Learn Amazon SageMaker 2021 – Best Amazon SageMaker Courses & Best Amazon SageMaker Books

Best Amazon SageMaker Courses 2021


Best Amazon SageMaker Tutorials 2021

AWS SageMaker Practical for Beginners | Build 6 Projects

Machine and deep learning are the hottest topics in tech! Various fields have adopted ML and DL techniques, from banking to healthcare, transportation to technology. AWS is one of the most widely used ML cloud computing platforms in the world – several Fortune 500 companies depend on AWS for their business operations. SageMaker is a fully managed service within AWS that enables data scientists and AI practitioners to train, test, and deploy AI / ML models quickly and efficiently. In this course, students will learn how to create AI / ML models using AWS SageMaker.

The projects will cover various topics in business, health and technology. In this course, students will be able to master many subjects in a practical manner such as: (1) data engineering and functionality engineering, (2) selection of AI / ML models, (3) appropriate selection of AWS SageMaker algorithm to solve business problems (4) Creation, training and deployment of AI / ML models, (5) Model optimization and hyper-parameter tuning.

The course covers many topics such as data engineering, AWS services and algorithms, and the basics of machine / deep learning in a hands-on way:

Data engineering: data types, key python libraries (pandas, Numpy, scikit Learn, MatplotLib and Seaborn), data distributions and functionality engineering (imputation, grouping, encoding and normalization).

AWS Services and Algorithms: Amazon SageMaker, Linear Learner (Regression / Classification), Amazon S3 Storage Services, Boosted Gradient Trees (XGBoost), Image Classification, Principal Component Analysis (PCA), SageMaker Studio, and AutoML.

Machine and Deep Learning Fundamentals: Types of Artificial Neural Networks (ANN) such as feedforward ANNs, convolutional neural networks (CNNs), activation functions (sigmoid, RELU and hyperbolic tangent) , machine learning strategies (supervised / unsupervised), gradient descent algorithm, learning rate, backpropagation, bias, variance, bias-variance tradeoff, regularization (L1 and L2), overfitting, abandonment, detectors of features, grouping, batch normalization, disappearance gradient problem, confusion matrix, precision, recall, F1 score, mean squared error (RMSE), ensemble learning, decision trees and random forest.

We teach SageMaker’s wide range of ML and DL tools with hands-on projects. Immerse yourself in:

Project # 1: Train, Test, and Deploy a Simple Regression Model to Predict Employee Salary Using AWS SageMaker Linear Learner

Project # 2: Train, test and deploy a multiple linear regression machine learning model to predict medical insurance premium.

Project # 3: Train, test and deploy a model to predict retail store sales using XGboost regression and optimize model hyperparameters using SageMaker’s hyperparameter tuning tool.

Project # 4: Perform dimensionality reduction using SageMaker’s built-in PCA algorithm and create a classification model to predict cardiovascular disease using the XGBoost classification model.

Project n ° 5: Develop a traffic sign classifier model using Sagemaker and Tensorflow.

Project # 6: Deep Dive into AWS SageMaker Studio, AutoML, and Model Debugging.

The course is intended for novice developers and data scientists who want to gain a fundamental understanding of AWS SageMaker and solve difficult real world problems. Basic knowledge of machine learning, python programming, and the AWS cloud is recommended.

You will learn:

Train and Deploy AI / ML Models Using AWS SageMaker
Optimize model parameters using hyperparameter optimization research.
Develop, train, test and deploy a linear regression model to make predictions.
Deploy a production-level multipolynomial regression model to predict store sales based on given functionality.
Develop a deep learning-based model to perform image classification.
Develop time series forecasting models to predict future product prices using DeepAR.
Develop and deploy a sentiment analysis model using SageMaker.
Deploy a trained NLP model and interact / make predictions using a secure API.
Train and evaluate the object detection model using SageMaker’s built-in algorithms.

AWS SageMaker and Certified Machine Learning Specialty Exam

There are several courses on machine learning and AI. What is unique about this course?

Here are the main reasons:

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

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

4. The cloud service is easy to integrate into your application and supports 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 manage it all.

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

Practical laboratories

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

What exactly will you learn in this course?

Here are the things you will learn in this course:

AWS SageMaker

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

* You will get an overview of the algorithms provided by the SageMaker service

* Learn how to train, optimize and deploy your models

AI services

In the AI ​​Services section of this course,

* You will discover a set of pre-trained services that you can directly integrate into your application.

* Within minutes, you can create image and video analysis apps, like facial recognition

* You can develop solutions for natural language processing, such as sentiment search, text translation, and conversational chatbots.

The integration

* Learning algorithms are part of the story – You need to know how to incorporate the trained models into your application.

* You will learn to host your models, to evolve on demand, to manage breakdowns

* Provide a clean interface for 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, and visualizing your data assets.

* We will be creating a data lake solution in this course.

Best Amazon SageMaker Books 2021


Bestseller No. 1
Machine Learning with Amazon SageMaker Cookbook: 80 proven recipes for data scientists and...
  • Joshua Arvin Lat (Author)
  • English (Publication Language)
  • 698 Pages - 10/11/2021 (Publication Date) - Packt Publishing - ebooks Account (Publisher)
SaleBestseller No. 2
Amazon SageMaker Developer Guide
  • Hardcover Book
  • Team, Development (Author)
  • English (Publication Language)
  • 442 Pages - 06/26/2018 (Publication Date) - Samurai Media Limited (Publisher)
Bestseller No. 3
Learn Amazon SageMaker: A guide to building, training, and deploying machine learning models for...
  • Amazon Kindle Edition
  • Simon, Julien (Author)
  • English (Publication Language)
  • 490 Pages - 08/27/2020 (Publication Date) - Packt Publishing (Publisher)
Bestseller No. 4
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Bestseller No. 5
Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines
  • Amazon Kindle Edition
  • Fregly, Chris (Author)
  • English (Publication Language)
  • 829 Pages - 04/07/2021 (Publication Date) - O'Reilly Media (Publisher)
Bestseller No. 6
Amazon Sagemaker: Everything you need to know about Amazon Sagemaker
  • Amazon Kindle Edition
  • Bolgopow Publications (Author)
  • English (Publication Language)
  • 383 Pages - 03/20/2021 (Publication Date)
Bestseller No. 7
Amazon SageMaker: Developer Guide
  • Amazon Kindle Edition
  • Amazon Web Services (Author)
  • English (Publication Language)
  • 1770 Pages - Amazon Web Services (Publisher)
SaleBestseller No. 8
Machine Learning in the AWS Cloud: Add Intelligence to Applications with Amazon SageMaker and Amazon...
  • Mishra, Abhishek (Author)
  • English (Publication Language)
  • 528 Pages - 09/11/2019 (Publication Date) - Sybex (Publisher)
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SaleBestseller No. 10
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