The Art and Science of ML

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Google Cloud

3:25:43

29 View

1 - Introduction

01 - Introduction.mp4

03:16

2 - The Art of ML

02 - Introduction.mp4

01:33

03 - Regularization.mp4

04:53

04 - L1 & L2 Regularizations.mp4

04:35

05 - Lab Intro - Regularization.mp4

00:12

06 - Lab Solution - Regularization.mp4

02:56

07 - Learning Rate and Batch Size.mp4

05:09

08 - Optimization.mp4

01:18

09 - Practicing with Tensorflow Code.mp4

01:13

10 - Lab Intro - Hand-Tuning ML Models.mp4

00:18

11 - [ML on GCP C5] Improve model accuracy by hand-tuning hyperparameters.mp4

00:10

12 - Lab Solution - Hand-Tuning ML Models.mp4

07:15

3 - Hyperparameter Tuning

13 - Introduction.mp4

01:04

14 - Parameters vs Hyperparameters.mp4

02:25

15 - Think Beyond Grid Search.mp4

03:27

16 - Lab Intro - Improve Model Accuracy by Hyperparameter Tuning with Cloud MLE.mp4

00:23

17 - [ML on GCP C5] Improve model accuracy by hyperparameter tuning with Cloud MLE.mp4

00:10

18 - Lab Solution - Improve Model Accuracy by Hyperparameter Tuning with Cloud MLE.mp4

00:30

4 - A Pinch of Science

19 - Introduction.mp4

01:07

20 - Regularization for Sparsity.mp4

05:26

21 - Lab Intro - L1 Regularization.mp4

03:19

22 - Lab Solution - L1 Regularization.mp4

00:51

23 - Logistic Regression.mp4

17:16

5 - The Science of Neural Networks

24 - Introduction.mp4

01:10

25 - Neural Networks.mp4

18:45

26 - Lab Intro - Neural Networks Playground.mp4

12:38

27 - Training Neural Networks.mp4

14:38

28 - Lab Intro - Using Neural Networks to build a ML Model.mp4

11:54

29 - [ML on GCP C5] Using Neural Networks to build ML model.mp4

00:10

30 - Multi-class Neural Networks.mp4

10:46

6 - Embeddings

31 - Intro to Embeddings.mp4

02:20

32 - Review of Embeddings.mp4

05:00

33 - Recommendations.mp4

04:28

34 - Data-driven Embeddings.mp4

03:09

35 - Sparse Tensors.mp4

04:42

36 - Train an Embedding.mp4

04:21

37 - Similarity Property.mp4

07:26

7 - Custom Estimator

38 - Custom Estimator.mp4

04:52

39 - Model Function.mp4

06:10

40 - Lab Intro- Implementing a Custom Estimator.mp4

11:48

41 - [ML on GCP C5] Using Custom Estimators.mp4

00:10

42 - Keras Models.mp4

04:46

43 - Demo of Keras + Estimator.mp4

02:50

8 - Summary

44 - Summary.mp4

02:07

45 - Specialization Summary.mp4

02:47

Description

In this data science course you will learn the essential skills of ML intuition, good judgment and experimentation to finely tune and optimize your ML models for the best performance.

What You'll Learn?

Welcome to the art and science of machine learning. In this data science course you will learn the essential skills of ML intuition, good judgment and experimentation to finely tune and optimize your ML models for the best performance.

In this course you will learn the many knobs and levers involved in training a model. You will first manually adjust them to see their effects on model performance. Once familiar with the knobs and levers, otherwise known as hyperparameters, you will learn how to tune them in an automatic way using Cloud Machine Learning Engine on Google Cloud Platform.

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Machine Learning

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.