Modern Artificial Intelligence with Zero Coding
Modern Artificial Intelligence with Zero Coding, available at $94.99, has an average rating of 4.44, with 74 lectures, 6 quizzes, based on 1566 reviews, and has 13279 subscribers.
You will learn about 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 This course is ideal for individuals who are Seasoned consultants who don’t possess coding skills (or have basic coding skills) and wanting to transform businesses by leveraging AI. or Visionary business owners who want to harness the power of AI to maximize revenue, reduce costs and optimize their business. or AI Practitioners wanting to advance their careers and build their portfolio. or Tech enthusiasts who are passionate about AI and want to gain real-world practical experience. It is particularly useful for Seasoned consultants who don’t possess coding skills (or have basic coding skills) and wanting to transform businesses by leveraging AI. or Visionary business owners who want to harness the power of AI to maximize revenue, reduce costs and optimize their business. or AI Practitioners wanting to advance their careers and build their portfolio. or Tech enthusiasts who are passionate about AI and want to gain real-world practical experience.
Enroll now: Modern Artificial Intelligence with Zero Coding
Summary
Title: Modern Artificial Intelligence with Zero Coding
Price: $94.99
Average Rating: 4.44
Number of Lectures: 74
Number of Quizzes: 6
Number of Published Lectures: 71
Number of Published Quizzes: 6
Number of Curriculum Items: 80
Number of Published Curriculum Objects: 77
Original Price: $199.99
Quality Status: approved
Status: Live
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
Who Should Attend
- 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.
Target Audiences
- 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.
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:
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Project #1: Develop an AI model to detect people’s emotions using Google Teachable Machines (Technology).
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Project #2: Develop an AI model to detect and classify chest disease using X-Ray chest data using Google Teachable Machines (HealthCare).
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Project #3: Predict Insurance Premium using Customer Features such as age, smoking habit, and geo-location using AWS AI AutoPilot (Business).
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Project #4: Detect Cardiovascular Disease using DataRobot AI (HealthCare).
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Project #5: Recognize food types and explore AI explainability using DataRobot AI (Technology).
Course Curriculum
Chapter 1: Course Introduction, Key Learning Outcomes, and Key Tips for Success
Lecture 1: Course Introduction and Welcome Message
Lecture 2: Course Introduction Key Tips for Success, Best Practices and Getting Certified
Lecture 3: What is Artificial Intelligence (AI)?
Lecture 4: AI Recipe and Key Ingredients!
Lecture 5: Supervised vs. Unsupervised AI Training
Lecture 6: Course Outline and Key Learning Outcomes
Lecture 7: Learning Paths
Chapter 2: AI In Healthcare: Disease Detection With AI-Powered Google Teachable Machine
Lecture 1: Case Study 1. Chest Disease Detection Using Google Teachable Machine
Lecture 2: The Rise of AI in HealthCare
Lecture 3: Reading Material: The Rise of AI in Healthcare Applications
Lecture 4: Project Overview
Lecture 5: AI Model Training & Testing in Google Teachable Machines
Lecture 6: Under the Hood – Artificial Neural Networks Simplified
Lecture 7: Under the Hood – Artificial Neural Networks Training & Testing Processes
Lecture 8: Under the Hood – AI Lingo Demystified
Lecture 9: Under the Hood – Confusion Matrix
Lecture 10: ANN Demo in Tensorflow Playground
Lecture 11: Export, Save and Deploy the AI Model
Lecture 12: Convolutional Neural Networks (CNNs) Deep Dive
Lecture 13: Covid-Net Overview
Lecture 14: Final Project Overview
Lecture 15: Final Project Solution
Chapter 3: Emotion AI with AI-powered Google Teachable Machines
Lecture 1: Case Study 2. Emotion AI with Google Teachable Machine
Lecture 2: Introduction to Emotion AI and Project Overview
Lecture 3: Reading Material: Emotion AI For Ad Testing and Media Analytics
Lecture 4: Teachable Machine Demo #1 – Data Collection
Lecture 5: Teachable Machine Demo #2 – Model Training
Lecture 6: Teachable Machine Demo #3 – Model Deployment and Testing
Lecture 7: Classification Models KPIs – Part #1
Lecture 8: Classification Models KPIs – Part #2
Lecture 9: Transfer Learning
Lecture 10: Off the shelf Networks, ResNets, and ImageNet
Lecture 11: Final Project Overview
Lecture 12: Final Project Solution
Chapter 4: AI for Cardiovascular Disease Detection with DataRobot
Lecture 1: Case Study 3. Cardiovascular Disease Detection with DataRobot
Lecture 2: Project Overview: Cardiovascular Disease Detection with DataRobot AI
Lecture 3: Reading Materials: AI for Cardiovascular Disease Detection
Lecture 4: DataRobot Demo #1: Signup and data upload
Lecture 5: DataRobot Demo #2: Target Selection & Exploratory Data Analysis
Lecture 6: DataRobot Demo #3: Model Training and Feature Importance
Lecture 7: Precision, Recall, ROC and AUC
Lecture 8: DataRobot Demo #4: Model Evaluation and Assessment
Lecture 9: DataRobot Demo #5: Model Deployment and Inference
Lecture 10: Introduction to XG-Boost [Optional Lecture/Additional Material]
Lecture 11: What is Boosting? [Optional Lecture/Additional Material]
Lecture 12: Decision Trees and Ensemble Learning [Optional Lecture/Additional Material]
Lecture 13: Gradient Boosting Deep Dive #1 [Optional Lecture/Additional Material]
Lecture 14: Gradient Boosting Deep Dive #2 [Optional Lecture/Additional Material]
Chapter 5: AI in Business With AWS Autopilot
Lecture 1: Case Study 4. AI in Business
Lecture 2: Introduction to AI in business with AWS
Lecture 3: Reading Material: AI Applications in Business
Lecture 4: Project Overview: Insurance Premium Prediction
Lecture 5: Simple and Multiple Linear Regression
Lecture 6: Amazon Web Services (AWS) 101
Lecture 7: Amazon S3 and EC2
Lecture 8: Introduction to AWS SageMaker
Lecture 9: Regression Metrics
Lecture 10: AWS SageMaker AutoPilot Demo #1
Lecture 11: AWS SageMaker AutoPilot Demo #2
Lecture 12: AWS SageMaker AutoPilot Demo #3
Chapter 6: AI for Food Recognition & Explainable AI with DataRobot
Lecture 1: Case Study 5. Food Recognition with AI & Explainable AI
Lecture 2: Project Introduction: Food Recognition with AI
Lecture 3: Reading Material: Machine Learning and AI in the Food Industry
Lecture 4: DataRobot Demo #1 – Upload & Explore Dataset
Lecture 5: DataRobot Demo #2 – Train AI Model
Lecture 6: DataRobot Demo #3 – Explainable AI
Lecture 7: Logistic Regression Theory [Optional Lecture/Additional Material]
Lecture 8: Bias Variance Tradeoff [Optional Lecture/Additional Material]
Lecture 9: L1 & L2 Regularization Part #1 [Optional Lecture/Additional Material]
Lecture 10: L1 & L2 Regularization Part #2 [Optional Lecture/Additional Material]
Chapter 7: Congratulations!! Don't forget your Prize 🙂
Lecture 1: Bonus: How To UNLOCK Top Salaries (Live Training)
Instructors
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Dr. Ryan Ahmed, Ph.D., MBA
Best-Selling Professor, 400K+ students, 250K+ YT Subs -
Ligency Team
Helping Data Scientists Succeed -
SuperDataScience Team
Helping Data Scientists Succeed -
Mitchell Bouchard
B.S, Host @RedCapeLearning 540,000 + Students
Rating Distribution
- 1 stars: 8 votes
- 2 stars: 24 votes
- 3 stars: 155 votes
- 4 stars: 491 votes
- 5 stars: 888 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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