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Development Data Science Machine Learning

AWS SageMaker - Certified Machine Learning Specialty Exam

[Updated] Complete Guide to AWS Certified Machine Learning (MLS-C01) - Specialty and Practice Test
Bestseller
Rating: 4.5 out of 54.5 (2,434 ratings)
19,113 students
Created by Chandra Lingam
Last updated 3/2021
English
English, French [Auto]
30-Day Money-Back Guarantee

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)
Curated for the Udemy for Business collection

Course content

24 sections • 209 lectures • 15h 5m total length

  • Preview00:07
  • Preview03:03
  • Increase the speed of learning
    00:37
  • Preview09:05
  • Preview00:24
  • Preview04:21
  • AWS Account Setup, Free Tier Offers, Billing, Support
    07:00
  • Billing Alerts, Delegate Access
    08:10
  • Configure IAM Users, Setup Command Line Interface (CLI)
    11:30
  • Benefits of Cloud Computing
    06:12
  • AWS Global Infrastructure Overview
    05:58
  • Security is Job Zero | AWS Public Sector Summit 2016 by Steve Schmidt
    00:04

  • Downloadable Resources
    00:05
  • Lab - S3 Bucket Setup
    02:52
  • Lab - Setup SageMaker Notebook Instance
    02:49
  • Lab - Source Code Setup
    02:25
  • Kaggle Data Setup
    00:18
  • SageMaker Console looks different from the course videos - Why?
    00:38
  • How to download Kaggle data with code?
    00:13

  • Introduction to Machine Learning, Concepts, Terminologies
    10:23
  • Data Types - How to handle mixed data types
    12:41
  • Introduction to Python Notebook Environment
    10:33
  • Introduction to working with Missing Data
    09:35
  • Data Visualization - Linear, Log, Quadratic and More
    04:38

  • Model Performance
    00:11
  • Downloadable Resources
    00:10
  • Introduction
    03:26
  • Regression Model Performance
    09:58
  • Binary Classifier Performance
    08:00
  • Binary Classifier - Confusion Matrix
    06:55
  • Binary Classifier - SKLearn Confusion Matrix
    03:18
  • Binary Classifier - Metrics Definition
    03:52
  • Binary Classifier - Metrics Calculation
    04:26
  • Question - Why not Model 1?
    00:41
  • Binary Classifier - Area Under Curve Metrics
    09:39
  • Multiclass Classifier
    12:35
  • Model Performance
    00:09
  • Model Performance Evaluation
    8 questions

  • Downloadable Resources
    00:10
  • Introduction to SageMaker
    04:54
  • Instance Type and Pricing
    10:20
  • DataFormat
    11:12
  • SageMaker Built-in Algorithms
    09:35
  • Popular Frameworks and Bring Your Own Algorithm
    05:24

  • Downloadable Resources
    00:02
  • Introduction to XGBoost
    08:52
  • Lab - Data Preparation Simple Regression
    05:06
  • Lab - Training Simple Regression
    12:25
  • Lab - Data Preparation Non-linear Data set
    02:39
  • Lab - Training Non-linear Data set
    04:47
  • Exercise - Improving quality of predictions
    00:12
  • Lab - Data Preparation Bike Rental Regression
    08:24
  • Lab - Train Bike Rental Regression Model
    06:09
  • Lab - Train using Log of Count
    04:14
  • ResourceLimitExceeded Error - How to Increase Resource Limit
    00:38
  • Use SageMaker SDK 2.x
    00:16
  • Lab - How to train using SageMaker's built-in XGBoost Algorithm
    07:36
  • Q&A: How does SageMaker built-in know the target variable?
    00:28
  • Lab - How to run predictions against an existing SageMaker Endpoint
    04:29
  • Additional Integration Scenarios and Examples
    00:05
  • SageMaker Endpoint Features
    05:41
  • SageMaker Spot Instances - Save up to 90% for training jobs
    01:34
  • Lab - Multi-class Classification
    05:41
  • Lab - Binary Classification
    06:21
  • Exercise - Improve Data Quality in Diabetes dataset
    00:19
  • Question on Diabetes Data Quality Improvement
    00:15
  • Question on Diabetes model - is group mean on target the right approach?
    00:04
  • HyperParameter Tuning, Bias-Variance, Regularization (L1, L2)
    11:07
  • Exercise - Mushroom Classification
    00:15
  • Quiz - XGBoost
    8 questions
  • Underfitting, Overfitting
    3 questions

  • Install SageMaker SDK, GIT Client, Source Code, Security Permissions
    00:13
  • IAM users for the lab
    00:09
  • Integration Overview
    02:32
  • Client to Endpoint using SageMaker SDK
    09:26
  • Client to Endpoint using Boto3 SDK
    03:50
  • Microservice - Lambda to Endpoint - Payload
    03:24
  • Lambda UI Changes
    00:14
  • Microservice - Lambda to Endpoint
    09:09
  • Microservice - API Gateway, Lambda to Endpoint
    10:34

  • Normalization and Standardization
    00:23
  • Downloadable Resources
    00:07
  • Introduction to Principal Component Analysis (PCA)
    05:49
  • PCA Demo Overview
    01:16
  • Demo - PCA with Random Dataset
    03:29
  • Demo - PCA with Correlated Dataset
    05:26
  • Cleanup Resources on SageMaker
    00:28
  • Demo - PCA with Kaggle Bike Sharing - Overview and Normalization
    03:51
  • Demo - PCA Local Mode with Kaggle Bike Train
    03:30
  • Use SageMaker SDK 2.x
    00:16
  • Demo - PCA training with SageMaker
    04:22
  • Demo - PCA Projection with SageMaker
    02:42
  • Exercise : Kaggle Bike Train and PCA
    00:23
  • Summary
    01:22

  • Recommender System
    01:10
  • Downloadable Resources
    00:07
  • Introduction to Factorization Machines
    05:59
  • MovieLens Dataset
    00:08
  • Use SageMaker SDK 2.x
    00:16
  • Demo - Movie Recommender Data Preparation
    10:35
  • Demo - Movie Recommender Model Training
    05:34
  • Demo - Movie Predictions By User
    07:10

  • Downloadable Resources
    00:02
  • Introduction to Hyperparameter Tuning
    06:11
  • Use SageMaker SDK 2.x
    00:16
  • Lab: Tuning Movie Rating Factorization Machine Recommender System
    18:05
  • Lab: Step 2 Tuning Movie Rating Recommender System
    05:00
  • HyperParameter, Bias-Variance, Regularization (L1, L2) [Repeat from XGBoost]
    11:07
  • Nuts and Bolts of Optimization
    00:31
  • Model Optimization
    6 questions
  • Model Optimization - related question
    00:02

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

*** NOV-2020 NEW: Nuts and Bolts of Optimization, quizzes ***

*** NOV-2020 All code examples and Labs were updated to use version 2.x of the SageMaker Python SDK ***

*** SEP-2020 Anomaly Detection with Random Cut Forest - Learn the intuition behind anomaly detection using Random Cut Forest.  With labs. ***

*** APR-2020 Bring Your Own Algorithm - We take a behind the scene look at the SageMaker Training and Hosting Infrastructure for your own algorithms. With Labs ***

*** JAN-2020 Timed Practice Test and additional lectures for Exam Preparation added

For  Practice Test, look for the section: 2020 Practice Exam - AWS Certified Machine Learning Specialty

For exam overview, gap analysis and preparation strategy, look for 2020 - Overview - AWS Machine Learning Specialty Exam

***

Benefits

There are several courses on Machine Learning and AI. What is unique about this course?

Here are the top reasons:

1. Cloud-based machine learning keeps you focused on the current best practices.

2. In this course, you will learn the most useful algorithms.  Don’t waste your time sifting through mountains of techniques that are in the wild

4. Cloud-based service is straightforward to integrate with your application and has support for a wide variety of programming languages.

5. Whether you have small data or big data, the elastic nature of the AWS cloud allows you to handle them all.

6. There is also No upfront cost or commitment – Pay only for what you need and use

Hands-on Labs

In this course, you will learn with hands-on labs and work on exciting and challenging problems

What exactly will you learn in this course?

Here are the things that you will learn in this course:

AWS SageMaker

* You will learn how to deploy a Notebook instance on the AWS Cloud.

* You will gain insight into algorithms provided by SageMaker service

* Learn how to train, optimize and deploy your models

AI Services

In the AI Services section of this course,

* You will learn about a set of pre-trained services that you can directly integrate with your application.

* Within a few minutes, you can build image and video analysis applications – like face recognition

* You can develop solutions for natural language processing, like finding sentiment, text translation, and conversational chatbots.

Integration

* Learning algorithms is one part of the story - You need to know how to integrate the trained models in your application.

* You will learn how to host your models, scale on-demand, handle failures

* Provide a clean interface for the applications using Lambda and API Gateway

Data Lake

* Data management is one of the most complex and time-consuming activities when working on machine learning projects.

* With AWS, you have a variety of powerful tools for ingesting, cataloging, transforming, securing, visualization of your data assets.

* We will build a data lake solution in this course.

Machine Learning Certification

* If you are planning to get AWS Machine Learning Specialty Certification, you will find all the resources that you need to pass the exam in this course.

* Timed Practice Exam and Quizzes

Source Code

* The source code for this course available on Git and that ensures you always get the latest code

Ideal Student

* The ideal student for this course is willing to learn, participate in the course Q&A forum when you need help, and you need to be comfortable coding in Python.

Author

My name is Chandra Lingam, and I am the instructor for this course.

I have over 50,000 thousand students

I spend a considerable amount of time keeping myself up-to-date and teach cloud technologies from the basics.

I have the following AWS Certifications: Solutions Architect, Developer, SysOps, Solutions Architect Professional, Machine Learning Specialty.

I am looking forward to meeting 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 Bhagat
37 courses
9 reviews
Rating: 5.0 out of 5a year 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
  • 4.6 Instructor Rating
  • 7,835 Reviews
  • 71,560 Students
  • 9 Courses

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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