Are you a data scientist or machine learning engineer interested in learning more about how to analyze and improve the performance and trustworthiness of your machine learning models? Then this live online course is for you! AI Quality: Driving ML Performance and Trustworthiness is a free course taught live by five experts from leading universities, including a professor from Carnegie Mellon University and Stanford University. This offer is exclusively for corporate and government practitioners. All students completing the course receive a certificate, limited edition shirt, and access to the Slack community.
The course material of this course is available freely. But for the certificate, you have to pay. In this course, you will learn the foundational TensorFlow concepts such as the main functions, operations, and execution pipelines. This course will also teach how to use TensorFlow in curve fitting, regression, classification, and minimization of error functions. You will understand different types of Deep Architectures, such as Convolutional Networks, Recurrent Networks, and Autoencoders.
AI adoption continues to expand across the globe, with Gartner predicting that organizations over the next five years will "adopt cutting-edge techniques for smarter, reliable, responsible and environmentally sustainable artificial intelligence applications." And as the industry matures and machine learning (ML) models become cheaper, faster, and more accessible, every enterprise will be looking at how and where the technology may benefit their organization. Expectations are high, from driving productivity and efficiency gains to delivering new products and services. AI platforms are being enhanced by developments in related fields, including ML, computer vision, language, speech, recommendation engines, reinforcement learning, edge IT hardware, and robotics. However, with so much noise and hype around AI, it's tough for many businesses to figure out how to harness the technology effectively.
As a coursera certified specialization completer you will have a proven deep understanding on massive parallel data processing, data exploration and visualization, and advanced machine learning & deep learning. You'll understand the mathematical foundations behind all machine learning & deep learning algorithms. You can apply knowledge in practical use cases, justify architectural decisions, understand the characteristics of different algorithms, frameworks & technologies & how they impact model performance & scalability. If you choose to take this specialization and earn the Coursera specialization certificate, you will also earn an IBM digital badge. To find out more about IBM digital badges follow the link ibm.biz/badging.
We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 – 28. By 2025, the World Economic Forum estimates that 97 million new jobs may emerge as artificial intelligence (AI) changes the nature of work and influences the new division of labor between humans, machines and algorithms. Specifically in banking, a recent McKinsey survey found that AI technologies could deliver up to $1 trillion of additional value each year. AI is continuing its steady rise and starting to have a sweeping impact on the financial services industry, but its potential is still far from fully realized. The transformative power of AI is already impacting a range of functions in financial services including risk management, personalization, fraud detection and ESG analytics.
We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. By 2025, the World Economic Forum estimates that 97 million new jobs may emerge as artificial intelligence (AI) changes the nature of work and influences the new division of labor between humans, machines and algorithms. Specifically in banking, a recent McKinsey survey found that AI technologies could deliver up to $1 trillion of additional value each year. AI is continuing its steady rise and starting to have a sweeping impact on the financial services industry, but its potential is still far from fully realized. The transformative power of AI is already impacting a range of functions in financial services including risk management, personalization, fraud detection and ESG analytics.
Artificial Intelligence (AI) is disrupting businesses and job roles in every industry, causing concerns about long-term job security for low-skill manual jobs and management roles alike. To prepare for this AI-driven economy, many experienced managers and seasoned executives are turning to MOOCs (Massive Open Online Courses) to upskill in foundational data analytics and AI. This trend is unlikely to slow down anytime soon: The global MOOC market is expected to grow from $3.9 billion in 2018 to $20.8 billion by 2023, a CAGR of 40.1 percent. Business and technology-related courses make up 40 percent of these online courses. Many universities have also joined the drive to fill the AI leadership gap by offering high-touch executive education programs.
As chief operating officer of one of the world's leading artificial intelligence labs, I spend a lot of time thinking about how our technologies impact people's lives – and how we can ensure that our efforts have a positive outcome. This is the focus of my work, and the critical message I bring when I meet world leaders and key figures in our industry. For instance, it was at the forefront of the panel discussion on'Equity Through Technology' that I hosted this week at the World Economic Forum in Davos, Switzerland. Inspired by the important conversations taking place at Davos on building a greener, fairer, better world, I wanted to share a few reflections on my own journey as a technology leader, along with some insight into how we at DeepMind are approaching the challenge of building technology that truly benefits the global community. In 2000, I took a sabbatical from my job at Intel to visit the orphanage in Lebanon where my father was raised. For two months, I worked to install 20 PCs in the orphanage's first computer lab, and to train the students and teachers to use them.
This course, Machine Learning for Accounting with Python, introduces machine learning algorithms (models) and their applications in accounting problems. It covers classification, regression, clustering, text analysis, time series analysis. This course provides an entry point for students to be able to apply proper machine learning models on business related datasets with Python to solve various problems. Accounting Data Analytics with Python is a prerequisite for this course. This course is running on the same platform (Jupyter Notebook) as that of the prerequisite course.
This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance. The goal of Guided Tour of Machine Learning in Finance is to get a sense of what Machine Learning is, what it is for and in how many different financial problems it can be applied to. The course is designed for three categories of students: Practitioners working at financial institutions such as banks, asset management firms or hedge funds Individuals interested in applications of ML for personal day trading Current full-time students pursuing a degree in Finance, Statistics, Computer Science, Mathematics, Physics, Engineering or other related disciplines who want to learn about practical applications of ML in Finance Experience with Python (including numpy, pandas, and IPython/Jupyter notebooks), linear algebra, basic probability theory and basic calculus is necessary to complete assignments in this course.