Do I need to complete Courses 1 and 2 of the specialization first?

Yes, it is strongly recommended. This course assumes familiarity with data preparation concepts from Course 1 and core ML modeling principles from Course 2. Learners who skip these may find certain concepts harder to follow.

Is any coding required in this course?

No. All platform setup, dataset upload, model training, deployment, and monitoring is performed entirely through the Google Cloud web console — no terminal, CLI, or programming is involved at any point.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

AutoML: Build ML Models without Code

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AutoML: Build ML Models without Code

Ce cours fait partie de Spécialisation "No-Code Data Science and Machine Learning"

Instructeur : Edureka

Inclus avec

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

8 heures à compléter

Planning flexible

Apprenez à votre propre rythme

4 modules

Obtenez un aperçu d'un sujet et apprenez les principes fondamentaux.

niveau Intermédiaire

Expérience recommandée

8 heures à compléter

Planning flexible

Apprenez à votre propre rythme

Ce que vous apprendrez

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

Compétences que vous acquerrez

Catégorie : Natural Language Processing
Catégorie : Artificial Intelligence and Machine Learning (AI/ML)
Catégorie : Computer Vision
Catégorie : Machine Learning Software
Catégorie : Text Mining
Catégorie : Cloud Deployment
Catégorie : Machine Learning
Catégorie : Google Cloud Platform
Catégorie : Predictive Modeling
Catégorie : Model Evaluation
Catégorie : Feature Engineering
Catégorie : Applied Machine Learning
Catégorie : Data Science
Catégorie : Image Analysis

Outils que vous découvrirez

Catégorie : No-Code Development
Catégorie : Model Deployment
Catégorie : Google Sheets

Détails à connaître

Certificat partageable

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Ce cours fait partie de la Spécialisation "No-Code Data Science and Machine Learning"

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Il y a 4 modules dans ce cours

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.

Inclus

19 vidéos6 lectures4 devoirs

19 vidéos 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 lectures 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 devoirs Total 33 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
Graded Assignment: AutoML Platform Setup and Hands-On Experience 15 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.

Inclus

8 vidéos4 lectures4 devoirs

8 vidéos 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 lectures 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 devoirs Total 33 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
Graded Assignment: Advanced Model Training - Vision, NLP & RL Concepts 15 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.

Inclus

9 vidéos4 lectures4 devoirs

9 vidéos 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 lectures 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 devoirs Total 33 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
Graded Assignment: Model Deployment and Business Integration 15 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.

Inclus

1 vidéo1 lecture2 devoirs

Obtenez un certificat professionnel

Ajoutez ce titre à votre profil LinkedIn, à votre curriculum vitae ou à votre CV. Partagez-le sur les médias sociaux et dans votre évaluation des performances.

Instructeur

Edureka

157 Cours 144 451 apprenants

Offert par

Edureka

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Foire Aux Questions

AutoML automates the machine learning pipeline — including data preprocessing, feature engineering, model selection, and hyperparameter tuning — enabling anyone to build production-grade ML models without deep technical expertise or code.

Google Vertex AI is Google Cloud's unified ML platform that brings AutoML and custom ML tools together in one place. In this course, you'll use Vertex AI's AutoML capabilities entirely through the web-based console — no command-line or API usage required.

Ideal for data analysts, product managers, business intelligence professionals, domain experts, and non-technical teams who want to leverage cloud-based ML to automate predictions and integrate AI into real business workflows.

Every platform task — from configuring Vertex AI to training and deploying models — is demonstrated step-by-step in instructor-led videos recorded directly in the Google Cloud console. You're expected to set up your own Google Cloud account, follow along with the instructor in real time, and pause the video whenever you need to complete a step, review a configuration, or wait for a model training job to finish.

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

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