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    • Getting Started with Video: Learning the Basics
      Getting Started with Video: Learning the Basics Matthew Pierce, Learning & Video Ambassador, TechSmith Upcoming: Jun 8 2017 3:00 pm UTC 60 mins
    • As video becomes more popular, more accessible to create, and more in demand from customers and stakeholders, it's important to learn how to start creating video. Video doesn't have to be complicated, and you don't have to become a video professional to make effective and engaging video. Learning a few of the essentials will put you on your way to make great videos.

      Join me, Scott Abel, The Content Wrangler, and my special guest, Matthew Pierce, Learning & Video Ambassador at TechSmith for this presentation. Matt will prepare you to get started with video with practical tips and steps. He will address the entire lifecycle from preproduction through distribution, and points in between to help you get with started creating videos.

      Takeways:

      What are the keys actions and components to getting started with video.
      Shooting and editing tips, tricks and techniques.
      Producing and distribution — what you need to be successful

      ABOUT MATTHEW PIERCE

      Matthew Pierce, Learning & Video Ambassador for TechSmith Corporation, is an advocate for using video for learning, marking, and communicating. Throughout Matthew's career, he has managed training, instructional design, support, public relations, social media, and video teams at TechSmith. He has also been an instructional designer. Matt regularly contributes to several online publications in the US and UK. He has an MS in instructional technology from Indiana University.

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    • How the Machine Learning Wave is Changing the Way Companies Look at Analytics
      How the Machine Learning Wave is Changing the Way Companies Look at Analytics Andrew Pease, Business Solutions SAS; Patrick Hall, Machine Learning Scientist SAS; Ben Lorica, Data Scientist O'Reilly Media Recorded: Jun 9 2016 3:00 pm UTC 58 mins
    • Machine learning is changing the way organizations look at analytics. Data scientists are being recognized as a key component in organizational analytics, but management often doesn't understand their work or know how to effectively manage them.

      Many businesses understand that analytics has moved beyond the data warehouse, and are pushing analysts and IT to grab and analyze data from new sources, even though they may not be ready to derive business value from it.

      Open source is seen as the path to machine learning innovation, despite challenges with deployment and approachable user interfaces. For organizations using or looking to adopt machine learning techniques, moving forward may be a challenge and measuring success even trickier.

      In this webcast, we will:

      -Discuss how different organizations are finding success with machine learning.

      -Look at how organizations are feeding the creativity of data scientists, making analytics accessible to business experts, and pushing the analytics closer to the data.

      -Identify how organizations are automating analytics processes in order to free up time for new analytics, new data and new business problem domains, ultimately creating real competitive advantage.

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    • Comparison of ETL v  Streaming Ingestion,Data Wrangling in Machine/Deep Learning
      Comparison of ETL v Streaming Ingestion,Data Wrangling in Machine/Deep Learning Kai Waehner, Technology Evangelist, TIBCO Recorded: Feb 15 2017 11:00 am UTC 45 mins
    • A key task to create appropriate analytic models in machine learning or deep learning is the integration and preparation of data sets from various sources like files, databases, big data storages, sensors or social networks. This step can take up to 50% of the whole project.

      This session compares different alternative techniques to prepare data, including extract-transform-load (ETL) batch processing, streaming analytics ingestion, and data wrangling within visual analytics. Various options and their trade-offs are shown in live demos using different advanced analytics technologies and open source frameworks such as R, Python, Apache Spark, Talend or KNIME. The session also discusses how this is related to visual analytics, and best practices for how the data scientist and business user should work together to build good analytic models.

      Key takeaways for the audience:
      - Learn various option for preparing data sets to build analytic models
      - Understand the pros and cons and the targeted persona for each option
      - See different technologies and open source frameworks for data preparation
      - Understand the relation to visual analytics and streaming analytics, and how these concepts are actually leveraged to build the analytic model after data preparation

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    • AI Deep Learning for Banks
      AI Deep Learning for Banks Bhagvan Kommadi, CEO, Architect Corner Recorded: Apr 11 2017 3:00 pm UTC 38 mins
    • As businesses begin to rely more on data-driven Artificial Intelligence applications, the new applications lead to new business issues, security, and privacy concerns. Each bank also needs to have a transparent system for total audit-ability so one can see who did what, and when. Bank can use AI Deep Learning techniques to identify erroneous or incomplete data to avoid misleading decisions.The new AI applications introduce a number of business, security and privacy issues which will have to be addressed. Neural Network, Natural Language Processing, Image Recognition, Speech Recognition and Sentimental Analysis techniques are Deep Learning techniques used in Banks and Financial Services. AI Deep Learning techniques are used to help with anti-money laundering programs, know-your-customer checks, sanctions list monitoring, billing fraud oversight or other general compliance functions, artificial intelligence can:

      - Improve efficiency
      - Weed out false-positive results
      - Reduce costs and increase profits.
      - Make better use of workers’ time and company resources
      - Help banks handle their compliance monitoring
      - Automate some legal and regulatory work
      - Handle most customer service and improve customer experience
      - Help in detection of Fraud
      - Creates a massive competitive advantage

      Bhagvan Kommadi, Founder, Architect Corner has around 20 years experience spanning in the creation of products & incubation of Product Startups. He has done Masters in Industrial Systems Engineering at Georgia Institute of Technology (1997) and Bachelors in Aerospace Engineering from Indian Institute of Technology, Madras (1993).Architect Corner is in CIO Advisor Top 25 Fast Growing AI startups in APAC for 2017. Architect Corner is part of Citi T4I Growth Accelerator.

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