Modern Artificial Intelligence with Zero Coding
What you’ll 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
Requirements
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.
Description
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).
Overview
Section 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
Section 2: AI In Healthcare: Disease Detection With AI-Powered Google Teachable Machine
Lecture 7 Case Study 1. Chest Disease Detection Using Google Teachable Machine
Lecture 8 The Rise of AI in HealthCare
Lecture 9 Reading Material: The Rise of AI in Healthcare Applications
Lecture 10 Project Overview
Lecture 11 AI Model Training & Testing in Google Teachable Machines
Lecture 12 Under the Hood – Artificial Neural Networks Simplified
Lecture 13 Under the Hood – Artificial Neural Networks Training & Testing Processes
Lecture 14 Under the Hood – AI Lingo Demystified
Lecture 15 Under the Hood – Confusion Matrix
Lecture 16 ANN Demo in Tensorflow Playground
Lecture 17 Export, Save and Deploy the AI Model
Lecture 18 Convolutional Neural Networks (CNNs) Deep Dive
Lecture 19 Covid-Net Overview
Lecture 20 Final Project Overview
Lecture 21 Final Project Solution
Section 3: Emotion AI with AI-powered Google Teachable Machines
Lecture 22 Case Study 2. Emotion AI with Google Teachable Machine
Lecture 23 Introduction to Emotion AI and Project Overview
Lecture 24 Reading Material: Emotion AI For Ad Testing and Media Analytics
Lecture 25 Teachable Machine Demo #1 – Data Collection
Lecture 26 Teachable Machine Demo #2 – Model Training
Lecture 27 Teachable Machine Demo #3 – Model Deployment and Testing
Lecture 28 Classification Models KPIs – Part #1
Lecture 29 Classification Models KPIs – Part #2
Lecture 30 Transfer Learning
Lecture 31 Off the shelf Networks, ResNets, and ImageNet
Lecture 32 Final Project Overview
Lecture 33 Final Project Solution
Section 4: AI for Cardiovascular Disease Detection with DataRobot
Lecture 34 Case Study 3. Cardiovascular Disease Detection with DataRobot
Lecture 35 Project Overview: Cardiovascular Disease Detection with DataRobot AI
Lecture 36 Reading Materials: AI for Cardiovascular Disease Detection
Lecture 37 DataRobot Demo #1: Signup and data upload
Lecture 38 DataRobot Demo #2: Target Selection & Exploratory Data Analysis
Lecture 39 DataRobot Demo #3: Model Training and Feature Importance
Lecture 40 Precision, Recall, ROC and AUC
Lecture 41 DataRobot Demo #4: Model Evaluation and Assessment
Lecture 42 DataRobot Demo #5: Model Deployment and Inference
Lecture 43 Introduction to XG-Boost [Optional Lecture/Additional Material]
Lecture 44 What is Boosting? [Optional Lecture/Additional Material]
Lecture 45 Decision Trees and Ensemble Learning [Optional Lecture/Additional Material]
Lecture 46 Gradient Boosting Deep Dive #1 [Optional Lecture/Additional Material]
Lecture 47 Gradient Boosting Deep Dive #2 [Optional Lecture/Additional Material]
Section 5: AI in Business With AWS Autopilot
Lecture 48 Case Study 4. AI in Business
Lecture 49 Introduction to AI in business with AWS
Lecture 50 Reading Material: AI Applications in Business
Lecture 51 Project Overview: Insurance Premium Prediction
Lecture 52 Simple and Multiple Linear Regression
Lecture 53 Amazon Web Services (AWS) 101
Lecture 54 Amazon S3 and EC2
Lecture 55 Introduction to AWS SageMaker
Lecture 56 Regression Metrics
Lecture 57 AWS SageMaker AutoPilot Demo #1
Lecture 58 AWS SageMaker AutoPilot Demo #2
Lecture 59 AWS SageMaker AutoPilot Demo #3
Section 6: AI for Food Recognition & Explainable AI with DataRobot
Lecture 60 Case Study 5. Food Recognition with AI & Explainable AI
Lecture 61 Project Introduction: Food Recognition with AI
Lecture 62 Reading Material: Machine Learning and AI in the Food Industry
Lecture 63 DataRobot Demo #1 – Upload & Explore Dataset
Lecture 64 DataRobot Demo #2 – Train AI Model
Lecture 65 DataRobot Demo #3 – Explainable AI
Lecture 66 Logistic Regression Theory [Optional Lecture/Additional Material]
Lecture 67 Bias Variance Tradeoff [Optional Lecture/Additional Material]
Lecture 68 L1 & L2 Regularization Part #1 [Optional Lecture/Additional Material]
Lecture 69 L1 & L2 Regularization Part #2 [Optional Lecture/Additional Material]
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.
Course Information:
Udemy | English | 9h 33m | 5.59 GB
Created by: Dr. Ryan Ahmed, Ph.D., MBA
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