Serverless Machine Learning with Tensorflow on Google Cloud Platform

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1 - Welcome to Serverless Machine Learning on Google Cloud Platform

01 - Welcome to the Course.mp4

02:22

02 - How to Think About Machine Learning.mp4

02:32

2 - Getting Started with Machine Learning

03 - What is Machine Learning (ML).mp4

07:13

04 - Types of ML.mp4

03:10

05 - The ML Pipeline.mp4

02:29

06 - Variants of ML model.mp4

07:22

07 - Framing a ML problem.mp4

02:55

08 - Playing with Machine Learning (ML).mp4

08:01

09 - Optimization.mp4

09:39

10 - A Neural Network Playground.mp4

18:44

11 - Combining Features.mp4

03:08

12 - Feature Engineering.mp4

03:19

13 - Image Models.mp4

05:23

14 - Effective ML.mp4

02:12

15 - What makes a good dataset.mp4

05:26

16 - Error Metrics.mp4

03:50

17 - Accuracy.mp4

02:58

18 - Precision and Recall.mp4

05:48

19 - Creating Machine Learning Datasets.mp4

03:03

20 - Splitting Dataset.mp4

06:56

21 - Python Notebooks.mp4

01:37

22 - Create ML Datasets Lab Overview.mp4

03:01

23 - Serverless Machine Learning - Lab 1 - Create ML datasets v1.3.mp4

00:10

24 - Create ML Datasets Lab Review.mp4

02:56

3 - Building ML models with Tensorflow

25 - Overview.mp4

01:04

26 - What is TensorFlow.mp4

05:21

27 - Core TensorFlow.mp4

05:46

28 - Getting Started with TensorFlow Lab Overview.mp4

00:07

29 - Serverless Machine Learning - Lab 2 - Getting Started with TensorFlow v1.3.mp4

00:10

30 - TensorFlow Lab Review.mp4

10:33

31 - Estimator API.mp4

08:30

32 - Machine Learning with tf.estimator.mp4

00:15

33 - Serverless Machine Learning - Lab 3 - Machine Learning using tf.estimator v1.3.mp4

00:10

34 - Estimator Lab Review.mp4

07:05

35 - Building Effective ML.mp4

06:33

36 - Lab Intro -Refactoring to add batching and feature creation.mp4

00:38

37 - Serverless Machine Learning - Lab 4 - Refactoring to add batching and feature-creation v1.3.mp4

00:10

38 - Refactoring Lab Review.mp4

04:42

39 - Train and Evaluate.mp4

04:09

40 - Monitoring.mp4

01:01

41 - Lab Intro -Distributed Training and Monitoring.mp4

02:22

42 - Serverless Machine Learning - Lab 5 - Distributed training and monitoring v1.3.mp4

00:10

43 - Lab Review -Distributed Training and Monitoring.mp4

07:01

4 - Scaling ML models with Cloud ML Engine

44 - Overview.mp4

01:59

45 - Why Cloud ML Engine.mp4

06:45

46 - Development Workflow.mp4

01:23

47 - Packaging trainer.mp4

03:02

48 - TensorFlow Serving.mp4

03:41

49 - Lab -Scaling up ML.mp4

00:39

50 - Serverless Machine Learning - Lab 6 - Scaling up ML using Cloud ML Engine v1.3.mp4

00:10

51 - Lab Review -Scaling up ML.mp4

10:22

5 - Feature Engineering

52 - Overview.mp4

01:53

53 - Good Features.mp4

06:40

54 - Casuality.mp4

08:49

55 - Numeric.mp4

05:31

56 - Enough Examples.mp4

16:23

57 - Raw data to features.mp4

01:35

58 - Categorical features.mp4

08:03

59 - Feature crosses.mp4

03:39

60 - Bucketizing.mp4

03:26

61 - Wide and Deep.mp4

05:54

62 - Where to do feature engineering.mp4

03:17

63 - Feature Engineering Lab Overview.mp4

03:21

64 - Serverless Machine Learning - Lab 7 - Feature Engineering v1.3.mp4

00:10

65 - Feature Engineering Lab Review.mp4

10:10

66 - Hyperparameter Tuning + Demo.mp4

15:39

67 - ML Abstraction Levels.mp4

04:28

68 - Summary.mp4

01:58

Description

This course covers how to build, scale and operationalize machine learning models on Google Cloud Platform. You build ML models with TensorFlow, an open-source ML package and you can train and deploy them in a serverless way using Cloud ML Engine.

What You'll Learn?

This course covers how to build, scale and operationalize machine learning models on Google Cloud Platform. On GCP, you build ML models with TensorFlow, an open-source ML package and you can train and deploy them in a serverless way using Cloud ML Engine.

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Machine Learning

TensorFlow

Google Cloud

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Pluralsight

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Pluralsight, LLC is an American privately held online education company that offers a variety of video training courses for software developers, IT administrators, and creative professionals through its website. Founded in 2004 by Aaron Skonnard, Keith Brown, Fritz Onion, and Bill Williams, the company has its headquarters in Farmington, Utah. As of July 2018, it uses more than 1,400 subject-matter experts as authors, and offers more than 7,000 courses in its catalog. Since first moving its courses online in 2007, the company has expanded, developing a full enterprise platform, and adding skills assessment modules.