"What do I need to know about AI and what's the best way to learn it?" I've invested a considerable amount of time taking numerous courses, so I dug into my emails to collect some of the suggestions I've doled out. First, it's worth addressing the extent to which a product manager even needs to understand how AI works in order to be effective. There is an endless stream of business articles about what AI is, what it does and how it is going to disrupt this and that, all of which is great, but I am talking about understanding how it works (e.g. As Marty Cagan pointed out in Inspired (a must-read), product managers can come from a variety of different vertical disciplines, including those that are not necessarily technical, such as marketing or sales.

In case if you are not doing that specialization then you can just do this course to learn about TensorFlow and how you can use it for Artifical Intelligence and Machine Learning.

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself.

Education through machines is a reality. From industry to business and to education now, it is time to lose our reservations and embrace artificial intelligence. Teachers can see the limitations that technology can't or doesn't observe. However, there is no scalable way for students to closely engage with teachers as class sizes grow. This leads to gaps of knowledge between the lessons teachers submit to their administrators and the principles students are able to absorb.

In a wide-ranging discussion today at VentureBeat's AI Transform 2019 conference in San Francisco, AWS AI VP Swami Sivasubramanian declared "Every innovation in technology is going to be driven by developers." Sivasubramanian made the statement while talking about growing demand for machine learning engineers and internal efforts at Amazon to train more employees to use machine learning. Facebook VP Jérôme Pesenti also stressed plans to make machine learning part of each employee's job at the company. And earlier today, Amazon committed $700 million to upskilling its U.S. workers. "Amazon developed what we call Machine Learning University. This is what we use to train our own engineers on machine learning even if they didn't take it as part of their own university [course work]," Sivasubramanian said.

In this video you will learn how to practically apply machine learning. We are showing an example of how to use our image classification service via an easy-to-use web service. More information about SAP Cloud Platform and its services can be found at https://cloudplatform.sap.com.

Your study material will be available to you on Imarticus's Learning Management System, which is a fully integrated state-of-the-art learning management system for an extended duration of 7 months. You will need to log in to the learning portal using the credentials provided and navigate through the portal as required.

Looking to get started with AI but don't know how? NVIDIA has just published a new self-paced Deep Learning Institute course that uses the newly released Jetson Nano Developer Kit to get up and running fast. In the course, students will learn to collect image data and use it to train, optimize, and deploy AI models for custom tasks like recognizing hand gestures, and image regression for locating a key point in an image. The Jetson Nano Developer Kit is a powerful, easy-to-use, mini AI computer that lets users run multiple neural networks in parallel. The device is perfectly suited for image classification, object detection, segmentation, speech processing, and more. Students will use Jupyter notebooks on the Jetson Nano to build a deep learning classification project with computer vision models.

It feels impossible to keep up with every new concept and technology in data science and machine learning. You have multiple languages, libraries and design principles. We have written pieces on different resources that can help data professionals keep up to date with all the various technologies. However, many of these courses cost money. But coursera offers an opportunity to take online courses for free from actual colleges and educational institutions. This allows you to get deeper understanding of concepts like machine learning, deep learning, statistics, etc.

Every organization wants to infuse the power of AI in its business. In this second of two parts we'll explore the productivity, performance and cost considerations that should be weighed when deploying deep learning at scale in the enterprise, and how to avoid the pitfalls of short-term decisions that can negatively impact your long term AI strategy.