Course Details
Mastering GCP Machine Learning:From Fundamentals to Certification
Data Science
Created On
July 06, 2023Description
GCP Machine Learning is Google's suite of tools and services for developing, training, and deploying machine learning models. It offers pre-built models, APIs, and powerful tools like TensorFlow for customization. With scalable infrastructure and integration with other GCP services, it enables developers to build intelligent applications and extract insights from data.
Overview
This course provides students with a comprehensive understanding of GCP Machine Learning, covering key areas such as fundamentals, tools and services, model building and evaluation, speech-to-text and text-to-speech applications, MLOps, and exam preparation. Through hands-on exercises and practical assignments, students will gain the skills needed to design, build, and deploy machine learning models on the Google Cloud Platform, preparing them for the GCP Machine Learning Certification exam
Features
- Course material
- Course resources
- Comprehensive Coverage
- Hands-on Learning
- On demand recorded videos
- Practical exercises
- Quizzes
- Assignments
- Course completion certificate
- Industry-Relevant Skills
- GCP Integration
- Project-Based Learning
What you'll learn
- Introduction to Big Data and Machine Learning
- Building Personalized Recommendation Systems
- Machine Learning with BigQuery
- Key Features of BigQuery
- Exploring BigQuery's Features and Capabilities
- Features and Functionality of PubSub
- Leveraging AI Platform for Machine Learning Workloads
- Data Preparation and Cleaning with DataPrep
- Understanding the Machine Learning Workflow
- Introduction to Google's Vertex AI
- Understanding Pricing for Google Cloud's Machine Learning Services
- Managing Machine Learning Datasets with Vertex AI
- Hands-on Experience with Managed Datasets
- Converting Speech to Text using the UI Console
- Speech-to-Text Conversion with gcloud Command Line
- Implementing Speech-to-Text with Python Library
Prerequisites
Curriculum
- 11 modules
Overview of the Course Syllabus
Key Concepts and Skills to Master
Importance of Compute Power in Machine Learning Workloads
Significance of Storage, Networking, and Security in ML Environments
Exploring Big Data and Tools for BigQuery
Fundamentals of Recommendation Systems
GCP Products for Building Recommendation Systems
Hands-on Practice - Querying Public Datasets in BigQuery
Hands-on Practice - Loading and Querying Data
Key Points of BigQuery ML
Features of BigQuery for Machine Learning
Example of Using BigQuery GIS
Workflow of Machine Learning Process
Overview of the Machine Learning Process
Creating Models with BigQuery ML
Evaluating Models with BigQuery ML
Predicting with Models in BigQuery ML
Understanding Dataflow Pipeline Basics
Features and Capabilities of PubSub
Key Concepts in Dataflow Pipelines
Practical Exercise - Sending and Receiving Messages with gcloud
Exploring Google Dataflow
Exploring Google's AI Platform
Data Preparation with DataPrep Tool
Overview of the Machine Learning Workflow
Introduction to Google's Vertex AI
Understanding Pricing for Machine Learning Services
Dataset Management with Vertex AI
Hands-on Experience with Managed Datasets
Model Building and Training with Vertex AI
Training a Model with AutoML in Vertex AI
AutoML Model Training and Deployment
Making Predictions with AutoML Models
Resource Cleanup Best Practices
Introduction to Custom Models in Vertex AI
Enabling APIs for Vertex AI
Creating a Notebook Instance in Vertex AI
Creating a Container Image for Deployment
Training a Custom ML Model with Vertex AI
Deploying a Trained ML Model
Making Predictions with Deployed Models
Overview of Image Classification Applications
Creating a Dataset for Yoga Pose Images
Building an Image Classification Model with Vertex AI
Deploying and Making Predictions with the Model
Introduction to Vertex AI SDK
Walkthrough of Vertex AI SDK Code
Hands-on Exercises with Vertex AI SDK
Introduction to Hyperparameter Tuning
Optimizing Model Performance with Hyperparameter Tuning
Understanding the Mechanism of Hyperparameter Tuning
Hands-on Exercises for Hyperparameter Tuning
Converting Speech to Text using the UI Console
Converting Speech to Text using the gcloud Command
Converting Speech to Text using a Python Library
Exploring Different Types of Speech Recognition
Demonstrating Asynchronous Speech Recognition
Converting Streaming Speech to Text
Introduction to Cloud Text-to-Speech
Hands-on Experience with Text-to-Speech using Python
Listing Available Voices for Text-to-Speech
Understanding the Basics of Text-to-Speech
Converting SSML to Audio with Text-to-Speech
Translating Text from an Image
Introduction to Cloud Natural Language AI
Analyzing Sentiment in Text
Sentiment Analysis in Different Languages
Analyzing Entities in a Text String
Text Classification with Natural Language AI
Syntax Analysis with Natural Language AI
Analyzing Entity Sentiment in Text
Introduction to Video Intelligence
Understanding the Problem Scope in AI Systems Design
Handling Data Changes in AI Systems
Dealing with System Failures in AI Systems
Managing Concept Drift in AI Systems
Identifying Risks in ML Systems
Key Rules to Follow in Machine Learning Systems
Data Best Practices in AI System Design
Incorporating Hybrid ML Models in System Design
Exploring Kubeflow for AI System Development
Understanding the Basics of MLOps
Creating GKE Cluster with Kubeflow
Defining Custom Component using KFP SDK
Running/Deploying Pipeline
Continuous Integration and Continuous Deployment for MLOps
Triggering MLOps Pipelines with Cloud Build
Understanding the Principles and Importance of Explainable AI
Utilizing Vertex Explainable AI for Interpretable Machine Learning Models
Questions and Answers
Instructors
Skoliko Faculty
₹6550.00
- 11 Modules
- 27 Hours
- Data Science
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