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Explore and Evaluate Models using Model Garden GSP1166

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Lesson extensions

Lessons List | 27 Lesson
Create Text Embeddings for a Vector Store using LangChain GSP1233
00:09:48

Create Text Embeddings for a Vector Store using LangChain GSP1233
Vertex AI Qwik Start GSP917
00:15:47

Vertex AI Qwik Start GSP917
Vertex AI Workbench Notebook Qwik Start GSP076
00:19:04

Vertex AI Workbench Notebook Qwik Start GSP076
Getting Started with the Vertex AI Gemini API and Python SDK GSP1209
00:07:09

Getting Started with the Vertex AI Gemini API and Python SDK GSP1209
Vertex AI Tabular Data Qwik Start GSP1067
00:13:25

Vertex AI Tabular Data Qwik Start GSP1067
Machine Learning with TensorFlow in Vertex AI GSP273
00:34:14

Machine Learning with TensorFlow in Vertex AI GSP273
Getting Started with the Vertex AI Gemini API with cURL GSP1228
00:08:20

Getting Started with the Vertex AI Gemini API with cURL GSP1228
Process Documents with Python Using the Document AI API GSP925
00:12:16

Process Documents with Python Using the Document AI API GSP925
Using Gemini for Multimodal Retail Recommendations GSP1230
00:09:00

Using Gemini for Multimodal Retail Recommendations GSP1230
Multimodality with Gemini GSP1210
00:08:37

Multimodality with Gemini GSP1210
Vertex Pipelines Qwik Start GSP965
00:14:53

Vertex Pipelines Qwik Start GSP965
Identify Damaged Car Parts with Vertex AutoML Vision GSP972
00:18:59

Identify Damaged Car Parts with Vertex AutoML Vision GSP972
Predict Baby Weight with TensorFlow on Vertex AI GSP013
00:15:30

Predict Baby Weight with TensorFlow on Vertex AI GSP013
Identify Horses or Humans with TensorFlow and Vertex AI GSP634
00:31:07

Identify Horses or Humans with TensorFlow and Vertex AI GSP634
TFX on Google Cloud Vertex AI Pipelines GSP1023
00:22:02

TFX on Google Cloud Vertex AI Pipelines GSP1023
Introduction to Function Calling with Gemini GSP1227
00:08:43

Introduction to Function Calling with Gemini GSP1227
Vertex AI PaLM API Qwik Start GSP1155
00:08:23

Vertex AI PaLM API Qwik Start GSP1155
Multimodal Retrieval Augmented Generation RAG using the Vertex AI Gemini API GSP1231
00:14:18

Multimodal Retrieval Augmented Generation RAG using the Vertex AI Gemini API GSP1231
Using Gemini in Education GSP1232
00:10:05

Using Gemini in Education GSP1232
Deploy a Streamlit App Integrated with Gemini Pro on Cloud Run GSP1229
00:09:57

Deploy a Streamlit App Integrated with Gemini Pro on Cloud Run GSP1229
Safeguarding with Vertex AI Gemini API
00:08:20

Safeguarding with Vertex AI Gemini API
Running Distributed TensorFlow using Vertex AI GSP971
00:27:16

Running Distributed TensorFlow using Vertex AI GSP971
Integrate Search in Applications using Vertex AI Agent Builder GSP1152
00:27:06

Integrate Search in Applications using Vertex AI Agent Builder GSP1152
Explore and Evaluate Models using Model Garden GSP1166
00:06:52

Explore and Evaluate Models using Model Garden GSP1166
Classify Images with TensorFlow Convolutional Neural Networks GSP633
00:42:43

Classify Images with TensorFlow Convolutional Neural Networks GSP633
Introduction to Convolutions with TensorFlow GSP632
00:05:59

Introduction to Convolutions with TensorFlow GSP632
Classify Images of Cats and Dogs using Transfer Learning GSP900
00:18:32

Classify Images of Cats and Dogs using Transfer Learning GSP900

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Course Description

Vertex AI for tabular data course, in this course we will learn about Vertex AI for Tabular Data, focusing on the essential techniques and tools to manage and analyze structured datasets using Google's Vertex AI platform. Vertex AI is a powerful machine learning service that enables users to build, deploy, and scale models efficiently, particularly when working with tabular data. The course covers key topics such as data preprocessing, feature engineering, and selecting appropriate machine learning algorithms tailored for tabular datasets. You'll explore how to use Vertex AI's tools for training and evaluating models, automating machine learning workflows, and deploying models to production environments. Additionally, the course will guide you through best practices for data management, including data cleaning, normalization, and dealing with missing values. You'll also learn how to optimize models for better performance and interpret the results effectively.
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