, like solving customer pain points to improve customer satisfaction, support ticket automation, and data mining from internal sources and all over the internet.
But what’s behind the machine learning process?
Types of Machine Learning
To understand how machine learning works, you’ll need to explore different machine learning methods and algorithms, which are basically sets of rules that machines use to make decisions. Below, you’ll find the five most common and most used types of machine learning:
Supervised learning algorithms and supervised learning models make predictions based on labeled training data. Each training sample includes an input and a desired output. A supervised learning algorithm analyzes this sample data and makes an inference – basically, an educated guess when determining the labels for unseen data.
This is the most common and popular approach to machine learning. It’s “supervised” because these models need to be fed manually tagged sample data to learn from. Data is labeled to tell the machine what patterns (similar words and images, data categories, etc.) it should be looking for and recognize connections with.
For example, if you want to automatically detect spam, you would need to feed a machine learning algorithm examples of emails that you want classified as spam and others that are important, and should not be considered spam.
Which brings us to our next point – the two types of supervised learning tasks: classification and regression.
Classification in supervised machine learning
There are a number of classification algorithms used in supervised learning, with Support Vector Machines (SVM) and Naive Bayes among the most common.
In classification tasks, the output value is a category with a finite number of options. For example, with this free pre-trained sentiment analysis model, you can automatically classify data as positive, negative, or neutral.
Let’s say you want to analyze customer support conversations to understand your clients’ emotions: are they happy or frustrated after contacting your customer service team? A sentiment analysis classifier can automatically tag responses for you, like in the below:
In this example, a sentiment analysis model tags a frustrating customer support experience as “Negative”.
Regression in supervised machine learning
In regression tasks, the expected result is a continuous number. This model is used to predict quantities, such as the probability an event will happen, meaning the output may have any number value within a certain range. Predicting the value of a property in a specific neighborhood or the spread of COVID19 in a particular region are examples of regression problems.
Unsupervised learning algorithms uncover insights and relationships in unlabeled data. In this case, models are fed input data but the desired outcomes are unknown, so they have to make inferences based on circumstantial evidence, without any guidance or training. The models are not trained with the “right answer,” so they must find patterns on their own.
One of the most common types of unsupervised learning is clustering, which consists of grouping similar data. This method is mostly used for exploratory analysis and can help you detect hidden patterns or trends.
For example, the marketing team of an e-commerce company could use clustering to improve customer segmentation. Given a set of income and spending data, a machine learning model can identify groups of customers with similar behaviors.
Segmentation allows marketers to tailor strategies for each key market. They might offer promotions and discounts for low-income customers that are high spenders on the site, as a way to reward loyalty and improve retention.
In semi-supervised learning, training data is split into two. A small amount of labeled data and a larger set of unlabeled data.
In this case, the model uses labeled data as an input to make inferences about the unlabeled data, providing more accurate results than regular supervised-learning models.
This approach is gaining popularity, especially for tasks involving large datasets such as image classification. Semi-supervised learning doesn’t require a large number of labeled data, so it’s faster to set up, more cost-effective than supervised learning methods, and ideal for businesses that receive huge amounts of data.
Reinforcement learning (RL) is concerned with how a software agent (or computer program) ought to act in a situation to maximize the reward. In short, reinforced machine learning models attempt to determine the best possible path they should take in a given situation. They do this through trial and error. Since there is no training data, machines learn from their own mistakes and choose the actions that lead to the best solution or maximum reward.
This machine learning method is mostly used in robotics and gaming. Video games demonstrate a clear relationship between actions and results, and can measure success by keeping score. Therefore, they’re a great way to improve reinforcement learning algorithms.
Deep Learning (DL)
Deep learning models can be supervised, semi-supervised, or unsupervised (or a combination of any or all of the three). They’re advanced machine learning algorithms used by tech giants, like Google, Microsoft, and Amazon to run entire systems and power things, like self driving cars and smart assistants.
Deep learning is based on Artificial Neural Networks (ANN), a type of computer system that emulates the way the human brain works. Deep learning algorithms or neural networks are built with multiple layers of interconnected neurons, allowing multiple systems to work together simultaneously, and step-by-step.
When a model receives input data ‒ which could be image, text, video, or audio ‒ and is asked to perform a task (for example, text classification with machine learning), the data passes through every layer, enabling the model to learn progressively. It’s kind of like a human brain that evolves with age and experience!
Deep learning is common in image recognition, speech recognition, and Natural Language Processing (NLP). Deep learning models usually perform better than other machine learning algorithms for complex problems and massive sets of data. However, they generally require millions upon millions of pieces of training data, so it takes quite a lot of time to train them.
How Machine Learning Works
In order to understand how machine learning works, first you need to know what a “tag” is. To train image recognition, for example, you would “tag” photos of dogs, cats, horses, etc., with the appropriate animal name. This is also called data labeling.
When working with machine learning text analysis, you would feed a text analysis model with text training data, then tag it, depending on what kind of analysis you’re doing. If you’re working with sentiment analysis, you would feed the model with customer feedback, for example, and train the model by tagging each comment as Positive, Neutral, and Negative.
Take a look at the diagram below:
At its most simplistic, the machine learning process involves three steps:
Feed a machine learning model training input data. In our case, this could be customer comments from social media or customer service data.
Tag training data with a desired output. In this case, tell your sentiment analysis model whether each comment or piece of data is Positive, Neutral, or Negative. The model transforms the training data into text vectors – numbers that represent data features.
Test your model by feeding it testing (or unseen) data. Algorithms are trained to associate feature vectors with tags based on manually tagged samples, then learn to make predictions when processing unseen data.
If your new model performs to your standards and criteria after testing it, it’s ready to be put to work on all kinds of new data. If it’s not performing accurately, you’ll need to keep training. Furthermore, as human language and industry-specific language morphs and changes, you may need to continually train your model with new information.
Machine Learning Use Cases
Machine learning applications and use cases are nearly endless, especially as we begin to work from home more (or have hybrid offices), become more tied to our smartphones, and use machine learning-guided technology to get around.
Machine learning in finance, healthcare, hospitality, government, and beyond, is already in regular use. Businesses are beginning to see the benefits of using machine learning tools to improve their processes, gain valuable insights from unstructured data, and automate tasks that would otherwise require hours of tedious, manual work (which usually produces much less accurate results).
For example, UberEats uses machine learning to estimate optimum times for drivers to pick up food orders, while Spotify leverages machine learning to offer personalized content and personalized marketing. And Dell uses machine learning text analysis to save hundreds of hours analyzing thousands of employee surveys to listen to the voice of employee (VoE) and improve employee satisfaction.
How do you think Google Maps predicts peaks in traffic and Netflix creates personalized movie recommendations, even informs the creation of new content ? By using machine learning, of course.
There are many different applications of machine learning, which can benefit your business in countless ways. You’ll just need to define a strategy to help you decide the best way to implement machine learning into your existing processes. In the meantime, here are some common machine learning use cases and applications that might spark some ideas:
Social Media Monitoring
Customer Service & Customer Satisfaction
Stock Market Trading
Social Media Monitoring
Using machine learning you can monitor mentions of your brand on social media and immediately identify if customers require urgent attention. By detecting mentions from angry customers, in real-time, you can automatically tag customer feedback and respond right away. You might also want to analyze customer support interactions on social media and gauge customer satisfaction (CSAT), to see how well your team is performing.
Natural Language Processing gives machines the ability to break down spoken or written language much like a human would, to process “natural” language, so machine learning can handle text from practically any source.
Customer Service & Customer Satisfaction
Machine learning allows you to integrate powerful text analysis tools with customer support tools, so you can analyze your emails, live chats, and all manner of internal data on the go. You can use machine learning to tag support tickets and route them to the correct teams or auto-respond to common queries so you never leave a customer in the cold.
Furthermore, using machine learning to set up a voice of customer (VoC) program and a customer feedback loop will ensure that you follow the customer journey from start to finish to improve the customer experience (CX), decrease customer churn, and, ultimately, increase your profits.
Image recognition is helping companies identify and classify images. For example, facial recognition technology is being used as a form of identification, from unlocking phones to making payments.
Self-driving cars also use image recognition to perceive space and obstacles. For example, they can learn to recognize stop signs, identify intersections, and make decisions based on what they see.
Virtual assistants, like Siri, Alexa, Google Now, all make use of machine learning to automatically process and answer voice requests. They quickly scan information, remember related queries, learn from previous interactions, and send commands to other apps, so they can collect information and deliver the most effective answer.
Customer support teams are already using virtual assistants to handle phone calls, automatically route support tickets, to the correct teams, and speed up interactions with customers via computer-generated responses.
Association rule-learning is a machine learning technique that can be used to analyze purchasing habits at the supermarket or on e-commerce sites. It works by searching for relationships between variables and finding common associations in transactions (products that consumers usually buy together). This data is then used for product placement strategies and similar product recommendations.
Associated rules can also be useful to plan a marketing campaign or analyze web usage.
Stock Market Trading
Machine learning algorithms can be trained to identify trading opportunities, by recognizing patterns and behaviors in historical data. Humans are often driven by emotions when it comes to making investments, so sentiment analysis with machine learning can play a huge role in identifying good and bad investing opportunities, with no human bias, whatsoever. They can even save time and allow traders more time away from their screens by automating tasks.
The ability of machines to find patterns in complex data is shaping the present and future. Take machine learning initiatives during the COVID-19 outbreak, for instance. AI tools have helped predict how the virus will spread over time, and shaped how we control it. It’s also helped diagnose patients by analyzing lung CTs and detecting fevers using facial recognition, and identified patients at a higher risk of developing serious respiratory disease.
Machine learning is driving innovation in many fields, and every day we’re seeing new interesting use cases emerge. In business, the overall benefits of machine learning include:
It’s cost-effective and scalable. You only need to train a machine learning model once, and you can scale up or down depending on how much data you receive.
Performs more accurately than humans. Machine learning models are trained with a certain amount of labeled data and will use it to make predictions on unseen data. Based on this data, machines define a set of rules that they apply to all datasets, helping them provide consistent and accurate results. No need to worry about human error or innate bias. And you can train the tools to the needs and criteria of your business.
Works in real-time, 24/7. Machine learning models can automatically analyze data in real-time, allowing you to immediately detect negative opinions or urgent tickets and take action.
Get Started With Machine Learning Tools
When you’re ready to get started with machine learning tools it comes down to the Build vs. Buy Debate. If you have a data science and computer engineering background or are prepared to hire whole teams of coders and computer scientists, building your own with open-source libraries can produce great results. Building your own tools, however, can take months or years and cost in the tens of thousands.
Using SaaS or MLaaS (Machine Learning as a Service) tools, on the other hand, is much cheaper because you only pay what you use. They can also be implemented right away and new platforms and techniques make SaaS tools just as powerful, scalable, customizable, and accurate as building your own.
Whether you choose to build or buy you machine learning tools, here are some of the best of each:
Top SaaS Machine Learning Tools
Some of the best SaaS machine learning tools on the market:
Google Cloud ML
MonkeyLearn is a powerful SaaS machine learning platform with a suite of text analysis tools to get real-time insights and powerful results, so you can make data-driven decisions from all manner of text data: customer service interactions, social media comments, online reviews, emails, live chats, and more.
Just connect your data and use one of the pre-trained machine learning models to start analyzing it. You can even build your own no-code machine learning models in a few simple steps, and integrate them with the apps you use every day, like Zendesk, Google Sheets and more.
MonkeyLearn is scalable to handle any amount of data – from just a few hundred surveys, to hundreds of thousands of comments from all over the web – to get real-world results from your data with techniques, like topic analysis, sentiment analysis, text extraction, and more.
And you can take your analysis even further with MonkeyLearn Studio to combine your analyses to work together. It’s a seamless process to take you from data collection to analysis to striking visualization in a single, easy-to-use dashboard.
Take a look at this MonkeyLearn Studio aspect-based sentiment analysis of online reviews of Zoom:
Aspect-based sentiment analysis first categorizes the customer opinions by “aspect” (topic or subject): Usability, Reliability, Pricing, etc. Each comment is then sentiment analyzed to show whether it’s Positive, Negative, or Neutral. This allows you to see which aspects of your business are particularly positive and which are negative.
Other techniques, like intent classification are especially useful for incoming emails or social media inquiries to automatically show why the customer is writing. Also, at the bottom right you can see word clouds that show the most used and most important words and phrases by sentiment.
The goal of BigML is to connect all of your company’s data streams and internal processes to simplify collaboration and analysis results across the organization. They specialize in industries, like aerospace, automotive, energy, entertainment, financial services, food, healthcare, IoT, pharmaceutical, transportation, telecommunications, and more, so many of their tools are ready to go, right out of the box.
You can use pre-trained models or train your own with classification and regression and time series forecasting.
IBM Watson is a machine learning juggernaut, offering adaptability to most industries and the ability to build to huge scale across any cloud.
Watson Speech-to-Text is one of the industry standards for converting real-time spoken language to text, and Watson Language Translator is one of the best text translation tools on the market.
Watson Studio is great for data preparation and analysis and can be customized to almost any field, and their Natural Language Classifier makes building advanced SaaS analysis models easy.
See products page for pricing.
Google Cloud ML
Google Cloud ML is a SaaS analysis solution for image and text that connects easily to all of Google’s tools: Gmail, Google Sheets, Google Slides, Google Docs, and more.
Google AutoML Natural Language is one of the most advanced text analysis tools on the market, and AutoML Vision allows you to automate the training of custom image analysis models for some of the best accuracy, regardless of your needs.
Google Cloud AI and ML pricing
Top Open Source Libraries for Machine Learning
Open source machine learning libraries offer collections of pre-made models and components that developers can use to build their own applications, instead of having to code from scratch. They are free, flexible, and can be customized to meet specific needs.
Some of the most popular open-source libraries for machine learning include:
Scikit-learn is a popular Python library and a great option for those who are just starting out with machine learning. Why? It’s easy to use, robust, and very well documented. You can use this library for tasks such as classification, clustering, and regression, among others.
Developed by Facebook, PyTorch is an open source machine learning library based on the Torch library with a focus on deep learning. It’s used for computer vision and natural language processing, and is much better at debugging than some of its competitors. If you want to start out with PyTorch, there are easy-to-follow tutorials for both beginners and advanced coders. Known for its flexibility and speed, it’s ideal if you need a quick solution.
Launched over a decade ago (and acquired by Google in 2017), Kaggle has a learning-by-doing philosophy, and it’s renowned for its competitions in which participants create models to solve real problems. Check out this online machine learning course in Python, which will have you building your first model in next to no time.
The Natural Language Toolkit (NLTK) is possibly the best known Python library for working with natural language processing. It can be used for keyword search, tokenization and classification, voice recognition and more. With a heavy focus on research and education, you’ll find plenty of resources, including data sets, pre-trained models, and a textbook to help you get started.
An open-source Python library developed by Google for internal use and then released under an open license, with tons of resources, tutorials, and tools to help you hone your machine learning skills. Suitable for both beginners and experts, this user-friendly platform has all you need to build and train machine learning models (including a library of pre-trained models). Tensorflow is more powerful than other libraries and focuses on deep learning, making it perfect for complex projects with large-scale data. However, it may take time and skills to master. Like with most open-source tools, it has a strong community and some tutorials to help you get started.
Monkeylearn is an easy-to-use SaaS platform that allows you to create machine learning models to perform text analysis tasks like topic classification, sentiment analysis, keyword extraction, and more.
MonkeyLearn offers simple integrations with tools you already use, like Zendesk, Freshdesk, SurveyMonkey, Google Apps, Zapier, Rapidminer, and more, to streamline processes, save time, and increase internal (and external) communication.
Take a look at the MonkeyLearn Studio public dashboard to see how easy it is to use all of your text analysis tools from a single, striking dashboard. Play around and search data by date, category, and more.
Ready to take your first steps with MonkeyLearn’s low-code, no code solution?
Request a demo and start creating value from your data.
Machine learning is at the helm of social media, self-driving cars and more daily applications.
Machine learning, in which a computer simulates human thinking by using data models to recognize patterns and make predictions, is being applied in nearly every industry.
Indeed, machine learning examples are numerous, and they can be found in fields ranging from healthcare and banking to marketing and sports. The list of machine learning applications below will give you an idea of how the technology is used on a daily basis.
MACHINE LEARNING APPLICATIONS TO KNOW
Social media personalization.
Business intelligence optimization.
TV, movie and video recommendations.
Machine Learning and Image Recognition
Face ID authentication by Apple utilizes machine learning to carry out image recognition and unlock mobile devices. Apple’s biometric technology is powered by Vision, a deep learning framework which is able to detect the features of users’ faces and quickly match them to previous device records. The Vision framework can also be used to detect barcodes, text and landmarks through device cameras.
Microsoft’s Azure, a cloud platform of over 200 services, is utilizing its machine learning and DevOps features to fight against animal extinction in the Wild Me project. The Wild Me open-source platforms help researchers track, document and conduct analyses on wildlife population data based on animal photographs, overall accelerating research endeavors that may be understaffed or underfunded.
Waymo’s self-driving vehicles use machine learning sensors to crunch surrounding environment data in real time and help guide vehicle responses when faced with various situations, from a red light to a human walking across the crosswalk.
Online reviews site Yelp relies on machine learning to sort through tens of millions of photos users upload to its site and then uses the technology to group them into various categories, such as, food, menus, inside the establishment or outside photos.
Amazon Web Services
Amazon’s cloud service AWS provides Amazon Rekognition, which uses machine learning to automatically identify objects, people, text, and activities in both images and videos. AWS also offers free machine learning services and products to help developers and data scientists build, train and deploy customized machine learning models.
Blue River Technology
Blue River Technology, an agriculture tech company, grafts together machine learning and computer vision to differentiate between crops and weeds, as well as achieve proper spacing between plants. The company’s See & Spray rig targets specific plants and sprays them with herbicide or fertilizer.
Machine Learning and Speech Recognition
Duolingo, the language learning app, incorporates machine learning-based speech recognition to gauge a user’s spoken language skills. The closer a user’s pronunciation is to native speaker data stored in Duolingo’s system, the higher the user will be scored during speaking and conversational lessons.
Google Translate can detect and switch between languages seamlessly, thanks to the Google Neural Machine Translation (GNMT) system, which is powered by machine learning and recurrent neural network technology.
Using language datasets, the GNMT system can train models how to input, output and compare words and phrases between languages, making translation faster and more accurate over time. Google is continuing to use this technology to allow feats like text translation from images and under-resourced language translation.
Etsy, whose online marketplace platform for users to buy and sell products, applies machine learning to personalize the shopping experience, providing customized product recommendations and ads based on previous purchases or product searches.
A provider of AI-powered technology for pathology research, PathAI helps healthcare professionals measure the accuracy of diagnoses and the efficacy of complex diseases. Using predictive machine learning, the company’s technology can be used to make medicinal solutions more accurate, reproducible and personalized based on patient history.
Fit Analytics, which helps consumers find the right sized clothes, uses machine learning to make recommendations on the best-fit styles. It also uses the technology to assist brands in gaining insights into their customers from popular styles to average customer measurements.
In a process called collaborative filtering, Netflix uses machine learning to analyze the viewing habits of its millions of customers to make predictions on which media viewers may also enjoy. Recommendations are based on those predictions and determine what shows, movies and videos will display on the homepage and watch-next reel of each user.
NielsenIQ’s Label Insight platform manages a gargantuan database of product nutrients, product ingredients and product claims. Its product metadata platform uses machine learning to give a personalized view of each food product, such as ingredients, suppliers, and supply chain history, which helps customers decide whether to purchase an item.
Social media giant Twitter relies on machine learning to prioritize tweets that are the most relevant to every user. Twitter’s machine learning ranks tweets with a relevance score based on what you engage with the most and other metrics. High-ranking tweets based on similar engaged posts are placed at the top of feeds, so users are more likely to see them.
Quora, a social media question and answer website, uses machine learning to determine which answers are pertinent to your personal search queries. The company ranks answers based on results from its machine learning, such as thoroughness, truthfulness and reusability, when seeking to give the best response to a question.
Machine Learning and Finance
Financial institution Capital One uses machine learning to detect, diagnose and remediate anomalous app behavior in real time. It also uses the technology as part of its anti-money laundering and fraud tactics to adapt quickly to changes in criminals’ behaviors.
Deserve, a fintech company that lets institutions build and launch their own credit card and cryptocurrency programs, uses machine learning for its security, event management and compliance features. On personal platforms, the security feature analyzes event logs to detect user account patterns and quickly alert of any abnormalities.
Trading Technologies, a futures trading platform, uses machine learning to track analytics and identify trading behavior that could result in regulatory inquiries.
Machine Learning and Business
McDonald’s Global Technology
Tech is big at McDonald’s, which has been working to develop applications for new technology in the food and beverage industry. The company continues to push the boundaries of how AI and machine learning can optimize the process of making and serving food, using machine learning to automate order taking and to predict what menu items will sell the best at drive-thru windows.
Adtech company Yieldmo offers the Yieldmo Smart Exchange: a “global omnichannel exchange” for ad content. Different ad buyers have different KPIs, and Yieldmo’s predictive analytics are geared toward curating ad inventory to serve specific performance indicators. The exchange uses machine learning to analyze contextual ad data and pair ad publishers and buyers, with the goal of maximizing monetization and performance according to ad spend.
RS21 is a mission-driven data company that collects and analyzes information to create data-driven recommendations for clients in the healthcare, government and space infrastructure domains. Its main offering is the proprietary AI engine MOTHR, which serves as a replacement for data platforms that are challenging to scale. MOTHR’s comprehensive AI product development system includes a data warehouse and lakes, an AI solution library and apps for data visualization and projection.
Data analytics company Civis Analytics helps businesses make informed decisions. AI-powered business predictions and analytics are developed to give companies insights about how to identify, attract and engage their customers best.
Fashion retailer ASOS uses machine learning to determine the customer lifetime value (CLTV). This metric estimates the net profit a business receives from a specific customer over time. Machine learning aids ASOS in determining which customers are likely to continue buying its products and which customers are likely to have low CLTV, which in turn could affect ASOS offering them free shipping or other promotions.
Marketing, sales and service business software provider HubSpot uses machine learning in a number of ways. It gives content marketers insight into what search engineers associate their content with to assigning predictive lead scores for sales teams to use when assessing which customers are ready to purchase their products.