Tensorflow on Google's Cloud Platform for Data Engineers
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Tensorflow on Google's Cloud Platform for Data Engineers

The Fourth Course in a Series for Attaining the Google Certified Data Engineer
Best Seller
4.8 (4 ratings)
Instead of using a simple lifetime average, Udemy calculates a course's star rating by considering a number of different factors such as the number of ratings, the age of ratings, and the likelihood of fraudulent ratings.
56 students enrolled
Created by Mike West
Last updated 8/2017
English
Current price: $10 Original price: $25 Discount: 60% off
5 hours left at this price!
30-Day Money-Back Guarantee
Includes:
  • 1 hour on-demand video
  • 12 Articles
  • 10 Supplemental Resources
  • Full lifetime access
  • Access on mobile and TV
  • Certificate of Completion
What Will I Learn?
  • You'll understand the basics of TensorFlow.
  • You'll be able to build TensorFlow models on Google's Cloud.
  • You'll be prepared for TensorFlow questions on the Google Certified Data Engineering Exam.
  • Upon completion you'll know how to build machine learning models inside Google's Cloud.
View Curriculum
Requirements
  • A solid understanding of Python and the core libraries for machine learning.
  • A solid understanding of the principles of machine learning.
  • Strong familiarity with SQL
  • I'd highly recommend taking my data engineering courses in order.
Description

Welcome to Tensorflow on the Google Cloud Platform for Data Engineers This is the fourth course in a series of courses designed to help you attain the coveted Google Certified Data Engineer. 

Additionally, the series of courses is going to show you the role of the data engineer on the Google Cloud Platform

NOTE: This is not a course on how to develop machine learning models with TensorFlow. This is a very targeted course on TensorFlow for data engineers.  My goal is to give data engineers what they need to know for the exam and provide learners with the foundations of TensorFlow on Google's Cloud Platform. 

At this juncture the Google Certified Data Engineer is the only real world certification for data and machine learning engineers.

TensorFlow is an open source software library created by Goggle for doing graph-based computations quickly. It does this by utilizing the GPU(Graphics Processing Unit)  and also making it easy to distribute the work across multiple GPUs and computers.

Tensors, in general, are simply arrays of numbers, or functions, that transform according to certain rules under a change of conditions. Nodes in the graphs represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. 

In the course you'll discover how to apply TensorFlow to machine learning, the concept of a Tensor, the anatomy of a simple program, basic constructs such as constants, variables, placeholders, sessions and the computation graph.

You'll work with basic math operations and image transformations to see how common computations are performed.

You'll learn TensorFlow within the context of the Google Cloud Platform

                                                             *Five Reasons to take this Course.*

1) You Want to be a Data Engineer 

It's the number one job in the world. (not just within the computer space) The growth potential career wise is second to none. You want the freedom to move anywhere you'd like. You want to be compensated for your efforts. You want to be able to work remotely. The list of benefits goes on. 

2) The Google Certified Data Engineer 

Google is always ahead of the game. If you were to look back at a timeline of their accomplishments in the data space you might believe they have a crystal ball. They've been a decade ahead of everyone.  Now, they are the first and the only cloud vendor to have a data engineering certification. With their track record I'll go with Google. 

3) The Growth of Data is Insane 

Ninety percent of all the world's data has been created in the last two years. Business around the world generate approximately 450 billions transactions a day. The amount of data collected by all organizations is approximately 2.5 Exabytes a day. That number doubles every month. 

4) TensorFlow in Plain English

TensorFlow is a low level language.  The basic concept of a tenor is hard to grasp if you aren't familiar with neural networks. In the course we will break down TensorFlow in to bite sized pieces ensuring you learn the fundamentals first. After we've built a base understanding of tensors and how they flow we will move on to more complicated examples. 

5) You want to be ahead of the Curve 

The data engineer role is fairly new.  While your learning, building your skills and becoming certified you are also the first to be part of this burgeoning field.  You know that the first to be certified means the first to be hired and first to receive the top compensation package. 

Thank you for your interest in Tensorflow on the Google Cloud Platform for Data Engineers and we will see you in the course!!




Who is the target audience?
  • If you're preparing for the Google Certified Data Engineering exam then this course is for you.
  • Anyone wanting to learn how to build TensorFlow models in Google's Cloud.
Compare to Other TensorFlow Courses
Curriculum For This Course
44 Lectures
01:16:30
+
Welcome to TensorFlow
11 Lectures 11:47

This course is about TensorFlow. 

This is Google's computational engine for machine learning. 

Preview 01:25

In this lecture I discuss created a machine learning course for data engineers. 

This course will be very targeted and specific to questions on the exam. 

Preview 00:55

Is this course right for you? 

Let's find out in this lesson. 

Is this Course for You?
01:36

In this lesson I answer a few questions about the course and the exam. 

Instructor Course Q&A
00:48

Let's define what an array is. 

The very basics. 


Preview 01:18

Tensors are multi-dimensional arrays. 

Let's find out what that is in this lesson. 

Preview 01:24

Tensors and nodes together create a TensorFlow network. 

These networks are represented as graphs in TensorFlow. 

How Tensors Flow
01:01

In this lesson let's add some real numbers to our graph. 

This will help show how tensor move through the graph with data in them. 

Real Numbers Flowing through our Graph
01:04

Every programming language has hello world and TensorFlow is no different. 

Let's walk through the most basic TensorFlow example we can. 

Preview 01:35

Course Downloads
00:20

Summary
00:20

Quiz
6 questions
+
Up and Running in Cloud Datalab
5 Lectures 11:17

Let's learn how to create a datalab vm for the course. 


Preview 05:40

Once we close our datalab VM we will need to be able to log back into it in order to build our models. 

Let's see how simple it is to call our existing datalab notebook from cloud shell. 

Reconnect to Datalab Virtual Machine
01:20

In this lecture let's learn how to upload and download our cloud datalab notebooks. 

Download/Upload Notebooks to Datalab
02:41

In this lesson let's set up a virtual machine that we will use throughout the course to host our datalab notebooks. 

Lab: Up and Running with Datalab
01:09

Summary
00:27

Quiz
5 questions
+
TensorFlow Basics
16 Lectures 26:55

Let's learn about the TensorFlow toolkit in this lesson. 

The TensorFlow Code Base
01:48

All TensorFlow graphs feed in one direction and that is forward through the graph. 

Let's learn how this operates in this lesson. 

Forward Feeding Graphs
01:34

Machine learning involves lots of iteration but TensorFlow doesn't. 

Let's learn how Tensorflow handles that in this lesson. 

Handling Iteration in TensorFlow Graphs
01:28

Every TensorFlow program has two steps. 

In this lesson let's learn what they are. 

2 Steps in Every TensorFlow Program
03:56

Real world machine learning is machine learning at scale. 

Let's learn how TensorFlow handles that in this lesson. 

Modeling Larger Computational Graphs
01:18

In this lesson let's add more resources to our datalab vm. 

Resizing After High Utilization Warning
00:58

In this example let's walk through a very simple end to end model in TensorFlow. 

Simple End to End Example
02:03

Let's learn about the dimension of Tensors in this lesson. 

Tensor Dimensions
01:54

In this lesson let's learn what placeholders are and how to use them. 

Placeholders
02:17

In this lesson let's learn how to pass two core parameters into our session. 

Session Parameters: Fetch and Feed_Dict
00:48

Let's learn what a node's life cycle in this brief lesson. 

Node Life Cycle
01:56

A tensor have three properties. 

Let's learn what they are in this lesson. 

Tensor Properties
01:46

Let's convert our non-tensored arrays to tensors. 


Convert to Tensors
01:17

In this lesson let's learn how to enable logging on our TensorFlow models. 

Enabling Logging with TensorFlow
01:12

In this lesson let's work through the most simple TensorFlow example we can. 

Lab: Hello World in TensorFlow
01:31

Summary
01:08

Quiz
10 questions
+
TensorFlow Demos
12 Lectures 26:33

In this lesson let's view the difference between executing a numpy array in Python and a tensor in TensorFlow. 

We will do all of this in our datalab notebooks. 

Numpy Vs TensorFlow
02:01

We are going to do this in our datalab notebooks but our data will be in BigQuery. 

Dataset Creation and Exploration
04:39

Let's massage our data and create clean datasets for our machine learning models. 

Data Wrangling
01:48

In this lesson let'walk through a linear regression model in TensorFlow. 

Linear Regression in TensorFlow
02:04

A strange example from Google but it's been on a few exams so let's cover it here. 

The Mandelbrot Set
01:32

In this lesson let's learn about overfitting and how to avoid it. 

Overfitting and How to Correct it
03:08

Real world machine learning is all about scale. 

In this lesson let's learn about scale on GCP. 

Using Cloud Machine Learning
01:39

Once we have our model it's time to pack it up. 

Let's learn how to package a completed model in this lesson. 

Model Packaging
03:22

In this lesson let's learn the basics of creating a prediction service. 

Creating a Server Input Function
00:53

In this lesson let's complete a lab specific to linear regression in TensorFlow. 

Lab: Linear Regression in TensorFlow
01:36

Let's walk through the lab step by step. 

Lab Review: Linear Regression
03:06

Summary
00:43

Section Quiz
10 questions

Sample Exam Questions
6 questions
About the Instructor
Mike West
4.2 Average rating
2,988 Reviews
49,492 Students
42 Courses
SQL Server and Machine Learning Evangelist

I've been a production SQL Server DBA most of my career.

I've worked with databases for over two decades. I've worked for or consulted with over 50 different companies as a full time employee or consultant. Fortune 500 as well as several small to mid-size companies. Some include: Georgia Pacific, SunTrust, Reed Construction Data, Building Systems Design, NetCertainty, The Home Shopping Network, SwingVote, Atlanta Gas and Light and Northrup Grumman.

Experience, education and passion

I learn something almost every day. I work with insanely smart people. I'm a voracious learner of all things SQL Server and I'm passionate about sharing what I've learned. My area of concentration is performance tuning. SQL Server is like an exotic sports car, it will run just fine in anyone's hands but put it in the hands of skilled tuner and it will perform like a race car.

Certifications

Certifications are like college degrees, they are a great starting points to begin learning. I'm a Microsoft Certified Database Administrator (MCDBA), Microsoft Certified System Engineer (MCSE) and Microsoft Certified Trainer (MCT).

Personal

Born in Ohio, raised and educated in Pennsylvania, I currently reside in Atlanta with my wife and two children.