Perhaps the most popular data science methodologies come from machine learning. What distinguishes machine learning from other computer guided decision processes is that it builds prediction algorithms using data. Some of the most popular products that use machine learning include the handwriting readers implemented by the postal service, speech recognition, movie recommendation systems, and spam detectors. In this course, part of our Professional Certificate Program in Data Science, you will learn popular machine learning algorithms, principal component analysis, and regularization by building a movie recommendation system. You will learn about training data, and how to use a set of data to discover potentially predictive relationships.

The artificial intelligence (AI) industry is perpetually growing and changing, and its evolution gives rise to numerous rumors about the next "big idea." Some of the trends we see in the news become big business, like the Siri and Alexa personal assistants produced by Apple and Amazon. Others are mere flashes in the pan of AI technology, like the automated restaurants that have become interesting novelties but have hardly revolutionized the dining industry. One big trend I have been hearing about often over the past year is the idea of "generic" AI models. Generic AI is alluring to companies and consumers because it is backed by hugely powerful technology giants like Google -- in fact, a great example of generic AI is Google Vision.

Most of you would have shopped on Amazon. Now when you go into Amazon you see that there are products recommended to you. Who do u think that could have happened. So this is something known as a recommendation engine and a recommendation engine is nothing but a component of machine learning. So let say you and your friend buy similar products to a friend buys five products and you buy three product.

Learning Python can be difficult. You might spend a lot of time watching videos and reading books; however, if you can't put all the concepts learned into practice, that time will be wasted. This is why you should get your hands dirty with Python projects. A project will help you bring together everything you've learned, stay motivated, build a portfolio and come up with ways of approaching problems and solving them with code. In this article, I listed some projects that helped me level up my Python code and hopefully will help you too.

Google debuted the latest Nest Hub in March and now we're starting to see it get discounted across the web. B&H Photo currently has the second-generation Nest Hub for $80, or $20 off its normal price and a new all-time low. This model, which has new sleep-tracking features, was already a good value at $100 but this sale makes it even more tempting for those that want to expand their Google Assistant device ecosystem with a smart display. If you're unfamiliar, this is Google's answer to Amazon's Echo Show (specifically the Echo Show 8) as it provides a visual interface with which you can interact with the Google Assistant. Its three far-field mics also allow you to bark orders at the Assistant as well, but those who like to have on-screen buttons to press and pages to swipe through have the option to do so.

The recommendation engine has become quite popular across diverse industries in recent years. The recommendation engine is gaining rapid traction from OTT (Over the Top) platforms to e-commerce stores. Whether you have just started your OTT platform or plan to scale it up, recommendation engines can significantly improve your profitability. A Recommendation engine or recommendation system is an information filtering tool that provides the most relevant suggestions regarding products or services to various customers. A recommendation engine uses machine learning algorithms to collect and analyze user activities such as their preferences, search history, and others.

Smart environments powered by the Internet of Things aim at improving our daily lives by automatically tuning ambient parameters (e.g. temperature, interior light) and by achieving energy savings through self-managing cyber-physical systems. Commercial solutions, however, only permit setting simple target goals on those parameters and do not consider mediating conflicting goals among different users and/or system administrators, and feature limited compatibility across different IoT verticals. In this article, we propose a declarative framework to represent smart environments, user-set goals and customisable mediation policies to reconcile contrasting goals encompassing multiple IoT systems. An open-source Prolog prototype of the framework is showcased over two lifelike motivating examples.

Session-based recommendation aims to predict user the next action based on historical behaviors in an anonymous session. For better recommendations, it is vital to capture user preferences as well as their dynamics. Besides, user preferences evolve over time dynamically and each preference has its own evolving track. However, most previous works neglect the evolving trend of preferences and can be easily disturbed by the effect of preference drifting. In this paper, we propose a novel Preference Evolution Networks for session-based Recommendation (PEN4Rec) to model preference evolving process by a two-stage retrieval from historical contexts. Specifically, the first-stage process integrates relevant behaviors according to recent items. Then, the second-stage process models the preference evolving trajectory over time dynamically and infer rich preferences. The process can strengthen the effect of relevant sequential behaviors during the preference evolution and weaken the disturbance from preference drifting. Extensive experiments on three public datasets demonstrate the effectiveness and superiority of the proposed model.

When customers reach out to the company for service, they expect instant responses to their problems. However, a customer care provider can only cater to a certain number of cases at a time. How do you scale support? Have you thought about the customers? There are so many service horror sagas that customers have experienced like long wait lines where the agent is unavailable, one where the agent is unable to deliver answers to customer's questions, agents ghosting on a customer on call, and inconsistent answers provided by service agents.

Whenever we talk about Artificial Intelligence (AI) and Machine Learning (ML), what we instantly imagine are powerful tech companies, convenient and futuristic solutions, fancy self-driving cars, and basically everything that is aesthetically, creatively, and intellectually pleasing. What hardly gets projected to people is the real world behind all the conveniences and lifestyle experiences offered by AI. For your device to set an alarm clock just by listening to your voice, hundreds of hours of work would have gone through at the back end -- right from the time of ideation to developing prototypes and testing. Now, imagine the scale of operations and efforts behind your Netflix recommendation engines, eCommerce personalizations, home automation systems, on-demand transport, and food delivery solutions, and basically anything powered by a smartphone or an app. Today's spectrum of artificial intelligence is just like a fancy restaurant that gets marketed among people.