Want to make sense of the volumes of data you have collected? Need to incorporate data-driven decisions into your process? This course provides an overview of machine learning techniques to explore, analyze, and leverage data. You will be introduced to tools and algorithms you can use to create machine learning models that learn from data, and to scale those models up to big data problems. At the end of the course, you will be able to: • Design an approach to leverage data using the steps in the machine learning process.

AI models can benefit as much from soft data such as personal anecdotes as much as hard data. It's well known among data science circles that the more diverse your set of training data, the more accurate your model will be. This includes structured, unstructured, and semistructured data. However, not all data is treated equally, especially when it comes to unstructured data. Soft data such as collective memory and personal anecdotes can be challenging to access, but they can help build better decision-making systems.

The race to Industry 4.0 and accelerated adoption of digital automation are pressure testing organizations across industries. It is becoming increasingly matter-of-fact that an enterprise's ability to leverage data is a key source of competitive advantage--this principle especially holds true when it comes to building and maintaining computer vision applications. Visual automation models are powered by images and videos that capture a digital representation of our physical world. In most enterprises, media is captured across multiple sensor edge devices and lives in siloed source systems, making the integration of media across environments a core challenge that needs to be solved when building a computer vision system that seeks to automate visual inspection. Media becomes even more important in a world where model architectures (the code that builds up the neural networks on which an AI model is trained) are increasingly commoditized and stable.

MotorDNA seeks to close the data gap between the Insurers, Consumers, Auto Lenders, Fleet Managers, Regulators, and OEMs with a platform to encourage the production and demand for safer vehicles. The US Automobile Insurance industry, valued at greater than $300B, is modernizing and transforming its products to mirror the transformation in the mobility space. Automobile technology is rapidly advancing, and vehicles are getting smarter. Safety features, sometimes referred to as ADAS (Advanced Driver Assistance Systems) and their effectiveness are progressing quickly on the journey to autonomous driving. However, vehicle build data is neither readily available in the market nor has it been organized to make it actionable for insurers to create products and pricing based on accurately assessing the risk of each vehicle.

MotorDNA seeks to close the data gap between the Insurers, Consumers, Auto Lenders, Fleet Managers, Regulators, and OEMs with a platform to encourage the production and demand for safer vehicles. The US Automobile Insurance industry, valued at greater than $300B, is modernizing and transforming its products to mirror the transformation in the mobility space. Automobile technology is rapidly advancing, and vehicles are getting smarter. Safety features, sometimes referred to as ADAS (Advanced Driver Assistance Systems) and their effectiveness are progressing quickly on the journey to autonomous driving. However, vehicle build data is neither readily available in the market nor has it been organized to make it actionable for insurers to create products and pricing based on accurately assessing the risk of each vehicle.

Where does your enterprise stand on the AI adoption curve? Take our AI survey to find out. Join manufacturing leaders at Transform 2021, the industry's premier AI digital event, hosted July 12-16. Transform gathers thought and action leaders from a range of manufacturing and industrial companies seeking to move into the future of work using AI for highly informative sessions, focused roundtables, and unbeatable networking opportunities. Register today to join manufacturing leaders at Transform 2021.

The mathematician Clive Humby famously said, "data is the new oil." Many in financial services agree with that assertion as they look at the mountains of data financial institutions have amassed. However, they struggle with finding ways to tap this new oil and turn it into profits. There isn't a simple process to extract data from disparate systems, refine it and develop a product for sale. Since Amazon, Netflix, Google, Apple, Facebook, and other big tech companies have successfully used data to personalize their products and drive innovation, financial institutions have focused on personalization as the main use case for data.

Data Intelligence provides more than analytics to business. It helps a company in making data the core of its activities and drive disruptive transformation. Deep data science technologies like AI, Machine Learning and Deep learning, etc., experienced a number of hype cycles in the past. In the meantime, big data analytics took over the headlines. But the buzz around "big data analytics" dwindled in some time. We are churning out an enormous amount of data each day.

Big data and machine learning have emerged as fundamental tools for companies and cities looking to unlock insights into people and places, enabled by the myriad connected devices that now permeate society. The likes of PredictHQ leverage data from global events to help companies like Uber and airlines forecast demand, while fellow San Francisco startup Streetlight Data aggregates mobile app data to help cities measure the flow of traffic. In a similar vein, Israeli startup Waycare has been setting out to help city planners make better-informed traffic-management decisions. It does so by tapping historical and real-time data inputs from connected cars, telematics, roadside cameras, fleet management platforms, public transit services, construction projects, and even weather forecasts to build a more complete picture of what's happening on a city's thoroughfares. Individually, these various data sets can do little to help municipalities make planning decisions, but the sum -- as the saying goes -- is far greater than its parts.

Increasingly, businesses are using ML to strengthen their competitive advantage and drive innovation. Is your organisation embracing this shift or are you falling behind? If you are on the "bias-for-action" side of the scale and have already started steering your organisation towards digital & ML transformation, are you confident you are doing so in the right way? Over the past decade, data has become increasingly important and has even been described as the "new oil". Organisations with extensive user data can leverage data to increase sales and customer retention.