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DevelopmentData ScienceMachine Learning

AWS Certified Machine Learning Specialty (MLS-C01)

Hands on AWS ML SageMaker Course with Practice Test. Join Live Study Group Q&A!
Bestseller
Rating: 4.6 out of 54.6 (3,125 ratings)
25,737 students
Created by Chandra Lingam
Last updated 8/2022
English
English, French [Auto]

What you'll learn

  • You will gain first-hand experience on how to train, optimize, deploy, and integrate ML in AWS cloud
  • AWS Built-in algorithms, Bring Your Own, Ready-to-use AI capabilities
  • Complete Guide to AWS Certified Machine Learning – Specialty (MLS-C01)
  • Includes a high-quality Timed practice test (a lot of courses charge a separate fee for practice test)
  • Zero Downtime Model Deployment
  • How to Integrate and Invoke ML from your Application
  • Automated Hyperparameter Tuning

Requirements

  • Familiarity with Python
  • AWS Account - I will walk through steps to setup one
  • Basic knowledge of Pandas, Numpy, Matplotlib
  • Be an active learner and use course discussion forum if you need help - Please don't put help needed items in course review

Description

Learn about cloud based machine learning algorithms, how to integrate with your applications and Certification Prep

*** NEW Labs - A/B Testing, Multi-model endpoints ***

*** NEW section Emerging AI Trends and Social Issues. How to detect a biased solution, ensure model fairness and prove the fairness ***

*** New Endpoint focused section on how to make SageMaker Endpoint Changes with Zero Downtime ***

*** Lab notebook now use spot-training as the default option. Save over 60% in training costs ***

*** NEW: Nuts and Bolts of Optimization, quizzes ***

*** All code examples and Labs were updated to use version 2.x of the SageMaker Python SDK ***

*** Anomaly Detection with Random Cut Forest - Learn the intuition behind anomaly detection using Random Cut Forest.  With labs. ***

*** Bring Your Own Algorithm - We take a behind the scene look at the SageMaker Training and Hosting Infrastructure for your own algorithms. With Labs ***

*** Timed Practice Test and additional lectures for Exam Preparation added

Welcome to AWS Machine Learning Specialty Course!

I am Chandra Lingam, and I am your instructor

In this course, you will gain first-hand SageMaker experience with many hands-on labs that demonstrates specific concepts

We start with how to set up your SageMaker environment

If you are new to ML, you will learn how to handle mixed data types, missing data, and how to verify the quality of the model

These topics are very important for an ML practitioner as well as for the certification exam

SageMaker uses containers to wrap your favorite algorithms and frameworks such as Pytorch, and TensorFlow

The advantage of a container-based approach is it provides a standard interface to build and deploy your models

It is also straightforward to convert your model into a production application

In a series of concise labs, you will in fact train, deploy, and invoke your first SageMaker model

Like any other software project, ML Solution also requires continuous improvement

We look at how to safely incorporate new changes in a production system, perform A/B testing, and even rollback changes when necessary

All with zero downtime to your application

We then look at emerging social trends on the fairness of Machine learning and AI systems.

What will you do if your users accuse your model as racially biased or gender-biased? How will you handle it?

In this section, we look at the concept of fairness, how to explain a decision made by the model, different types of bias, and how to measure them

We then look at Cloud security – how to protect your data and model from unauthorized use

You will also learn about recommender systems to incorporate features such as movie and product recommendation

The algorithms that you learn in the course are state of the art, and tuning them for your dataset is especially challenging

So, we look at how to tune your model with automated tools

You will gain experience in time series forecasting

Anomaly detection and building custom deep learning models

With the knowledge, you gain here and the included high-quality practice exam, you will easily achieve the certification!

And something unique that I offer my students is a weekly study group meeting to discuss and clarify any questions

I am looking forward to seeing you!

Thank you!

Who this course is for:

  • This course is designed for anyone who is interested in AWS cloud based machine learning and data science
  • AWS Certified Machine Learning - Specialty Preparation

Featured review

Amit Bhagat
Amit B.
50 courses
12 reviews
Rating: 5.0 out of 52 years ago
A 5-star course. Unlike others, Chandra keeps updating this course. The notebooks he presents clearly shows that he has a vast experience in this field. He is also very responsive on Q&A forum. If you are preparing for AWS ML Speciality certification, this course will come in very handy.

Instructor

Chandra Lingam
Cloud Wave LLC
Chandra Lingam
  • 4.5 Instructor Rating
  • 11,593 Reviews
  • 99,207 Students
  • 10 Courses

I am truly honored Udemy CEO highlighted my SAA-C03 courses in the recent earnings call for providing up-to-date and relevant content.

Chandra Lingam is an expert on Amazon Web Services, mission-critical systems, and machine learning. He has a rich background in systems development in both traditional IT data center and on the Cloud. He is uniquely positioned to guide you to become an expert in AWS Cloud Platform.

Before becoming a full-time course developer and instructor, he spent 15 years at Intel as a software engineer.

He has a Master's degree in Computer Science from Arizona State University, Tempe, and a Bachelor's degree in Computer Science from Thiagarajar College of Engineering, Madurai

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