All About Google’s Machine LearningSyah Ismail
What is Machine Learning?
Machine learning is closely related to (and often overlaps with) computational statistics, which also focuses on prediction-making through the use of computers. It has strong ties to mathematical optimization, which delivers methods, theory and application domains to the field. Machine learning is sometimes conflated with data mining, where the latter subfield focuses more on exploratory data analysis and is known as unsupervised learning. Machine learning can also be unsupervised and be used to learn and establish baseline behavioral profiles for various entities and then used to find meaningful anomalies.
Within the field of data analytics, machine learning is a method used to devise complex models and algorithms that lend themselves to prediction; in commercial use, this is known as predictive analytics. These analytical models allow researchers, data scientists, engineers, and analysts to “produce reliable, repeatable decisions and results” and uncover “hidden insights” through learning from historical relationships and trends in the data.
Cloud Machine Learning Engine brings the power and flexibility of TensorFlow to the cloud. You can use its components to select and extract features from your data, train your machine learning models, and get predictions using the managed resources of Google Cloud Platform.
Machine Learning is revolutionising every industry
Machine learning is no longer exclusive to digital companies: Almost all businesses in every industry are utilizing this technology to improve processes. Machine learning’s data-driven intelligence is permeating every corner of each industry, and it’s starting to disrupt the way we do business globally.
Machine learning will also affect the success rate of a business in a global market. Because data has no native language, organizations that are data-rich have more leverage, regardless of their locations. That changes the landscape of competition. While developing countries have begun to realize machine learning is a challenge to their demographic dividend in terms of job prospects, the developed countries can feel a broader impact as the influence is more profound.
Machine learning has evolved into a powerful capability underpinning a variety of business solutions, including curating interesting content for visitors on websites, helping movie studios learn about consumer behaviours, and even engaging with users through customer chatbots.
Industries that Machine Learning is disrupting
The National Football League (NFL) uses machine learning to gather deep insights into player movements, positions and passes to reorganize play style. In the medical sector, machine learning analyzes patients and predicts the likelihood of their returning. Even hiring and talent management in most companies is now handled by algorithms that dig out desired characteristics and, hopefully, remove biases.
Many hospitals use this data analysis technique to predict admissions rates. Physicians are also able to predict how long patients with fatal diseases can live. Similarly, medical systems are incorporating these technologies for cost-cutting measures, along with streamlining and centralizing expense reports and testing protocols.
Insurance agencies across the world are also able to predict the types of insurance and coverage plans new customers will purchase, predict existing policy updates, coverage changes and the forms of insurance (such as health, life, property, flooding) that will most likely be dominant, predict fraudulent insurance claim volumes while establishing new solutions based on actual and artificial intelligence.
4 industries that will be transformed by machine learning in 2017 are:
Perhaps the most popular application of machine learning in the consumer field of vision is in driverless cars technology. Many of these cars are currently in the testing phase, but the idea of self-driving cars on public roads is in its infancy.
As self-driving cars take to the road, it will prove essential that they are able to respond to the situations around them in real time. This means that all the information gained through the sensors must be processed in the car, rather than being submitted to a server or the cloud for analysis, which could cost crucial time.
As a result, machine learning will be central to the car’s digital infrastructure, enabling it to learn from the conditions it observes. One particularly interesting use of this data will be in mapping – while self-driving cars can have maps programmed onto them, they will need to be able to update these maps automatically in response to the real-world conditions, and the vehicle will have to learn a new navigational network on its own.
The manufacturing sector collects a huge amount of data from sensors attached to every aspect of the production line during the growth of the IoT. However, that information simply isn’t being fully utilised. As multiple parameters data is collected from complex systems, analytics can be a daunting task. The biggest application of machine learning in manufacturing will be in anomaly detection. Machine learning will be used to drive collaborative robots proof of concepts in factories that can learn by observing the production line and data streams, and be able to smartly optimise the production process to lower production costs and speed production cycles without the time and financial costs of a human having to analyse the data.
The financial industry is renowned for the vast amounts of data it holds – from transaction data to customer data, and everything in between. This volume is unlikely to decrease in the future, and the finance sector is increasingly looking to make the most of the data that it holds. To date this has been largely analysed using statistical analysis tools, however, the challenge is sorting through such a wealth of data in a timely fashion.
Financial institutions will increasingly lean on machine learning to devise new business opportunities, deliver customer services and even detect banking fraud as it is taking place. Machine learning can help to scout social media, websites and other sources, analysing unstructured data to get some additional inputs and make better decisions.
The science of online recommendations has become increasingly complex, however this will become even more nuanced as more data streams such as social media are incorporated to provide better recommendations.
If you were to browse for cat food online, at the moment, you may be able to see recommendations for cat-related products. Now, e-commerce sites will be able to use more buying trends and customer data to provide nuanced recommendations that will accurately reflect products you might like to buy.
While online retail is already experiencing the early stages of machine learning deployment, one of the most exciting things we’ll see is the application of this technology in the physical store environment. Retailers will be able to analyse customers as they walk in, and we will start to see this analysis used to help the customer find the right products and appropriate offers. With the incorporation of video analytics, retailers will be able to analyse which products people are looking at, and even where they are looking to the product – whether that’s the price, the features or the picture on the box. By considering this data against customer purchases, retailers will be able to make the best recommendations for other products that the customer may want to consider.
What company can achieve by deploying Machine Learning?
Machine learning is also being applied in recommendation engines, marketing automation, financial fraud detection, language translation, and text-to-speech applications. With businesses generating more and more data, simply navigating this growing archive of information effectively almost necessitates machines with analytical capabilities.
Machine learning is proactive and specifically designed for “action and reaction” industries. In fact, systems are able to quickly act upon the outputs of machine learning – making your marketing message more effective across the board. For example, newly obtained data may propel businesses to present new offers for specific or geo-based customers. However, data can also signify cutting back on unnecessary offers if these customers do not require them for conversion purposes.
The latter may even be a form of learning from past behaviours. Machine learning models are able to learn from past predictions, outcomes and even mistakes. This enables them to continuously improve predictions based on new incoming and different data. Many of our day-to-day activities are powered by machine learning algorithms, including:
- Fraud detection.
- Web search results.
- Real-time ads on web pages and mobile devices.
- Text-based sentiment analysis.
- Credit scoring and next-best offers.
- Prediction of equipment failures.
- New pricing models.
- Network intrusion detection.
- Pattern and image recognition.
- Email spam filtering.
Whether you’re looking for a cloud infrastructure, database, developer tool, or even machine learning APIs, Google Cloud Platform is your one-stop cloud solution.