Hands on Google cloud platform(GCP) Data Engineer

Hands on Google cloud platform(GCP) – Data Engineer, available at $39.99, has an average rating of 3.45, with 54 lectures, based on 193 reviews, and has 946 subscribers.

You will learn about Understand major components of GCP Understand Machine learning fundamentals Get introduced to BigQuery Know how to use tensorflow on cloud Learn about Hadoop and its cloud Counter parts Get basics set to be certified This course is ideal for individuals who are Anyone wanting to learn about Google cloud or Anyone interested to take up data engineer certification or Anyone interested in learning machine learning and tensorflow or Anyone looking to migrate from hadoop to google cloud or Anyone wanting to learning something new and interesting It is particularly useful for Anyone wanting to learn about Google cloud or Anyone interested to take up data engineer certification or Anyone interested in learning machine learning and tensorflow or Anyone looking to migrate from hadoop to google cloud or Anyone wanting to learning something new and interesting.

Enroll now: Hands on Google cloud platform(GCP) – Data Engineer

Summary

Title: Hands on Google cloud platform(GCP) – Data Engineer

Price: $39.99

Average Rating: 3.45

Number of Lectures: 54

Number of Published Lectures: 54

Number of Curriculum Items: 54

Number of Published Curriculum Objects: 54

Original Price: $44.99

Quality Status: approved

Status: Live

What You Will Learn

Understand major components of GCP

Understand Machine learning fundamentals

Get introduced to BigQuery

Know how to use tensorflow on cloud

Learn about Hadoop and its cloud Counter parts

Get basics set to be certified

Who Should Attend

Anyone wanting to learn about Google cloud

Anyone interested to take up data engineer certification

Anyone interested in learning machine learning and tensorflow

Anyone looking to migrate from hadoop to google cloud

Anyone wanting to learning something new and interesting

Target Audiences

Anyone wanting to learn about Google cloud

Anyone interested to take up data engineer certification

Anyone interested in learning machine learning and tensorflow

Anyone looking to migrate from hadoop to google cloud

Anyone wanting to learning something new and interesting

This course is exclusively designed by NoTEZ to teach about GCP in most simplest way possible.?

Students who enrolled? for our previous courses on GCP had requested more in the series and hence this course is live.?

If you havn’t enrolled for our Other courses, enroll today n start exploring more.?

This course is designed to give idea about Google’ s data engineer certification But Not limited to just that.?

This course will give you indepth practical knowledge on various components of GCP.?

Enroll today &? explore more

Course Overview

Module 1- Introduction

?All about Google certification, Overview –Data Engineer Certification, What is and why to use CLOUD?

Module 2 – Hands on GCP

Labs

Module 3 –Hadoop

Introduction to,Hadoop, Hadoop-bigger picture, Hadoop- In detail, HIVE, HBASE,PIG

Module 4- Compute

Introduction to Computing, Google compute engine(GCE), Preemptible Virtual Machine, Google APP engine (G A E), Google container engine ?,Kubernetes ( GKE),Comparison,Labs

Module 5- Storage

Introduction, Cloud Storage, BIGQUERY, Data Store, More on cloud storage, Working with cloud storage, Transfer service, Cloud SQL, Cloud SQL – PROXY, Cloud Spanner, Hot Spotting, Data Types, Transactions , Staleness, Labs

Module 6-Big Table

Big Table Introduction, Columnar store, Denormalized Storage, CRUD Operations, Column families, Choice of BigTable

Module 7-Datalab

Module 8-Pub/Sub

Module 9- Dataflow

Module 10-BigQuery

BigQuery Data Model, Querying & Viewing,Labs

Module 11-Machine Learning & Tensorflow

Introduction to Machine Learning, Typical usage of Mechine Learning, Types,

The Mechine Learning block diagram, Deep learning & Neural Networks, Labels, Understanding? Tenser Flow, Computational Graphs, Tensors, Linear regression , Placeholders & variables,

Image processing in Tensor Flow, Image as tensors, M-NIST – Introduction, K-nearest neighbors Algorithm, L1 distance, Steps in K- nearest neighbour implementation, Neural Networks in Real Time, Learning regression and learning XOR

Linear Regression, Gradient descent, Logistic Regression, Logit, Activation function,Softmax, Cost function -Cross entropy,Labs

Module 12-Operation & Security

Stack driver, Stack driver Logging, Cloud Deployment Manager, Cloud Endpoints, Cloud IAM ,API keys, Cloud IAM- Extended, Labs,

Course Curriculum

Chapter 1: Introduction

Lecture 1: Introduction

Lecture 2: Details on Google certification

Lecture 3: Overview of Data engineer certification

Lecture 4: Why cloud?

Chapter 2: Dive into practicals

Lecture 1: Lab 1: Creating a Google cloud account

Lecture 2: Working with cloud Shell

Chapter 3: The Mighty HADOOP

Lecture 1: Hello Hadoop

Lecture 2: Hadoop-2

Lecture 3: Hadoop -3

Lecture 4: Hadoop ecosystem and GCP equivalent

Chapter 4: Cloud compute engine

Lecture 1: Introduction

Lecture 2: preemtible VMs

Lecture 3: Google App Engine(GAE)

Lecture 4: Lab 1: Creating VM instance

Lecture 5: Lab 2: Dealing with VM

Lecture 6: Lab 3 : VM Instance Via Command Line

Chapter 5: Lets Explore Storage

Lecture 1: Get introduced to cloud storage

Lecture 2: A bit more about storage

Lecture 3: All about datastore

Lecture 4: Cloud SQL

Lecture 5: Cloud Spanner

Lecture 6: Lab : Cloud Spanner

Lecture 7: Lab : Storage buckets

Lecture 8: Lab : Cloud SQL

Chapter 6: Big Table- columnar store

Lecture 1: Big Table introduction

Lecture 2: CRUD operations & choice of Big Table

Chapter 7: Cloud Datalab

Lecture 1: Datalab

Chapter 8: PUB/SUB

Lecture 1: Streaming Data -PUB/SUB

Chapter 9: Dataflow

Lecture 1: Dataflow

Chapter 10: BigQuery

Lecture 1: More GCP – BigQuery

Lecture 2: Lab : BigQuery – Loading CSV data

Lecture 3: Lab : Querying Table

Chapter 11: Machine Learning & Tensorflow

Lecture 1: Hello Machine learning

Lecture 2: The ML block diagram, Deep learning & neural networks

Lecture 3: Lab: simple Math in Tensorflow

Lecture 4: Understanding Tensorflow

Lecture 5: Tensors

Lecture 6: Lab : Tensors

Lecture 7: Linear regression basics

Lecture 8: Lab: Simple regression example

Lecture 9: Placeholders & variables

Lecture 10: Lab: simple math with placeholders

Lecture 11: Lab: Dealing with variables

Lecture 12: Image processing in TensorFlow

Lecture 13: Lab : work with images

Lecture 14: Image as Tensors

Lecture 15: lab: work with images- part 2

Lecture 16: MNIST & KNN

Lecture 17: L1 Distance

Lecture 18: Neural Network in Real Time,Learning regression and XOR

Lecture 19: Gradient descent

Lecture 20: A bit on logistic regression

Chapter 12: Add on: operations & security

Lecture 1: Stack Driver

Lecture 2: Cloud IAM

Instructors

Notez (Rent a Mind)
Notez

Rating Distribution

1 stars: 11 votes

2 stars: 11 votes

3 stars: 24 votes

4 stars: 27 votes

5 stars: 120 votes

Frequently Asked Questions

How long do I have access to the course materials?

You can view and review the lecture materials indefinitely, like an on-demand channel.

Can I take my courses with me wherever I go?

Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!