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: Let's 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
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