Modern Artificial Intelligence with Zero Coding
- Description
- Curriculum
- FAQ
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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).
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Case Study 1. Chest Disease Detection Using Google Teachable Machine -
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The Rise of AI in HealthCare
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Reading Material: The Rise of AI in Healthcare Applications -
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Quiz: The Rise of AI in Healthcare ApplicationsPlease read the article entitled: "The Rise of Artificial Intelligence in Healthcare Applications" and answer the following questions.
Link to article: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7325854/
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Project Overview
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AI Model Training & Testing in Google Teachable Machines
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Under the Hood - Artificial Neural Networks Simplified
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Under the Hood - Artificial Neural Networks Training & Testing Processes
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Under the Hood - AI Lingo Demystified
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Under the Hood - Confusion Matrix
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ANN Demo in Tensorflow Playground
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Export, Save and Deploy the AI Model
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Convolutional Neural Networks (CNNs) Deep Dive
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Covid-Net Overview
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COVID-NET
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Final Project Overview
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Final Project Solution
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Case Study 2. Emotion AI with Google Teachable Machine
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Introduction to Emotion AI and Project Overview
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Reading Material: Emotion AI For Ad Testing and Media Analytics
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Quiz: Emotion AI For Ad Testing and Media Analytics
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Teachable Machine Demo #1 - Data Collection
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Teachable Machine Demo #2 - Model Training
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Teachable Machine Demo #3 - Model Deployment and Testing
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Classification Models KPIs - Part #1
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Classification Models KPIs - Part #2
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Transfer Learning
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Off the shelf Networks, ResNets, and ImageNet
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Final Project Overview
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Final Project Solution
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Case Study 3. Cardiovascular Disease Detection with DataRobot
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Project Overview: Cardiovascular Disease Detection with DataRobot AI
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Reading Materials: AI for Cardiovascular Disease Detection
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Quiz: AI for Cardiovascular Disease Detection
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DataRobot Demo #1: Signup and data upload
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DataRobot Demo #2: Target Selection & Exploratory Data Analysis
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DataRobot Demo #3: Model Training and Feature Importance
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Precision, Recall, ROC and AUC
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DataRobot Demo #4: Model Evaluation and Assessment
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DataRobot Demo #5: Model Deployment and Inference
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Introduction to XG-Boost [Optional Lecture/Additional Material]
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What is Boosting? [Optional Lecture/Additional Material]
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Decision Trees and Ensemble Learning [Optional Lecture/Additional Material]
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Gradient Boosting Deep Dive #1 [Optional Lecture/Additional Material]
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Gradient Boosting Deep Dive #2 [Optional Lecture/Additional Material]
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Case Study 4. AI in Business
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Introduction to AI in business with AWS
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Reading Material: AI Applications in Business
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Quiz: AI Applications in Business
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Project Overview: Insurance Premium Prediction
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Simple and Multiple Linear Regression
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Amazon Web Services (AWS) 101
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Amazon S3 and EC2
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Introduction to AWS SageMaker
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Regression Metrics
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AWS SageMaker AutoPilot Demo #1
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AWS SageMaker AutoPilot Demo #2
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AWS SageMaker AutoPilot Demo #3
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Case Study 5. Food Recognition with AI & Explainable AI
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Project Introduction: Food Recognition with AI
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Reading Material: Machine Learning and AI in the Food Industry
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Quiz: Machine Learning and AI in the Food Industry
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DataRobot Demo #1 - Upload & Explore Dataset
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DataRobot Demo #2 - Train AI Model
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DataRobot Demo #3 - Explainable AI
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Logistic Regression Theory [Optional Lecture/Additional Material]
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Bias Variance Tradeoff [Optional Lecture/Additional Material]
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L1 & L2 Regularization Part #1 [Optional Lecture/Additional Material]
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L1 & L2 Regularization Part #2 [Optional Lecture/Additional Material]
