10 Days of No Code Artificial Intelligence Bootcamp
What you’ll learn
Build, train, test and deploy 10 AI/ML models in 10 days without writing any code.
Build, train, test and deploy AI models to classify fashion items using Google Teachable Machine.
Visualize State-of-the-Art Artificial Intelligence Models Using Tensorspace JS, Google Tensorflow Playground and Ryerson 3D CNN Visualizations.
Explain the difference between learning rate, epochs, batch size, accuracy, and loss.
Build, train and deploy advanced AI to detect Diabetic Retinopathy disease using DataRobot AI.
Leverage the power of AI to solve regression tasks and predict used car prices using DataRobot AI.
Evaluate trained AI models using various KPIs such as confusion matrix, classification accuracy, and error rate.
Understand the theory and intuition behind Residual Neural Networks (ResNets), a state-of-the-art deep NNs that are widely adopted in several industries.
Understand the impact of classifier threshold on False Positive Rate (Fallout) and True Positive Rate (Sensitivity).
Predict employee attrition based on their features such as employee engagement, distance from home, job satisfaction using DataRobot AI.
Develop an AI model to detect face masks using Google Teachable Machines.
Build, train and deploy XGBoost-based algorithm to perform regression tasks using AWS SageMaker Autopilot.
Learn how to transfer knowledge from a pre-trained Artificial Neural Network to a new network using transfer learning strategy.
Learn how to train multiple AI models based on XG-Boost, Artificial Neural Networks, Random Forest Classifiers and compare their performance in DataRobot.
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 and Mean Squared error.
Learn how to build, train, test and deploy advanced machine learning classification models using Google Vertex AI.
Understand how to leverage the power of AI/ML to predict bank customers credit card default using their features such as interest rates and loan purpose
Learn how to create a new dataset using Google Vertex AI Develop and manage experiments using Google Vertex AI.
Understand the theory, intuition, and mathematics behind simple and multiple linear regression and differentiate between various regression models KPIs.
Deploy the best model after the hyperparameters optimization job is complete and Learn how to assess feature importance and explain model predictions.
Deploy and monitor AI/ML models and create AI/ML applications with Google Vertex AI.
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.
The no-code AI revolution is here! Do you have what it takes to leverage this new wave of code-friendly tools paving the way for the future of AI?Businesses of all sizes want to implement the power of Machine Learning and AI, but the barriers to entry are high. That’s where no-code AI/ML tools are changing the game.From fast implementation to lower costs of development and ease of use, departments across healthcare, finance, marketing and more are looking to no-code solutions to deliver impactful solutions.But groundbreaking as they are, they’re nothing without talent like YOU calling the shots…Do you want to leverage machine learning and AI but feel intimidated by the complex coding involved?Do you want to master some of the top no-code tools on the market?Do you want to implement ML and AI solutions in your business, but don’t have the academic background to understand?Yes?! Then this course is for you.Master the top tools on the market and start solving practical industry scenarios when you enroll in our new course: 10 Days of No Code Artificial Intelligence BootcampJoin our best-selling instructor Dr. Ryan Ahmed and learn how to build, train, test, and deploy models that solve 10 practical challenges across finance, human resources, business, and more, using these state-of-the-art tools:Google Teachable MachineGoogle TensorFlow PlaygroundDataRobotAWS SageMaker AutopilotGoogle Vertex AITensorspace.JSThe best part? You’ll be done in 10 days or less!Take a look at the 10 professional projects you will complete:Day #1: Develop an AI model to classify fashion elements using Google Teachable Machines.Day #2: Deep-dive into AI technicalities by tweaking hyperparameters, epochs, and network architecture.Day #3: Build, train, test, and deploy an AI model to detect and classify face masks using Google Teachable Machines.Day #4: Visualize state-of-the-art AI models using Tensorspace.JS, Google Tensorflow Playground, and Ryerson 3D CNN Visualizations.Day #5: Develop a machine learning model to predict used car prices using DataRobot.Day #6: Develop an AI model to predict employee attrition rate using DataRobot.Day #7: Develop an AI model to detect Diabetic Retinopathy Disease using DataRobotDay #8: Build, train, test, and deploy an AI model to predict customer sentiment from text.Day #9: Develop an AI to predict credit card default using AWS SageMaker Autopilot.Day #10: Develop an AI model to predict university admission using Google Vertex AI.Ready to challenge your AI skills in new and exciting ways? Enroll now and experience the power of no-code AI tools.
Section 1: Welcome to the Course!
Lecture 1 Main Course Intro
Lecture 2 Course Introduction and Best Practices
Lecture 3 AI Superpowers
Lecture 4 Key AI Components
Lecture 5 Course Outline
Section 2: Day 1: Develop an AI model to classify fashion elements using Google Teachable
Lecture 6 Introduction to Day 1
Lecture 7 Task 1. Project Card and Demo
Lecture 8 Task 2. AI Applications in Fashion
Lecture 9 Task 3. Data Exploration
Lecture 10 Task 4. Model Training and Testing in Google Teachable Machines
Lecture 11 Task 5. Export and Deploy Model in Google Teachable Machines
Lecture 12 Task 6. Final Project
Lecture 13 End of Day 1
Section 3: Day 2: Deep Dive into AI technicalities
Lecture 14 Introduction to Day 2
Lecture 15 Task 1. Project Overview
Lecture 16 Task 2. Artificial Neural Networks (ANNs) Simplified
Lecture 17 Task 3. AI Training vs. Testing Process
Lecture 18 Task 4. AI Lingo
Lecture 19 Task 5. Confusion Matrix
Lecture 20 Task 6. Final Project Part A
Lecture 21 Task 7. Final Project Part B
Lecture 22 End of Day 2
Section 4: Day 3: Detect and classify face masks using Google Teachable Machines
Lecture 23 Introduction Day 3
Lecture 24 Task 1. Project Card and Demo
Lecture 25 Task 2. Business Case
Lecture 26 Task 3. Google Teachable Machines Demo: Data Collection
Lecture 27 Task 4. Google Teachable Machines Demo: Model Training
Lecture 28 Task 5. Google Teachable Machines Demo: Model Evaluation/Deployment
Lecture 29 Task 6. Classifier Models KPIs
Lecture 30 Task 7. Precision vs. Recall
Lecture 31 Task 8. Final Project
Lecture 32 End of Day 3
Section 5: Day 4: Visualize Artificial Intelligence Models Using Tensorspace.JS and GTP
Lecture 33 Introduction Day 4
Lecture 34 Task 1. Project Card and Demo
Lecture 35 Task 2. Artificial Neural Networks 101
Lecture 36 Task 3. Visualize ANNs in GTP
Lecture 37 Task 4. Final Project Part A
Lecture 38 Task 5. Convolutional Neural Networks (CNNs)
Lecture 39 Task 6. CNNs Visualization
Lecture 40 Task 7. LeNet Architecture Overview
Lecture 41 Task 8. 3D Visualization in Tensorspace.JS
Lecture 42 Task 9. ResNet Visualization in TensorSpace.JS
Lecture 43 Task 10. Final Project
Lecture 44 End of Day 4
Section 6: Day 5: Develop an ML Model to predict used car prices using DataRobot
Lecture 45 Introduction Day 5
Lecture 46 Task 1. Project Card and Demo
Lecture 47 Task 2. Success Stories and Business Case
Lecture 48 Task 3. Data Overview
Lecture 49 Task 4. DataRobot Demo: Data Upload
Lecture 50 Task 5. DataRobot Demo: Exploratory Data Analysis
Lecture 51 Task 6. DataRobot Demo: Model Training
Lecture 52 Task 7. DataRobot Demo: Model Assessment
Lecture 53 Task 8. DataRobot Demo: Model Deployment
Lecture 54 Task 9. Technicalities
Lecture 55 Task 10. Final Project Part A
Lecture 56 Task 11. Final Project Part B
Lecture 57 End of Day 5
Section 7: Day 6: Develop an AI model to predict employee’s attrition using DataRobot
Lecture 58 Introduction Day 6
Lecture 59 Task 1. Project Card and Demo
Lecture 60 Task 2. Success Stories and Business Case
Lecture 61 Reading Materials: How AI is Transforming Human Resources?
Lecture 62 Task 3. Data Overview
Lecture 63 Task 4. DataRobot Demo: Data Upload
Lecture 64 Task 5. DataRobot Demo: Data Exploration
Lecture 65 Task 6. DataRobot Demo: Model Training
Lecture 66 Task 7. Classification Models KPIs
Lecture 67 Task 8. Model Assessment
Lecture 68 Task 9. Final Project
Lecture 69 End of Day 6
Section 8: Day 7: Develop an AI model to detect Diabetic Retinopathy Using DataRobot
Lecture 70 Introduction Day 7
Lecture 71 Task 1. Project Card and Demo
Lecture 72 Task 2. Business Case and Success Stories
Lecture 73 Task 3. Data Exploration
Lecture 74 Task 4. DataRobot Demo: Data Upload
Lecture 75 Task 5. DataRobot Demo: Model Training
Lecture 76 Task 6. DataRobot Demo: Deploy Model
Lecture 77 Task 7. Explainable AI
Lecture 78 Task 8. Final Project
Lecture 79 End of Day 7
Section 9: Day 8: Deploy an AI model to predict customer sentiment from Text
Lecture 80 Introduction to Day 8
Lecture 81 Task 1. Project Card and Demo
Lecture 82 Task 2. Business Case, Reading Materials and Quiz
Lecture 83 Task 3. Data Exploration
Lecture 84 Task 4. DataRobot Demo: Data Upload
Lecture 85 Task 5. DataRobot Demo: Data Analysis
Lecture 86 Task 6. DataRobot Demo: Model Training
Lecture 87 Task 7. DataRobot Demo: Model Deployment
Lecture 88 Task 8. Final Project Part A
Lecture 89 Task 9. Final Project Part B
Lecture 90 End of Day 8
Section 10: Day 9: Predict credit card default using AWS SageMaker Autopilot
Lecture 91 Introduction Day 9
Lecture 92 Task 1. Project Card
Lecture 93 Task 2. AI Applications in Business
Lecture 94 Task 3. AWS 101
Lecture 95 Task 4. AWS S3 EC2 and SageMaker
Lecture 96 Task 5. AWS SageMaker Autopilot Demo 1
Lecture 97 Task 5. AWS SageMaker Autopilot Demo 2
Lecture 98 Task 5. AWS SageMaker Autopilot Demo 3
Lecture 99 Task 6. Delete Endpoint
Lecture 100 Task 7. Final Project Overview
Lecture 101 Task 8. Final Project Solution – AWS AutoPilot Demo 1
Lecture 102 Task 8. Final Project Solution – AWS AutoPilot Demo 2
Lecture 103 Task 8. Final Project Solution – AWS AutoPilot Demo 3
Lecture 104 End of Day 9
Section 11: Day 10: Google Vertex AI-Powered Regression Model Prediction
Lecture 105 Task 1. The Rise of Machine Learning in Higher Education
Lecture 106 Task 2. Machine Learning Regression
Lecture 107 Task 3. Vertex AI Demo Part 1 Setup and Upload
Lecture 108 Task 4. Vertex AI Demo Part 2 Model Training
Lecture 109 Task 5. Machine Learning Regression Models Metrics
Lecture 110 Task 6. Vertex AI Demo Part 3 Deploy Model
Lecture 111 Task 7. Recap and Concluding Remarks
Lecture 112 End of Day 10
Lecture 113 Introduction Day 10
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.
Udemy | English | 12h 20m | 8.43 GB
Created by: Dr. Ryan Ahmed, Ph.D., MBA
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