Master Google Cloud Platform: Big Data & Machine Learning Fundamentals – Complete Certification Course

In the modern world of cloud computing, Google Cloud Platform (GCP) stands out as a leading
platform for building, managing, and scaling applications, particularly in the realms of big data and
machine learning (ML). Google’s powerful infrastructure, combined with its innovative big data and
ML tools, allows businesses to leverage their data and gain actionable insights for better decision-
making.

ENCODE-IT’s Google Cloud Platform (GCP) Big Data and Machine Learning Fundamentals
certification course is designed for individuals who want to gain foundational knowledge of GCP and
its data processing and machine learning capabilities. This course provides a deep dive into key GCP
services such as BigQuery, Dataflow, Pub/Sub, and AI Platform, giving you the hands-on experience
needed to manage data at scale and implement machine learning models in the cloud.
Whether you're an aspiring data scientist, cloud engineer, or data analyst, this course will help you
build expertise in Google Cloud’s big data and ML tools, allowing you to efficiently process vast
amounts of data and deploy machine learning models with ease.

Salary Scale in India
As cloud technologies and machine learning continue to revolutionize industries, the demand for
GCP professionals is on the rise. Entry-level professionals working with Google Cloud Platform,
particularly in Big Data and Machine Learning, can expect to earn between ₹8,00,000 and
₹15,00,000 annually. Experienced professionals in roles such as Cloud Data Engineers, Machine
Learning Engineers, and Data Scientists specializing in GCP can earn salaries upwards of ₹20,00,000
to ₹35,00,000 per year.

Placement Assistance & Certification
Upon successful completion of the Google Cloud Platform Big Data and Machine Learning
Fundamentals course, you will receive an official ENCODE-IT certification, validating your ability to
work with Google Cloud's big data and machine learning services. Additionally, ENCODE-IT provides
placement assistance to help you find career opportunities with top tech companies that are
actively seeking professionals skilled in cloud technologies and machine learning.

Course Curriculum

1. Introduction to Google Cloud Platform (GCP) and Cloud Computing
 Overview of Google Cloud Platform and its Key Services
 The Benefits of Using Cloud Computing for Data Storage and Processing
 Exploring Google Cloud's Core Components: Compute, Storage, Networking
 GCP Console and Tools: Cloud SDK, Cloud Shell, and Cloud Console
 Introduction to Google Cloud Billing and Cost Management
 Setting Up Your GCP Account and Creating Projects

2. Google Cloud Storage and Data Management
 Overview of Google Cloud Storage (GCS)

 Creating, Managing, and Accessing Buckets in GCS
 GCS for Big Data: Storing Large Datasets and Objects
 Working with Data Formats in GCS (CSV, JSON, Avro, Parquet)
 Managing Permissions and Access Control with Identity and Access Management (IAM)
 Data Lifecycle Management: Archiving and Deleting Data Automatically

3. Introduction to Big Data on GCP
 Introduction to Big Data and Its Importance in Modern Applications
 GCP Big Data Services Overview: BigQuery, Dataflow, Pub/Sub
 Setting Up BigQuery for Data Analytics
 Querying Data Using BigQuery: SQL-Based Queries on Large Datasets
 Integrating BigQuery with GCS for Data Storage and Analysis
 Real-Time Data Streaming with Pub/Sub and Dataflow
 Scaling Big Data Applications in GCP

4. Data Ingestion and Processing with Google Cloud
 Overview of Dataflow: Stream and Batch Data Processing
 Setting Up and Running Dataflow Jobs for ETL Pipelines
 Real-Time Streaming with Google Cloud Pub/Sub
 Using Cloud Functions for Data Integration and Event-Driven Architecture
 Introduction to Apache Beam: A Unified Programming Model for Data Processing
 Automating Data Ingestion and Transformation Pipelines
 Data Quality and Data Governance on GCP

5. Machine Learning with Google Cloud
 Introduction to Machine Learning (ML) and its Application in Cloud
 Google Cloud AI and ML Tools Overview: AI Platform, TensorFlow, AutoML
 Setting Up and Using AI Platform for ML Model Training
 Building ML Models Using TensorFlow on GCP
 Introduction to AutoML: Custom ML Models for Beginners
 Training and Deploying ML Models with AI Platform
 Evaluating and Tuning ML Models for Optimal Performance

6. Advanced Machine Learning Techniques on GCP
 Building and Deploying Scalable ML Models with Google Kubernetes Engine (GKE)

 Using TensorFlow Extended (TFX) for End-to-End ML Pipelines
 Introduction to Google Cloud AI Hub: Sharing and Managing ML Models
 Natural Language Processing (NLP) with Cloud Natural Language API
 Computer Vision with Cloud Vision API
 Speech Recognition and Synthesis with Cloud Speech-to-Text and Text-to-Speech APIs
 Building Custom ML Solutions Using Google Cloud APIs

7. Data Analytics and Visualization on GCP
 Introduction to Data Analytics in the Cloud
 Using BigQuery for Interactive Analytics on Large Datasets
 Analyzing Data with BigQuery ML for Machine Learning Queries
 Visualizing Data with Google Data Studio and Looker
 Creating Dashboards and Reports from BigQuery Data
 Connecting GCP Services with Third-Party Data Analytics Tools
 Leveraging GCP’s Data Visualization Features for Business Intelligence

8. Scaling Big Data and ML Workloads on GCP
 Scaling BigQuery Queries for Performance and Cost Efficiency
 Managing Big Data Workloads with Google Kubernetes Engine (GKE)
 Using Google Cloud Dataproc for Apache Hadoop and Spark on GCP
 Optimizing Cloud Resources with Autoscaling and Load Balancing
 Best Practices for Data Security, Compliance, and Privacy on GCP
 Cost Optimization Strategies for Big Data and ML Workloads
 Monitoring and Debugging Big Data and ML Jobs with Google Stackdriver

9. Security and Compliance for Big Data and Machine Learning
 Data Security and Encryption on Google Cloud
 Implementing Identity and Access Management (IAM) for GCP Services
 Managing Secrets with Google Cloud Secret Manager
 Data Compliance and Regulatory Requirements (GDPR, HIPAA)
 Auditing Data and Machine Learning Workflows on GCP
 Best Practices for Securing ML Models and Data Pipelines

10. Real-World Projects and Case Studies
 Building a Data Pipeline for Real-Time Analytics on GCP

 Implementing a Machine Learning Model to Predict Business Outcomes
 Deploying a Large-Scale Data Warehouse Solution with BigQuery
 Real-Time Streaming and Processing with Pub/Sub and Dataflow
 End-to-End Data Science Workflow Using AI Platform and BigQuery
 Building a Scalable ML Model Deployment Solution with GKE and TensorFlow

11. Final Project and Certification Exam
 Final Project: Designing a Complete Big Data and Machine Learning Solution on GCP
 Incorporating Data Ingestion, Processing, Analytics, and ML Model Deployment
 Optimizing for Performance, Security, and Cost Efficiency
 Final Exam: Comprehensive Evaluation of Google Cloud Big Data and ML Skills
 Certification of Completion from ENCODE-IT and Job Placement Assistance

Key Features
 Tools & Platforms: Google Cloud Platform, BigQuery, Dataflow, Pub/Sub, AI Platform,
TensorFlow, AutoML
 Real-World Projects: Hands-on experience building big data pipelines, deploying machine
learning models, and managing data in GCP
 Certification & Placement Support: Google Cloud certification and job placement assistance
 Expert Instructors: Learn from industry professionals with deep expertise in GCP and cloud
technologies
 Career Advancement: Acquire in-demand skills for roles in data science, cloud engineering,
and machine learning

Why Choose ENCODE-IT for Google Cloud Platform Big Data & Machine Learning Fundamentals?
ENCODE-IT’s Google Cloud Platform Big Data and Machine Learning Fundamentals course provides
you with the knowledge and practical experience needed to leverage GCP for data analytics and
machine learning. From managing large datasets to building and deploying machine learning models,
this course will prepare you to take full advantage of Google Cloud’s powerful services. Whether
you're starting your career in cloud computing or advancing your skill set, this course will help you
excel in the growing field of cloud-based big data and ML.