This course was last updated on 7/2021.
We analyzed factors such as the rating (4.7/5) and the ratio between the number of reviews and the number of students, which is a great signal of student commitment.
✓ Build, train and deploy AI models to detect people emotions using Google Teachable Machine
✓ Explain the difference between learning rate, epochs, batch size, accuracy and loss.
✓ Predict Insurance Premium using Customer Features such as age, smoking habit and geo-location using AWS AI AutoPilot
✓ Build, train and deploy advanced AI to detect cardiovascular disease using DataRobot AI
✓ Leverage the power of AI to recognize food types using DataRobot AI
✓ Develop an AI model to detect and classify chest disease using X-Ray chest data using Google Teachable Machines
✓ Evaluate trained AI models using various KPIs such as confusion matrix, classification accuracy, and error rate
✓ List the various advantages of transfer learning and know when to properly apply the technique to speed up training process
✓ Understand the theory and intuition behind residual networks, a state-of-the-art deep neural networks that are widely adopted in business, and healthcare
✓ Learn how to train multiple AI models based on XG-Boost, Artificial Neural Networks, Random Forest Classifiers and compare their performance in DataRobot
✓ Understand the impact of classifier threshold on False Positive Rate (Fallout) and True Positive Rate (Sensitivity)
✓ Learn how to use SageMaker Studio AutoML tool to build, train and deploy AI/Ml models which requires almost zero coding experience
✓ Differentiate between various regression models KPIs such as R2 or coefficient of determination, Mean absolute error, Mean Squared error, and Root Mean Squared Error
✓ Build, train and deploy XGBoost-based algorithm to perform regression tasks using AWS SageMaker Autopilot
• The course has no prerequisites and is open to anyone with no or basic programming knowledge. Students who enroll in this course will master AI fundamentals and directly apply these skills to solve real world challenging problems.
• Seasoned consultants who don’t possess coding skills (or have basic coding skills) and wanting to transform businesses by leveraging AI.
• Visionary business owners who want to harness the power of AI to maximize revenue, reduce costs and optimize their business.
• AI Practitioners wanting to advance their careers and build their portfolio.
• Tech enthusiasts who are passionate about AI and want to gain real-world practical experience.
The course costs $14.99. And currently there is a 82% discount on the original price of the course, which was $84.99. So you save $70 if you enroll the course now.
YES, Modern Artificial Intelligence with Zero Coding has a 30-day money back guarantee. The 30-day refund policy is designed to allow students to study without risk.
Dr. Ryan Ahmed, Ph.D., MBA has created 44 courses that got 27,789 reviews which are generally positive. Dr. Ryan Ahmed, Ph.D., MBA has taught 295,730 students and received a 4.6 average review out of 27,789 reviews. Depending on the information available, we think that Dr. Ryan Ahmed, Ph.D., MBA is an instructor that you can trust.
Professor & Best-selling Instructor, 250K+ students
Ryan Ahmed is a best-selling Udemy instructor who is passionate about education and technology. Ryan’s mission is to make quality education accessible and affordable to everyone. Ryan holds a Ph.D. degree in Mechanical Engineering from McMaster* University, with focus on Mechatronics and Electric Vehicle (EV) control. He also received a Master’s of Applied Science degree from McMaster, with focus on Artificial Intelligence (AI) and fault detection and an MBA in Finance from the DeGroote School of Business.
Ryan held several engineering positions at Fortune 500 companies globally such as Samsung America and Fiat-Chrysler Automobiles (FCA) Canada. Ryan has taught several courses on Science, Technology, Engineering and Mathematics to over 280,000+ students globally. He has over 25 published journal and conference research papers on state estimation, AI, Machine learning, battery modeling and EV controls. He is the co-recipient of the best paper award at the IEEE Transportation Electrification Conference and Expo (iTEC 2012) in Detroit, MI, USA.
Ryan is a Stanford Certified Project Manager (SCPM), certified Professional Engineer (P.Eng.) in Ontario, a member of the Society of Automotive Engineers (SAE), and a member of the Institute of Electrical and Electronics Engineers (IEEE). He is also the program Co-Chair at the 2017 IEEE Transportation and Electrification Conference (iTEC’17) in Chicago, IL, USA.
* McMaster University is one of only four Canadian universities consistently ranked in the top 100 in the world.
