Quick Facts

particular	details
Medium of instructions English	Mode of learning Self study	Mode of Delivery Video and Text Based

Course overview

Big data is the enormous amount of data that organizations have access to as a result of its scale and complexity, which makes it difficult for many analytics tools to handle or evaluate. The amount of data acquired, the frequency at which it is made available to an organization for evaluation, and the complexity or variety of that data can all be aided by tools for big data. The Google Cloud Big Data and Machine Learning Fundamentals online certification was created by Google Cloud and is taught by Google Cloud Training, which is provided through Coursera.

Google Cloud Big Data and Machine Learning Fundamentals online classes introduce the learners to the methods associated with big data and machine learning services on Google Cloud and products that enable the data-to-AI lifecycles. The Google Cloud Big Data and Machine Learning Fundamentals online course covers the steps, difficulties, and advantages of establishing a big data infrastructure and machine learning models using functionalities of Vertex AI on Google Cloud as well as the characteristics of tools like Qwiklabs, Apache Beam, AutoML, TensorFlow, BigQuery, and others.

Course and certificate fees

Google Cloud Big Data and Machine Learning Fundamentals Fee Details -

Description	Total Fee in INR
Course Fee, 1 month	Rs. 4,117
Course Fee, 3 months	Rs. 8,234
Course Fee, 6 months	Rs. 12,352

certificate availability

Yes

certificate providing authority

Coursera

Who it is for

Ai developer Data analyst Data scientist Cloud engineer It engineer Data engineer

What you will learn

Knowledge of big data Machine learning Knowledge of data visualization Knowledge of cloud computing

After completing the Google Cloud Big Data and Machine Learning Fundamentals certification course, learners will gain a solid understanding of the core principles and methodologies associated with google cloud and big data for operations like cloud computing, machine learning, big data analytics, and data visualization. Learners will explore functionalities of Qwiklabs, Vertex AI, BigQuery, TensorFlow, Locker, data studio, and AutoML for activities like big data analysis and data visualization. Learners will study strategies associated with data operations like data engineering, data preparation, and data streaming. Learners will also study techniques for model training, model evaluation, model deployment, and model monitoring.

The syllabus

Module 1: Course Introduction

Video

Course Introduction

Reading

Reading list

Module 2: Big Data and Machine Learning on Google Cloud

Video

Introduction
Compute
Storage
The history of big data and ML products
Big data and ML product categories
Customer example: Gojek
Getting Started with Google Cloud Platform and Qwiklabs
Lab introduction: Exploring a BigQuery Public Dataset
Summary

Reading

Reading list

Practice Exercise

Quiz

Module 3: Data Engineering for Streaming Data

Video

Introduction
Big data challenges
Message-oriented architecture
Designing streaming pipelines with Apache Beam
Implementing streaming pipelines on Cloud Dataflow
Visualization with Looker
Visualization with Data Studio
Lab introduction: Creating a streaming data pipeline for a Real-Time dashboard with Dataflow
Summary