Natural Language Processing
Python SciPy, Pandas, etc
Azure Machine Learning API
and many more..
Dr. Michael Brand is the Head and Founder of Otzma Analytics, a Data Science consultancy dedicated to maximizing clients’ value from data by providing analytics upskilling, project review, and executive mentoring. Before founding Otzma in 2018, Dr. Brand served as Chief Data Scientist at Telstra, as Senior Principal Data Scientist at Pivotal, as Chief Scientist at Verint Systems, and as CTO Group Algorithm Leader at PrimeSense (where he worked on developing the Xbox Kinect). Dr. Brand also served as Director of the Monash Centre for Data Science in his role as Associate Professor for Data Science and AI at Monash University, where he remains an adjunct. Dr. Brand holds a Ph.D. in IT from Monash University, an MSc in Applied Mathematics from the Weizmann Institute of Science, and a BSc in Engineering from Tel-Aviv University. He has made industry-defining contributions that have earned him 18 patents (more pending), garnered many prestigious industry and academic awards, and power flagship products for the companies he worked with.
Dr Denis Bauer is an internationally recognised expert in artificial intelligence, who is passionate about improving health by understanding the secrets in our genome using cloud-computing technology. She is CSIRO’s Principal Research Scientist in transformational bioinformatics and adjunct associate professor at Macquarie University. She keynotes international IT, LifeScience and Medical conferences and is an AWS Data Hero, determined to bridge the gap between academe and industry. To date, she has attracted more than $31M to further health research and digital applications. Her achievements include developing open-source bioinformatics software to detect new disease genes and developing computational tools to track, monitor and diagnose emerging diseases, such as COVID-19.
Dr. Nathan’s accomplishments resulted in him being named as one of Australia’s Most Innovative Engineers by Engineers Australia & as one of Australia’s and the US’ Top Ten Young Scientists by Popular Science magazine, along with receiving a number of other international awards and recognitions. Dr. Nathan is the Founder | CTO at Presien – cutting edge AI vision systems, a Special Advisor for Robotics | Ventures at one of the world’s larger private construction companies, a Director of the Robotics Australia Group peak body & sit on the Advisory Board of Queensland Robotics. He is an active academic researcher in robotics as an Honorary Professor at the Ohio State University. Previously he has served multiple academic appointments at Stanford University and the University of Technology Sydney. Dr. Nathan’s speciality is uncovering and imagining opportunities for emergent future technologies in the real world and forging viable R&D to Delivery pathways to their realisation. One of his multi-award winning portfolio projects – Blindsight by Presien (formerly Toolbox Spotter) AI computer vision for heavy industries – recently evolved into a $7m VC funded spinoff. He has over 15+ years in industry, and 10+ years in academia, initiating, shaping, driving and leading cutting-edge, research driven disruptive innovation.
Aditya is a tech enthusiast with more than 7 years of experience across various technologies in data science, machine learning, deep learning and computer vision. He has completed his Masters in Data Science from the National University of Singapore. He has worked across various domains including automotive, banking, retail among others consulting various clients around the globe. He is a true believer of ‘You got to see it work to know it works’ and sets goals towards achieving the same in any of the endeavours he undertakes. Being highly inclined towards technology, he founded Xaltius Pte. Ltd in Singapore which has a major focus on building solutions in Data Science and AI and educating students and professionals in the same areas. He also founded Code for India which specializes in delivering top notch skills in Data Science and AI as required in the industry today. Apart from work, he loves to engage with kids and get involved in social work.
Mathangi is a renowned data science leader in India. She has 11 Patent grants and 20+ patents published in the area of intuitive customer experience, indoor positioning and user profiles. She has recently published a book – “Practical Natural Language Processing with Python” She has 17+ years of proven track record in building world-class data sciences solutions and products. She is adept in machine learning, text mining, NLP technologies & tools. She is currently heading the data organization of GoFood, Gojek. In the past, she has built data sciences teams across large organizations like Citibank, HSBC, GE, and tech startups like 247.ai, PhonePe. She advises start-ups, enterprises, and venture capitalists on Data Science strategy and roadmap. She is an active contributor on machine learning to many premier institutes in India. She is recognized as one of “The Phenomenal SHE” by Indian National Bar Association in 2019.
NLP in Ecommerce(Tutorial)
Juan Kanggrawan is the current Head of Data Analytics at Jakarta Smart City. His key role is to fully utilize data to formulate public policy and to improve the quality of public services. Juan is currently working on several city-scale strategic analytics initiatives. He is actively analyzing complex, diverse and exciting urban data on a daily basis: citizen complaint/aspiration, transportation/mobility, health (COVID-19), CCTV, Open Data, weather-flood-river bank, subsidy utilization, food commodities price elasticity, etc. He is also developing and aligning a strategic partnership framework between Jakarta Smart City with other government agencies, business enterprises, research agencies, and universities.
Raghav Bali is a Senior Data Scientist at Optum(United HealthGroup), one of the world’s largest health care organizations. With about a decade’s experience working across Fortune 500 organizations such as Intel and American Express, his work involves research & development of enterprise-level solutions based on Machine Learning, Deep Learning, and Natural Language Processing for real-world use-cases. Raghav has published multiple peer-reviewed papers, has authored over 7 books, and is a co-inventor of multiple patents in the areas of machine learning, deep learning, and natural language processing.
Rowel Atienza is a Professor and Scientist at the Electrical and Electronics Engineering Institute of the University of the Philippines, Diliman. He holds the Dado and Maria Banatao Institute Professorial Chair in Artificial Intelligence. He received his MEng from the National University of Singapore for his work on an AI-enhanced four-legged robot. He finished his Ph.D. at The Australian National University for his contribution on the field of active gaze tracking for human-robot interaction. Dr. Atienza is the author of Advanced Deep Learning with TensorFlow 2 and Keras. His current research work focuses on robotics, computer vision and AI.
During her Bachelor of Economics in Buenos Aires, Argentina, Maggie learned to see the world from the lens of mathematics and statistics. She then started teaching herself how to code out of curiosity, got a job as a Junior Software Engineer in Sydney, Australia, and went on to do a Master of Software Development to further develop her skills. She completed her Masters degree with a research project involving some cute Pepper robots at UTS’ Social Robotics Lab – which won RoboCup’s Home category in 2019. Throughout the years, Maggie has dipped her toes in various industries, from business development and digital marketing at Google to not-for-profit, banking, autonomous vehicles and more recently quantum technology. She has practical experience applying deep reinforcement learning techniques to quantum control problems and then deploying her research to production for customers to enjoy. Maggie is involved with various nonprofits that teach coding to people of all ages, with a focus on teenage girls. She suspects that if she had had that level of exposure to computer science during high school, it would have captivated her right away. That’s Maggie’s wish for future generations – but she also reminds us that it’s never too late!
Seng-Beng Ho is currently Senior Scientist & Deputy Director, Department of Social & Cognitive Computing, Institute of High Performance Computing, Agency of Science, Technology & Research, Singapore. He obtained his Ph.D. in Cognitive Science (AI, Neuroscience, Psychology, & Linguistics) and M.Sc. in Computer Science from the University of Wisconsin, Madison, U.S.A. He has a B.E. in Electronic Engineering from the University of Western Australia. He is the author of a monograph published in June 2016 by Springer International entitled “Principles of Noology: Toward a Theory and Science of Intelligence”. In the book, he presents a principled and fundamental theoretical framework that is critical for building truly general AI systems. Prior to the current position, for 11 years he was President of E-Book Systems Pte Ltd, an e-book Technology company he founded with offices in the Silicon Valley, Beijing, Tokyo, Germany, and Singapore. The company developed and marketed a patented, novel 3D page-flipping technology platform for e-book. Prior to that, he lectured and conducted research on AI and Cognitive Science at the Department of Information Systems and Computer Science, National University of Singapore. He holds 36 U.S. and world-wide patents related to e-book technology and has published more than 30 papers in the field of AI since returning from industry.
Archana works as an AI Engineer at Continental Automotive. Her field of work is in TinyML i.e applying machine learning models to small devices with low power and memory requirements. This means that microcontrollers excite her and she loves working in this applied AI field. After work, you can usually find her volunteering at Women Who Code, where she co-leads the cloud and python track as a Leadership Fellow. Apart from that, she actively participates in TinyML and Women in Machine Learning events.
Andrew has international expertise in the areas of machine learning, statistical analysis, cloud computing and AI. He is founding member of the boutique data science consulting firm Datamahi and sports industry specialists Media Rights Value.
Learn Machine Learning on AWS SageMaker(Workshop)
Ravi Ranjan is a full-stack Data Scientist working as Manager Data Science at Publicis Sapient. He holds a Bachelor’s degree in Computer Science & Engineering with a proficiency course in Reinforcement Learning from IISc Bangalore. He has professional experience of 8+ years in AI and ML at scale with expertise in building enterprise data solutions and ML Engineering. He is part of the Centre of Excellence and is responsible for building ML products from inception to production. He has worked on multiple engagements with clients mainly from the Automobile, Banking, Retail, and Insurance industries. He is a Google Certified Professional Cloud Architect, blogger, speaker, and mentor.
Hui Xiang Chua is Senior Data Scientist at Dataiku, helping enterprises with data democratization and enabling them to build their own path to AI. Dataiku is a 2x Gartner Magic Quadrant Leader for Data Science and Machine-Learning Platforms (as of 2021).
She has both public and private experiences solving problems using data, namely over six years in the public service and two years in the media industry. She was also previously an instructor with General Assembly.
In 2017, she was accepted to the Data Science for Social Good Fellowship and was mentored by Rayid Ghani, Chief Scientist of the Obama for America campaign in 2012. For bringing data science into a high school’s curriculum, Hui Xiang was a recipient of the KDD Impact Program award by SIGKDD, the Association for Computing Machinery’s special interest group on knowledge discovery and data mining. She also runs a data science blog called Data Double Confirm that was recognised as 2018/2019 Top 100 Data Science Resources on MastersInDataScience.com.
Hui Xiang holds a B.Sc.(Sons) in Statistics and M.Sc. in Business Analytics from National University of Singapore.
Aditya Kanade is an Associate Professor at the Department of Computer Science and Automation of the Indian Institute of Science. He completed his PhD at IIT Bombay and post-doc at the University of Pennsylvania. His research interests span machine learning, software engineering and automated reasoning. He has received an ACM best paper award, a teaching excellence award, and faculty awards from IBM, Microsoft Research India and the Mozilla Foundation. He has been a Visiting Researcher at General Motors Research, Microsoft Research and most recently, at Google Brain. He is particularly excited about the prospect of developing machine learning techniques to automate software engineering, and designing trustworthy and deployable machine learning systems.
Vaishali is a lead data scientist at Indium Software, a leading digital engineering company. She has 7 years of experience in predictive modeling and data analysis. She designs and develops enterprise-grade solutions based on Machine Learning, Deep Learning, and Natural Language Processing for real-world use cases. As a technology evangelist, Vaishali also coaches aspiring professionals on data science and machine learning at Simplilearn, the world’s leading training boot camp. Vaishali holds a professional postgraduate degree in Artificial Intelligence and Machine Learning. She loves cracking Machine Learning Hackathons and has been a winner in many such events.
The field of artificial intelligence (AI) has seen several proposals for modeling intelligent computation. Two of the most popular ones are (1) neural – which is inspired by the structure of our brain and consists of millions of nodes resembling neurons connected in a network, and (2) symbolic – which uses the formalism of logic to make inferences from known facts. While deep neural models have revolutionized the field of AI in modern times, an emerging body of work combines neural models with symbolic computation to achieve the best of both worlds. In this introductory tutorial, we briefly present some of this literature in the context of (1) augmenting neural models by incorporating additional symbolic knowledge, (2) designing neural models for solving symbolic reasoning problems, and, (3) neuro-symbolic architectures for solving perceptual-reasoning tasks.
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