2020 AWS SageMaker, AI and Machine Learning Specialty Exam
- 14 hours on-demand video
- 52 articles
- 26 downloadable resources
- 1 Practice Test
- Full lifetime access
- Access on mobile and TV
- Certificate of Completion
Get your team access to 4,000+ top Udemy courses anytime, anywhere.
Try Udemy for Business- Learn AWS Machine Learning algorithms, Predictive Quality assessment, Model Optimization
- Integrate predictive models with your application using simple and secure APIs
- Convert your ideas into highly scalable products in days
- Practice test and resources to gain AWS Certified Machine Learning - Specialty Certification (MLS-C01)
The following downloadable resources are available as part of this lecture:
1. AWS SageMaker Course Introduction.pdf
2. AWS Certified Machine Learning Specialty-Preparation.pdf
3. Gap-Analysis.xlsx
4. AWS Housekeeping.pdf
5. 2020 Benefits of Cloud Computing.pdf
Introduction to AWS Machine Learning Course, Topics Covered, Course Structure
Following Downloadable Resources are available in this lecture:
Model Performance Evaluation Presentation
For exercises in this section, get the latest code from GitHub
https://github.com/ChandraLingam/AmazonSageMakerCourse
If you need help, please refer to SageMaker House Keeping section on how to get the latest code
"The XGBoost (eXtreme Gradient Boosting) is a popular and efficient open-source implementation of the gradient boosted trees algorithm".
https://docs.aws.amazon.com/sagemaker/latest/dg/xgboost.html
Introduction to XGBoost and how it compares to the Linear Model, Decision Tree, and Ensemble Methods
In this lab, let's train our model for forecasting hourly bike rental counts. This is a complex non-linear data set that has seasonality, trend and several factors that impact rentals. Evaluate quality of predictions using Plots, Residual Histograms, RMSE and RMSLE metrics. Finally, submit the results at Kaggle for test data.
"PCA is an unsupervised machine learning algorithm that attempts to reduce the dimensionality (number of features) within a dataset while still retaining as much information as possible. This is done by finding a new set of features called components, which are composites of the original features that are uncorrelated with one another. They are also constrained so that the first component accounts for the largest possible variability in the data, the second component the second most variability, and so on."
https://docs.aws.amazon.com/sagemaker/latest/dg/pca.html
"A factorization machine is a general-purpose supervised learning algorithm that you can use for both classification and regression tasks. It is an extension of a linear model that is designed to capture interactions between features within high dimensional sparse datasets economically. For example, in a click prediction system, the factorization machine model can capture click rate patterns observed when ads from a certain ad-category are placed on pages from a certain page-category. Factorization machines are a good choice for tasks dealing with high dimensional sparse datasets, such as click prediction and item recommendation."
https://docs.aws.amazon.com/sagemaker/latest/dg/fact-machines.html
- 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
Learn about cloud based machine learning algorithms, how to integrate with your applications and Certification Prep
*** UPDATE 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 ***
*** UPDATE FEB-2020 Subtitles and Closed Caption Available - I spent several hours cleaning and editing manually for an accurate subtitle ***
*** UPDATE 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
***
*** UPDATE DEC-2019 Third update for this month!!! AWS Certified Machine Learning Specialty Exam Overview and Preparation Strategies lectures added to the course! Timed Practice Exam is coming soon!
Also added, two new lectures that gives an overview of all SageMaker Built-in Algorithms, Frameworks and Bring-Your-Own Algorithm Supports
Look for lectures starting with 2020
***
*** UPDATE DEC-2019. In the Neural Network and Deep Learning section, we will look at the core concepts behind neural networks, why deep learning is popular these days, different network architectures and hands-on labs to build models using Keras, TensorFlow, Apache MxNet: 2020 Deep Learning and Neural Networks
***
*** UPDATE DEC-2019. New reference architecture section with hands-on lab that demonstrates how to build a data lake solution using AWS Services and the best practices: 2020 AWS S3 Data Lake Architecture. This topic covers essential services and how they work together for a cohesive solution. Covers critical topics like S3, Athena, Glue, Kinesis, Security, Optimization, Monitoring and more.
***
*** UPDATE NOV-2019. AWS Artificial Intelligence material is now live!
Within a few minutes, you will learn about algorithms for sophisticated facial recognition systems, sentiment analysis, conversational interfaces with speech and text and much more.
***
*** UPDATE OCT-2019. New XGBoost Lectures, Labs, do-it-yourself exercises, quizzes, Autoscaling, high availability, Monitoring, security, and lots of good stuff
*** UPDATE MAY-2019. 1. Model endpoint integration with hands-on-labs for (Direct Client, Microservice, API Gateway). 2. Hyperparameter Tuning - Learn how to automatically tune hyperparameters ***
*** UPDATE MARCH-12-2019. I came to know that new accounts are not able to use AWSML Service. AWS is asking new users to use SageMaker Service.
I have restructured the course to start with SageMaker Lectures First. Machine Learning Service Lectures are still available in the later parts of the course. Newly updated sections start with 2019 prefix.
All source code for SageMaker Course is now available on Github
The new house keeping lectures cover all the steps for setting up code from GitHub.
***
*** SageMaker Lectures - DeepAR - Time Series Forecasting, XGBoost - Gradient Boosted Tree algorithm in-depth with hands-on. XGBoost has won several competitions and is a very popular Regression and Classification Algorithm, Factorization Machine based Recommender Systems and PCA for dimensionality reduction ***
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!
- This course is designed for anyone who is interested in AWS cloud based machine learning and data science
- AWS Certified Machine Learning - Specialty Preparation
