Modern Artificial Intelligence with Zero Coding
Build 5 Practical Projects & Harness the Power of AI to solve practical, real-world business problems with Zero Coding!
4.38 (1713 reviews)
14 238
students
9.5 hours
content
Jun 2024
last update
$99.99
regular price
What you will learn
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
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Comidoc Review
Our Verdict
This course on Modern Artificial Intelligence with Zero Coding offers a wide-ranging introduction to various AI tools and practical projects for those new to the field. While it strives to explain complex concepts without delving into coding, some learners might find the depth of coverage lacking, especially since external resources are essential to complete the coursework. The instructor's expertise and accessible teaching style contribute positively to the learning experience; however, be prepared for repetition and outdated information that hinder optimal engagement with the material.
What We Liked
- Covers a wide range of AI tools and techniques, allowing students to explore different options
- Explains complex concepts in an accessible way, making it suitable for beginners with little to no prior knowledge in AI
- Project-based approach enables hands-on experience in building practical AI models
- Instructor is knowledgeable and presents the material in a clear and engaging manner
Potential Drawbacks
- Reliance on external resources can be frustrating, with broken links and outdated information hindering progress
- Course material could benefit from updates to reflect changes in tools and services such as DataRobot and AWS SageMaker
- Content may seem repetitive for some learners, with excessive reviewing of previously covered topics
- Lacks focus on specific AI tools or techniques, which could lead to a more superficial understanding of the subject matter
Related Topics
3885700
udemy ID
02/03/2021
course created date
18/03/2021
course indexed date
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