Offered by IBM. Machine Learning is one of the most in-demand skills for jobs related to modern AI applications, a field in which hiring has grown 74% annually for the last four years (LinkedIn). This Professional Certificate from IBM is intended for anyone interested in developing skills and experience to pursue a career in Machine Learning and leverage the main types of Machine Learning: Unsupervised Learning, Supervised Learning, Deep Learning, and Reinforcement Learning. It also complements your learning with special topics, including Time Series Analysis and Survival Analysis. This program consists of 6 courses providing you with solid theoretical understanding and considerable practice of the main algorithms, uses, and best practices related to Machine Learning . You will follow along and code your own projects using some of the most relevant open source frameworks and libraries. Although it is recommended that you have some background in Python programming, statistics, and linear algebra, this intermediate series is suitable for anyone who has some computer skills, interest in leveraging data, and a passion for self-learning. We start small, provide a solid theoretical background and code-along labs and demos, and build up to more complex topics. In addition to earning a Professional Certificate from Coursera, you will also receive a digital Badge from IBM recognizing your proficiency in Machine Learning.

Watch our free Webinar with Q&A live on November 8th, 2018 at 2.30pm CET. Have you already heard about the IBM Machine Learning solution on Mainframe? If not, then this webinar is your chance to understand what it is all about. It introduces the key trends in Analytics and Data Management where machine learning represents one of the key elements. It explains Machine Learning concepts, the typical challenges encountered by Data Scientists and how many of those challenges can be addressed using the IBM machine Learning for z/OS.

Machine learning and AI are reshaping the industries. Gartner predicts that the global enterprise value derived from AI will total $1.2 trillion in 2018, which is a 70% increase from 2017. By 2022, the number will reach $3.9 trillion [1]. From IBM Watson to Google Alpha Go, enterprises have made great strides in AI research in the last couple of years. While almost all executives believe AI is the key driver of growth and success [2], they are still in the early stage of applying the technology to their businesses.

IBM is making machine learning technology available in the place where much of the world's enterprise data resides: the z System mainframe. Today Big Blue announced IBM Machine Learning, a cognitive platform for creating, training and deploying a high volume of analytic models in the private cloud. The platform draws on the core machine learning technology from its Watson Machine Learning service on its Bluemix public cloud offering. "Our mission is making data simple and accessible to clients," says Rob Thomas, general manager, IBM Analytics. "If you look at the data landscape today, over 90 percent of the data in the world today cannot be Googled. Most of that data resides behind corporate firewalls in private clouds."

IBM is making machine learning technology available in the place where much of the world's enterprise data resides: the z System mainframe. Big Blue announced IBM Machine Learning, a cognitive platform for creating, training and deploying a high volume of analytic models in the private cloud. The platform draws on the core machine learning technology from its Watson Machine Learning service on its Bluemix public cloud offering. "Our mission is making data simple and accessible to clients," says Rob Thomas, general manager, IBM Analytics. "If you look at the data landscape today, over 90 percent of the data in the world today cannot be Googled. Most of that data resides behind corporate firewalls in private clouds."

Machine learning can infuse every application with predictive power. Data scientists use these sophisticated algorithms to dissect, search, sift, sort, infer, foretell, and otherwise make sense of the growing amounts of data in our world. Fundamentally, machine learning is a productivity tool for data scientists. As the heart of systems that can learn from data, machine learning allows data scientists to train a model on an example data set and then leverage algorithms that automatically generalize and learn both from that example and from fresh data feeds. With unsupervised approaches, data scientists can dispense with training examples entirely and use machine learning to distill insights directly and continuously from the data.

IBM (NYSE: IBM) today announced IBM Machine Learning, the first cognitive platform for continuously creating, training and deploying a high volume of analytic models in the private cloud at the source of vast corporate data stores. Even using the most advanced techniques, data scientists – in shortest supply among today's IT skills1 – might spend days or weeks developing, testing and retooling even a single analytic model one step at a time. IBM has extracted the core machine learning technology from IBM Watson and will initially make it available where much of the world's enterprise data resides: the z System mainframe, the operational core of global organizations where billions of daily transactions are processed by banks, retailers, insurers, transportation firms and governments. IBM Machine Learning also for the first time deploys Cognitive Automation for Data Scientists from IBM Research to assist data scientists in choosing the right algorithm for the data by scoring their data against the available algorithms and providing the best match for their needs. The service also considers various circumstances – such as what the algorithm is needed to do and how fast it needs to produce results. Clients are beginning to see the value in IBM Machine Learning for z/OS.

IBM Machine Learning for z/OS could be a boon to big banks and insurance companies that want advanced analytics on the mainframe. Next up is the IBM Power platform. Public cloud providers have popularized machine learning with low-cost, easily accessible services, but that's a separate world from the tightly regulated, on-premises computing environments maintained by many big banks and insurance companies. Now IBM is bringing cutting-edge analytics to these mainframe customers with IBM Machine Learning (IBM ML) for z/OS. Announced February 15 in New York, IBM ML is a private-cloud-only offshoot IBM Watson Machine Learning, the public-cloud service on IBM Bluemix.

As enterprises are becoming smarter nowadays, IBM has extracted the core machine learning technology from its Watson cognitive system and brought a native platform. The new machine learning platform is designed to run at scale through open source support. IBM is planning to bring its machine learning platform first to the z System mainframe that has already been used by a large number of organisations worldwide to handle "billions of daily transactions" from banks, retailers, insurers, transportation firms and governments. The solution is claimed to support any transactional data type and work with open source technologies like Java, Python. Additionally, there is support for machine learning frameworks such as Apache SparkML, TensorFlow and H2O that all are maintained by the developer community.

IBM-Machine–Learning-Watson IBM launches machine learning for mainframe computers on the cloud for private companies.