AutoML: Build ML Models without Code

This course is part of No-Code Data Science and Machine Learning Specialization

Instructor: Edureka

Access provided by SGCSRC

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

8 hours to complete

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

8 hours to complete

Flexible schedule

Learn at your own pace

What you'll learn

Set up Google Cloud Platform and Vertex AI to configure, upload datasets, and manage AutoML workflows for structured, image, and text data.
Train AutoML classification and regression models on structured data and interpret automated feature engineering and evaluation results
Build AutoML Vision and NLP models for image classification, object detection, and text sentiment analysis without writing any code
Deploy models for online predictions, connect outputs to Google Sheets and BigQuery, and monitor performance via the cloud console

Details to know

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Assessments

14 assignments¹

AI Graded see disclaimer

Taught in English

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This course is part of the No-Code Data Science and Machine Learning Specialization

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There are 4 modules in this course

Cloud-powered machine learning is now within reach for every data professional. This course teaches you to train, deploy, and monitor production-ready ML models using Google Vertex AI's AutoML platform — covering structured data, images, and text — entirely through the web console with no coding required.

Throughout this course, you'll move through the complete AutoML lifecycle: platform setup, dataset management, advanced model training across vision and NLP domains, real-world deployment, and business tool integration — all backed by step-by-step video demonstrations on Google Cloud. You're expected to set up your own Google Cloud account, follow along with each instructor demonstration in the console, and pause the video as needed to complete each configuration or training step at your own pace. By the end of this course, you'll be able to: - Configure Google Cloud Platform and Vertex AI to set up and manage AutoML workflows for structured, image, and text datasets. - Train classification and regression models using AutoML Tables and interpret automated feature engineering and model evaluation results. - Build and evaluate AutoML Vision and Natural Language models for image classification, object detection, and text sentiment analysis. - Deploy trained models for online predictions, integrate outputs with Google Sheets and BigQuery, and monitor model performance through the cloud console. This course is designed for a diverse audience: data analysts, business intelligence professionals, product managers, domain experts, and non-technical professionals looking to leverage cloud ML capabilities to automate predictions and integrate AI into business workflows. Basic familiarity with data and machine learning concept, is recommended before enrolling. Step into cloud-powered ML and master the skills to build, deploy, and manage intelligent AutoML models that deliver measurable business impact — without writing a single line of code.

Build a strong foundation in cloud-based no-code machine learning by setting up and navigating Google Cloud and Vertex AI for AutoML workflows. Explore cloud ML architecture, platform components, and the business value of scalable AI systems. This module prepares you to confidently train and interpret AutoML models while understanding the core concepts powering automated intelligence.

What's included

19 videos6 readings4 assignments

19 videos Total 84 minutes

Course Introduction 3 minutes
Cloud Machine Learning Platform Overview 5 minutes
Benefits and Business Value of Cloud Machine Learning 6 minutes
Platform Architecture and Workflow 5 minutes
AutoML Capabilities and Features 5 minutes
Hands-On: Configuring Google Cloud Platform for Machine Learning 5 minutes
Hands-On: Setting Up Vertex AI Environment for AutoML Workflows 4 minutes
Hands-On: Uploading and Managing Datasets in AutoML Console 3 minutes
Data Preparation & Automated Feature Engineering 4 minutes
Automated Model Selection, Training & Evaluation 4 minutes
Hands-On: Training Your First Classification Model with AutoML Tables (Web UI) 4 minutes
Hands-On: Training Your First Regression Model with AutoML Tables (Web UI) 6 minutes
Ensemble Learning & Gradient Boosting 5 minutes
XGBoost - Optimized Gradient Boosting in Practice 5 minutes
Deep Learning & Neural Network Fundamentals 4 minutes
Convolutional Neural Networks (CNNs) 4 minutes
Recurrent Neural Networks (RNNs) 3 minutes
Hands-On: Interpreting Deep Neural Network Outputs in AutoML Vision 6 minutes
Hands-On: Analyzing CNN-Based Image Classification Results in AutoML Vision 4 minutes

6 readings Total 60 minutes

Course Outline: Complete No-Code Advanced ML & Deployment Journey 10 minutes
Reading: Cloud ML and AutoML Platform Setup 10 minutes
Reading: AutoML Tables for Structured Data 10 minutes
Exploring and Interpreting Ensemble Models in AutoML Reports 10 minutes
Reading: Understanding Advanced ML Concepts with Demonstrations 10 minutes
Module Summary: AutoML Platform Setup and Hands-On Experience 10 minutes

4 assignments Total 33 minutes

Graded Assignment: AutoML Platform Setup and Hands-On Experience 15 minutes
Practice Assignment : Cloud ML and AutoML Platform Setup 6 minutes
Practice Assignment : AutoML Tables for Structured Data 6 minutes
Practice Assignment: Understanding Advanced ML Concepts with Demonstrations 6 minutes

Advance your modeling capabilities by working with image, text, and reinforcement learning concepts using AutoML Vision and AutoML Natural Language. Learn to train image classification and object detection models, build sentiment analysis and text classification systems, and interpret performance metrics responsibly. By the end of this module, you will be able to select the right AutoML solution for diverse data types and align advanced AI techniques with practical business use cases.

What's included

8 videos4 readings4 assignments

8 videos Total 35 minutes

Reinforcement Learning Fundamentals: Agents, Actions, Rewards and Q-Learning 5 minutes
Real-World RL Applications 4 minutes
Computer Vision Fundamentals 5 minutes
Hands-On: Uploading Image Datasets and Labeling in AutoML Vision 3 minutes
Hands-On: Training Image Classification Models (Web UI) 5 minutes
Hands-On: Training Object Detection Models and Evaluating Results 3 minutes
NLP Fundamentals: Text Classification and Entity Extraction 4 minutes
Hands-On: Training Text Classification and Sentiment Analysis Models (Web UI) 5 minutes

4 readings Total 40 minutes

Reading: Reinforcement Learning Concepts with Demonstrations 10 minutes
Reading: AutoML Vision for Image Data 10 minutes
Reading: AutoML Natural Language for Text Data 10 minutes
Module Summary: Advanced Model Training - Vision, NLP and RL Concepts 10 minutes

4 assignments Total 33 minutes

Graded Assignment: Advanced Model Training - Vision, NLP & RL Concepts 15 minutes
Practice Assignment: Reinforcement Learning Concepts with Demonstrations 6 minutes
Practice Assignment : AutoML Vision for Image Data 6 minutes
Practice Assignement: AutoML Natural Language for Text Data 6 minutes

Complete the end-to-end machine learning lifecycle by deploying, integrating, and managing models in production environments. Learn to choose between online and batch prediction strategies based on business requirements and performance constraints. Integrate AutoML outputs with tools like Google Sheets and BigQuery to operationalize insights in real workflows. This module equips you to move beyond experimentation and build scalable, production-ready AI systems that deliver measurable business value.

What's included

9 videos4 readings4 assignments

9 videos Total 37 minutes

Model Deployment Options 4 minutes
Hands-On: Deploying Models for Online Predictions (Web UI) 4 minutes
Hands-On: Making Predictions Using AutoML Console and Testing Models 4 minutes
Integrating AutoML with Business Tools 4 minutes
Hands-On: Connecting AutoML Predictions to Google Sheets (No-Code) 4 minutes
Hands-On: Using BigQuery ML with AutoML for Data Analysis (UI Only) 5 minutes
Model Monitoring: Performance Tracking and Model Lifecycle 4 minutes
Hands-On: Monitoring Model Performance in Cloud Console 4 minutes
Hands-On: Retraining Models with New Data in AutoML (Web UI) 5 minutes

4 readings Total 40 minutes

Reading: Deploying Models with AutoML 10 minutes
Reading: No-Code Business Tool Integration 10 minutes
Reading: Monitoring and Managing Models 10 minutes
Module Summary: Model Deployment and Business Integration 10 minutes

4 assignments Total 33 minutes

Graded Assignment: Model Deployment and Business Integration 15 minutes
Practice Assignment : Deploying Models with AutoML 6 minutes
Practice Assignment : No-Code Business Tool Integration 6 minutes
Practice Assignment: Monitoring and Managing Models 6 minutes

Consolidate your learning by revisiting the complete no-code AutoML lifecycle, from cloud platform setup and structured data modeling to advanced Vision, NLP, and reinforcement learning concepts. Reinforce key ideas in model training, evaluation, deployment strategies, business integration, and lifecycle management while demonstrating your ability to design, deploy, and monitor end-to-end machine learning solutions using Google Cloud Vertex AI through a comprehensive final assessment.