課程資料
報名日期
全年招生
課程級別
學習模式
修業期
9 小時 33 分鐘
教學語言
英語
授課導師
Prof. Ryan Ahmed | 450K+ Students | Best-Selling Professor | 250K+ YouTube, Ligency , SuperDataScience Team
證書
- 可獲發
- *證書的發放與分配，依課程提供者的政策及安排而定。
評分
4次瀏覽
課程簡介
Build 5 Practical Projects & Harness the Power of AI to solve practical, real-world business problems with Zero Coding!
Do you want to build super-powerful applications in Artificial intelligence (AI) but you don’t know how to code?
Are you intimidated by AI and don’t know where to start?
Or maybe you don’t have a computer science degree and want to break into AI?
Are you an aspiring entrepreneur who wants to maximize business revenue and reduce costs with AI but don’t know how to get there quickly and efficiently?
If the answer is yes to any of these questions, then this course is for you!
Artificial intelligence is one of the top tech fields to be in right now!
AI will change our lives in the same way electricity did 100 years ago.
AI is widely adopted in Finance, banking, healthcare, transportation, and technology. The field is exploding with opportunities and career prospects.
This course solves a key problem which is making AI available to anyone with no coding background or computer science degree.
The purpose of this course is to provide you with knowledge of key aspects of modern AI without any intimidating mathematics and in a practical, easy, and fun way. The course provides students with practical hands-on experience using real-world datasets.
In this course, we will assume that you have been recently hired as a consultant at a start-up in San Francisco. The CEO has tasked you to apply cutting-edge AI techniques to 5 projects. There is only one caveat, your key data scientist quit on you and do not know how to code, and you need to generate results fast. In fact, you only have one week to solve these key company problems. You will be provided with datasets from all these departments and you will be asked to achieve the following tasks:
Project #1: Develop an AI model to detect people's emotions using Google Teachable Machines (Technology).
Project #2: Develop an AI model to detect and classify chest disease using X-Ray chest data using Google Teachable Machines (HealthCare).
Project #3: Predict Insurance Premium using Customer Features such as age, smoking habit, and geo-location using AWS AI AutoPilot (Business).
Project #4: Detect Cardiovascular Disease using DataRobot AI (HealthCare).
Project #5: Recognize food types and explore AI explainability using DataRobot AI (Technology).
課程章節
- 6 個章節
- 70 堂課
- 第 1 章 Course Introduction, Key Learning Outcomes, and Key Tips for Success
- 第 2 章 AI In Healthcare: Disease Detection With AI-Powered Google Teachable Machine
- 第 3 章 Emotion AI with AI-powered Google Teachable Machines
- 第 4 章 AI for Cardiovascular Disease Detection with DataRobot
- 第 5 章 AI in Business With AWS Autopilot
- 第 6 章 AI for Food Recognition & Explainable AI with DataRobot
課程內容
- 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
此課程所涵蓋的技能
評價
-
JJennifer Brown
yes - it was a good match.
-
MMayla Granado
The easy to follow hands-on training with live tools is phenomenal!
-
HHruday Kuppali
It’s very simple and knowledgeable
-
SSathiamoorthi Raj
it's really useful to me.
