Modeling with AWS Machine Learning

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Saravanan Dhandapani

2:12:55

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01. Course Overview

01. Course Overview.mp4

01:46

02. ML Foundation and Supervised Learning Algorithms

01. Machine Learning Basics.mp4

05:05

02. Machine Learning Life Cycle.mp4

06:36

03. Terminologies of AWS Built-in Algorithms.mp4

03:03

04. Linear Learner and XGBoost.mp4

07:23

05. KNN and Anomaly Detection.mp4

06:44

03. Deep Learning Foundation and Algorithms

01. Basics of Deep Learning.mp4

06:43

02. Convolution Neural Networks- An Introduction.mp4

05:52

03. Image Classification, Object Detection, and Semantic Segmentation Algorithms.mp4

10:31

04. Recurrent Neural Networks- An Introduction.mp4

02:58

05. BlazingText, Sequence2Sequence, and Object2Vec Algorithms.mp4

12:21

04. Train ML Models

01. Creating a SageMaker Notebook Instance.mp4

08:17

02. Data Ingestion.mp4

04:19

03. Data Splitting Strategies.mp4

05:48

05. Evaluate ML Models

01. Metrics for Classification Models.mp4

05:53

02. Metrics for Regression Models.mp4

05:27

03. Overfitting and Underfitting.mp4

05:23

04. Evaluating a Model- Demo.mp4

08:15

06. Tune ML Models

01. Fundamentals of Hyperparameter.mp4

08:40

02. SageMaker Automated Hyperparameter Tuning. Setup.mp4

05:43

03. SageMaker Automated Hyperparameter Tuning. Monitor.mp4

06:08

Description

Along with good working experience and knowledge of how to train and evaluate models, you need to have a good understanding of all the ML algorithms provided by AWS. This course will teach you the use cases of built-in algorithms provided by AWS.

What You'll Learn?

Being the front runner when it comes to cloud infrastructure, AWS has cutting edge services when it comes to machine learning. In this course, Modeling with AWS Machine Learning, you’ll learn to convert your data to an optimal model leveraging AWS SageMaker. First, you’ll explore supervised and unsupervised learning algorithms that are built-in to your AWS account and learn how to apply them to a specific business problem. Next, you’ll discover deep learning neural networks architecture and the built-in algorithms provided by AWS that cater specifically to computer vision and language processing domain. Finally, you’ll learn how to train a model on a SageMaker notebook, evaluate the model against the objective metric, and fine-tune the hyperparameters and arrive at an optimally performing model. When you’re finished with this course, you’ll have the skills and knowledge of all the AWS built-in algorithms and train, evaluate, and tune your models that are needed to master AWS SageMaker and clear AWS Machine Learning Specialty certification exam.

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Machine Learning

Amazon AWS

Saravanan Dhandapani

Instructor's Courses

I have been working in IT design, development, and architecture for more than a decade for some of the top fortune 100 companies. I have been involved in fine-tuning software delivery process, improving performance and developing software that is scalable and portable. Some of the key areas where I have worked on are architecture and design using Java, ESB, Tomcat, ReactJS, JavaScript, Linux, Oracle, SVN, GIT and so on and cloud technologies including AWS and GCP.

Pluralsight

View courses Pluralsight

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.