AI & Machine Learning

You should know about Artificial Intelligence and Machine Learning in the healthcare industry and how it will impact our future. These technologies WILL dramatically change the way we work in healthcare. As the use of Machine Learning grows in healthcare, continue to obsess over the privacy of your customer data. Making “cool” innovations in Artificial Intelligence or Machine Learning won’t work if not coupled with a relentless pursuit to serve the customer. These endeavors are expensive, so spend your IT budget wisely, ensuring new innovation creates true value and is easy for the end user.

AI-based technology will fundamentally change economies, politics, the planet, and indeed humanity. Even today we are only just beginning to see some of these changes come to fruition. For better or for worse, society will be permanently altered due to artificial intelligence. Just think of the dramatic changes we’ve witnessed just in our own lives as the age of the Internet has disrupted the landscape. Given the dramatic pace of innovation today, one can’t help but wonder what humanity might look like in a few decades as compared to today. How will we, as a society, fare in the brave new world of tomorrow?

Learn the process of building a predictive machine learning model, deploying it as an API to be used in applications, testing the model and retraining the model with feedback data. In this post, the famous Iris flower data set is used for creating a machine learning model to classify species of flowers. In the terminology of machine learning, classification is considered an instance of supervised learning, i.e. learning where a training set of correctly identified observations is available. Following the steps, you will deploy your model as an API, test it and retrain by creating a feedback data connection.

When the concept of AI was first introduced, the HR departments were not completely convinced by it, as they feared a heavy loss in the number of jobs because of the increased dependence on machines. But gradually the organizations have opened up to it. Technologies and tools like cloud computing, business analytics, e-recruitment, CPM (Computerized Performance Monitoring) have minimized the labor of HR personnel and given them considerable time to focus on other goals. Now the question arises, that if AI is such a convenience for the HR, then what is the debate all about?

The trend of evolving cyberattacks doesn’t seem to have slowed down. Instead of creating new malware, attackers have started to upgrade existing variants by configuring them with the right threat evasion parameters. In 2018, it’s clear that companies need to adopt a high-level cyber security mechanism to keep their data safe and secure.By combining an organization’s IT department with an advanced cybersecurity framework, AI is just what organizations need to prevent increasingly complex cyberattacks. Many CIOs and CISOs have already begun to incorporate artificial intelligence (AI) into their organization’s cybersecurity plan.

There is a theorem telling us there is no single machine learning method that performs best in all problems. So how do we find the best one that fits our needs? This post suggests that before going into complex methods and spending time on fine-tuning your deep learning model, try simple ones. As you gear up towards more complex methods, you may find that simple one is sufficient for your needs. No matter how complicated or simple a method is, it will not perform best for all the problems.

Ready to learn Machine Learning? Browse courses like Machine Learning Foundations: Supervised Learning developed by industry thought leaders and Experfy in Harvard Innovation Lab. “I visualize a time when we will be to robots what dogs are to humans, and I’m rooting for the machines.” This quote from American mathematician, electrical engineer and cryptographer, Claude Shannon, sums up

Ready to learn Machine Learning? Browse courses like Machine Learning Foundations: Supervised Learning developed by industry thought leaders and Experfy in Harvard Innovation Lab. Optimization refers to the task of minimizing/maximizing an objective function f(x)parameterized by x. In machine/deep learning terminology, it’s the task of minimizing the cost/loss function J(w) parameterized by the model’s parameters w∈Rdw∈Rd. Optimization algorithms (in case of minimization) have

Ready to learn Machine Learning? Browse courses like Machine Learning Foundations: Supervised Learning developed by industry thought leaders and Experfy in Harvard Innovation Lab. Cognitive search, widely accepted as the next evolution of enterprise search, offers the potential for dramatic improvements in the accuracy, relevance, and efficiency of insight discovery. Although some see cognitive search as simply

Ready to learn Machine Learning? Browse courses like Robotics Application Machine Learning developed by industry thought leaders and Experfy in Harvard Innovation Lab. The artificial intelligence (AI) revolution is upon us: from Siri to facial recognition to self-driving cars, there’s no ignoring AI’s involvement in our everyday lives. Automation, which once started as a desire to make

Ready to learn Machine Learning? Browse courses like Machine Learning Foundations: Supervised Learning developed by industry thought leaders and Experfy in Harvard Innovation Lab. Are you into Machine Learning OR are you “just” a Statistician? Have you been asked this question yet? If you are in a career or looking to get into one that has anything