We encounter it in one way or another in our day to day lives. Machine learning is actually very closely related to ai or artificial intelligence. Ibm. The international technology business is powered by machine learning. Ibm says that machine learning is a branch of artificial intelligence focused on building applications that learn from data and improve their accuracy over time, without being programmed to do so when we think of any situation now in our day to day lives where our technology is making Recommendations or making predictions based on our activities it’s likely that machine learning is involved in these processes. To some degree, there are even more levels to machine learning, one of which being deep learning. Deep learning aims to understand how the human brain learns and requires vast amounts of data that will be interpreted to reach suitable conclusions. Some of the areas that deep learning will be applicable is fascinating and will shape the ways that we interact with technology in the future. In this video i’ll be discussing some of the ways that machine learning is applied to our day to day lives. Take an in depth. Look at how machine learning is impacting digital marketing and then consider whether this is all stuff that we need to be concerned about. As with all of our videos, make sure you click the like button, if you learned something new and subscribe to see more videos like this also don’t forget to leave a comment.

If you have any questions and we’ll get back to you as soon as we can, we live in a digital technological age, where it’s almost impossible to go for an hour, let alone a whole day without some kind of hands on interaction with a digital device. The fact that digital devices are ever present in our lives means that our dependency on them has grown. We need digital technologies in our lives and now because of our dependency, we need instantaneous access to information much faster than we ever did before, and this need is only growing, enter machine learning the solution to providing useful and relevant information fast to improve our quality of Life by making it easier and ultimately safer, there are so many things that we do now every day that need machine learning. Digital assistants, like apple’s siri, google’s assistant or amazon’s alexa, use machine learning to interpret speech and make recommendations based on queries. Speaking of recommendations, any service that we use that makes suggestions and recommendations based on our activities, uses machine learning, including things like netflix, recommending a film to watch based on our activities, amazon, recommending things to buy or even starbucks, suggesting a coffee to drink banks, deploy machine Learning to keep our finances safe and make banking faster and easier for customers. They can more accurately identify cases of fraud, make decisions on loans and credit cards and even allow us to quickly and easily deposit checks using mobile apps without needing to leave the house machine.

Learning also flags potential plagiarism on essays and articles that already exist. Machine learning makes online a safer space through improved cyber security, identifying potential threats and improving response times. It also helps us to commute faster by using real time data on maps to provide accurate journey times and alternative routes. Speaking of transport, self driving cars need machine learning and, more specifically, deep learning to safely operate and account for the often seemingly random and unexpected behaviors of other road users. A huge part of machine learning is understanding and interpreting incomprehensible amounts of data to identify trends. The more data that’s available, the better machine learning becomes. This is why we see such huge improvements in this space. Almost every year, like digital assistants, becoming much better at answering a question and self driving cars being one step closer to being on the roads in force. Machine learning goes much further than just making recommendations and is now at the stage where it impacts almost every element of our experience online. In what information we see and how we interact with businesses and other online users, marketers can use machine learning data to better inform their decisions and take advantage of machine learning opportunities to offer more relevant messaging to current customers and future prospects. One example of how machine learning helps marketers is through contextual online ads. Marketers can use machine learning data through advertising platforms such as google ads to better target placement of their ads on relevant websites across the display network.

Chat, bots also make use of machine learning to improve user experience on websites and help website visitors, reach satisfactory outcomes to queries without people ever needing to get involved. Machine learning can also improve email marketing campaigns by helping to decide what messaging will be more suitable. How an audience should be segmented to have the best impact and when the best time to send a campaign is marketers can also use a b or split testing in email marketing platforms that also uses machine learning. Machine learning also helps defend email, inboxes from unwanted junk mail through sophisticated spam filters, and can offer suggestions on how to reply to emails through smart suggestions. Social media platforms use machine learning to determine the best content to deliver to its users. Marketers can use social listening tools to identify trends across platforms to deliver content that will reach the right audiences, using machine learning to combat machine learning, some of the best machine learning social listening platforms out. There include awario, agora, pulse and tweetdeck check and see. If you already have access to a social listening tool, as some platforms already have social listening tools built into them like sem rush and its brand monitoring tools of all tools available to marketers, google has the widest need for an implementation of machine learning. We’Ll be looking at how google uses machine learning across their various services and platforms in an upcoming video. Keep in mind that when we’re talking about machine learning that it can’t exist or function without data, machine learning, insights and functionality is all based on data.

The more data your business provides and accesses the more likely your marketing efforts will be successful. Feedback is also an important part of machine learning without some sort of feedback, be it clicks, conversions or other data points. A machine learning system cannot improve its accuracy over time. Success is important in digital marketing, as this will mean a higher return on investments for your business, as machine learning becomes used across more and more industries that are making use of data that’s available to them. There’S growing concern over how easy it is to access this data in the first place. The main focus of this concern is around privacy and how easy it is for businesses to get data on people that can be used to understand their habits both online and offline. It feels as though there’s an endless collection of data from every business where they have extremely long service agreements where users sign over access to usage data in order to be able to access a service. Take, for example, services like netflix or even any of google services. There’S so much data available that businesses can now identify when it’s likely that people are going to buy a house as they can identify small changes in spending habits. This would never have been possible without machine learning being able to identify these trends. This could be a large reason behind why so many people every year are choosing to use private search engines like duck.

go, where privacy is a core element of the search engine. Data has quickly become one of the most important assets to a business. The more data that a business has access to the more it can use machine learning to make sense of that data to implement better services that will be more relevant and ultimately more useful to its end users. Machine learning is here to stay and is only going to get more sophisticated. As time goes on, we would expect algorithms to become more complex. Google have already said that they don’t fully know how their ranking signals work for seo. As there is an element of machine learning involved in the process, this is only going to become more complex in the future, so we expect that seo will become even more complex as a result, what’s more with more advanced machine learning. We also expect personalization and more sophisticated advertising targeting to be available to more businesses. Then, of course, there are robots, machine learning and more intelligent computing systems only mean one thing: the rise of intelligent robots but we’ll save that discussion for another video.

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