Build 10 AI projects in 10 days without coding using Google Teachable Machines, DataRobot, AWS Autopilot, and Vertex AI
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 Bootcamp
Join 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 Machine
- Google TensorFlow Playground
- AWS SageMaker Autopilot
- Google Vertex AI
The 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 DataRobot
Day #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.
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.
Table of Contents
Bonus Materials [Please download ASAP, Link expiring soon]
1 Bonus Materials
Welcome to the Course!
2 Main Course Intro
3 Course Introduction and Best Practices
4 AI Superpowers
5 Key AI Components
6 Course Outline
Day 1 Develop an AI model to classify fashion elements using Google Teachable
7 Introduction to Day 1
8 Task 1. Project Card and Demo
9 Task 2. AI Applications in Fashion
10 Task 3. Data Exploration
11 Task 4. Model Training and Testing in Google Teachable Machines
12 Task 5. Export and Deploy Model in Google Teachable Machines
13 Task 6. Final Project
14 End of Day 1
Day 2 Deep Dive into AI technicalities
15 Introduction to Day 2
16 Task 1. Project Overview
17 Task 2. Artificial Neural Networks (ANNs) Simplified
18 Task 3. AI Training vs. Testing Process
19 Task 4. AI Lingo
20 Task 5. Confusion Matrix
21 Task 6. Final Project Part A
22 Task 7. Final Project Part B
23 End of Day 2
Day 3 Detect and classify face masks using Google Teachable Machines
24 Introduction Day 3
25 Task 1. Project Card and Demo
26 Task 2. Business Case
27 Task 3. Google Teachable Machines Demo Data Collection
28 Task 4. Google Teachable Machines Demo Model Training
29 Task 5. Google Teachable Machines Demo Model EvaluationDeployment
30 Task 6. Classifier Models KPIs
31 Task 7. Precision vs. Recall
32 Task 8. Final Project
33 End of Day 3
Day 4 Visualize Artificial Intelligence Models Using Tensorspace.JS and GTP
34 Introduction Day 4
35 Task 1. Project Card and Demo
36 Task 2. Artificial Neural Networks 101
37 Task 3. Visualize ANNs in GTP
38 Task 4. Final Project Part A
39 Task 5. Convolutional Neural Networks (CNNs)
40 Task 6. CNNs Visualization
41 Task 7. LeNet Architecture Overview
42 Task 8. 3D Visualization in Tensorspace.JS
43 Task 9. ResNet Visualization in TensorSpace.JS
44 Task 10. Final Project
45 End of Day 4
Day 5 Develop an ML Model to predict used car prices using DataRobot
46 Introduction Day 5
47 Task 1. Project Card and Demo
48 Task 2. Success Stories and Business Case
49 Task 3. Data Overview
50 Task 4. DataRobot Demo Data Upload
51 Task 5. DataRobot Demo Exploratory Data Analysis
52 Task 6. DataRobot Demo Model Training
53 Task 7. DataRobot Demo Model Assessment
54 Task 8. DataRobot Demo Model Deployment
55 Task 9. Technicalities
56 Task 10. Final Project Part A
57 Task 11. Final Project Part B
58 End of Day 5
Day 6 Develop an AI model to predict employee’s attrition using DataRobot
59 Introduction Day 6
60 Task 1. Project Card and Demo
61 Task 2. Success Stories and Business Case
62 Reading Materials How AI is Transforming Human Resources
63 Task 3. Data Overview
64 Task 4. DataRobot Demo Data Upload
65 Task 5. DataRobot Demo Data Exploration
66 Task 6. DataRobot Demo Model Training
67 Task 7. Classification Models KPIs
68 Task 8. Model Assessment
69 Task 9. Final Project
70 End of Day 6
Day 7 Develop an AI model to detect Diabetic Retinopathy Using DataRobot
71 Introduction Day 7
72 Task 1. Project Card and Demo
73 Task 2. Business Case and Success Stories
74 Task 3. Data Exploration
75 Task 4. DataRobot Demo Data Upload
76 Task 5. DataRobot Demo Model Training
77 Task 6. DataRobot Demo Deploy Model
78 Task 7. Explainable AI
79 Task 8. Final Project
80 End of Day 7
Day 8 Deploy an AI model to predict customer sentiment from Text
81 Introduction to Day 8
82 Task 1. Project Card and Demo
83 Task 2. Business Case, Reading Materials and Quiz
84 Task 3. Data Exploration
85 Task 4. DataRobot Demo Data Upload
86 Task 5. DataRobot Demo Data Analysis
87 Task 6. DataRobot Demo Model Training
88 Task 7. DataRobot Demo Model Deployment
89 Task 8. Final Project Part A
90 Task 9. Final Project Part B
91 End of Day 8
Day 9 Predict credit card default using AWS SageMaker Autopilot
92 Introduction Day 9
93 Task 1. Project Card
94 Task 2. AI Applications in Business
95 Task 3. AWS 101
96 Task 4. AWS S3 EC2 and SageMaker
97 Task 5. AWS SageMaker Autopilot Demo 1
98 Task 5. AWS SageMaker Autopilot Demo 2
99 Task 5. AWS SageMaker Autopilot Demo 3
100 Task 6. Delete Endpoint
101 Task 7. Final Project Overview
102 Task 8. Final Project Solution – AWS AutoPilot Demo 1
103 Task 8. Final Project Solution – AWS AutoPilot Demo 2
104 Task 8. Final Project Solution – AWS AutoPilot Demo 3
105 End of Day 9
Day 10 Google Vertex AI-Powered Regression Model Prediction
106 Task 1. The Rise of Machine Learning in Higher Education
107 Task 2. Machine Learning Regression
108 Task 3. Vertex AI Demo Part 1 Setup and Upload
109 Task 4. Vertex AI Demo Part 2 Model Training
110 Task 5. Machine Learning Regression Models Metrics
111 Task 6. Vertex AI Demo Part 3 Deploy Model
112 Task 7. Recap and Concluding Remarks
113 End of Day 10