Google Professional Collaboration Engineer exam has been built to transform business objectives into tangible configurations, policies, and security practices as they relate to users, content, and integrations. The exam look for opportunities to educate end users and increase operational efficiency while advocating for G Suite and the Google toolset. Who should take the exam? Google Professional Collaboration Engineer (GCP) exam is suitable for IT systems administrator, cloud solutions engineer, enterprise collaboration engineer, systems engineer. Collaboration Engineers are required to leverage their understanding of their organization's mail routing and identity management infrastructure to enable efficient and secure communication and data access.
I studied Math in my undergraduate. After that I worked for Deloitte for three years as a business consultant. I wanted to be more technical so I made sure my math studies included computational challenges that required me to learn how to program. In 2013, I finished a Master's in mathematics, and left my PhD program after my first year due to personal reasons. So, in 2014 I began job search and wanted to find a job where I could bring my newfound programming skills to bear.
AI and ML strategies require foresight and planning--they shouldn't be an afterthought for your organization. Here are four best practices to help capital markets adopt and benefit from modern AI/ML technologies. When introducing new AI/ML strategies, IT leaders must ensure that they integrate and fit with existing modernization efforts, as opposed to being a bolt-on afterthought. This will lead to a true integration of AI/ML and business. In capital markets, the stakes have been raised for participants to establish value, win loyalty, and expand their share of wallet.
AI for fintech course - Early discounts and limited places The AI for fintech is a new course with limited places focused on AI design (product, development and Data) for the fintech industry. We will first explain the end-to-end principles of AI and Deep Learning and then describe specific applications and the implications of deploying them in context of fintech The course will be conducted by Ajit Jaokar and Jakob Aungiers. Outline Foundations Foundations of Enterprise AI Understanding the application of AI for fintech Introduction to TensorFlow and Keras End to end implementation for an AI application Designing an AI product Basics of Designing an AI product Understanding Deep learning Machine learning algorithms in TensorFlow and Keras: Designing with Deep Learning algorithms Multilayer Perceptron Deep Convolutional Networks Recurrent Neural Networks Reinforcement learning Natural language processing Basics of Text Analytics Deploying AI products for fintech Methodology for Enterprise AI projects Deploying Enterprise AI Understanding the Enterprise AI layer Acquiring Data and Training the Algorithm Processing and hardware considerations Business Models - High Performance Computing - Scaling and AI system Costing an AI system Creating a competitive advantage from AI Specific considerations for fintech: ex EU payment directive (PSD2) etc Course Logistics The course targets designers or developers who work with fintech. Strategic Option: You can choose to work with the strategic option (no coding) Developer Option: The full course based on development in TensorFlow and Keras.
Open source technologies are reshaping global enterprises: Incorporating open source technologies is a major contributor to digital transformation of enterprises. It is rapidly becoming the central driver of digital disruption among successful enterprises. Global companies have quickly adopted open-source frameworks to develop their apps and mobile-friendly websites. Open-source technologies have created immense opportunities for business innovation, ranging from operating systems to programming languages. In addition to transforming individual enterprises, open-source development helps reshape the entire industry.