Video DescriptionMore Than 7 Hours of Video Instruction
This course covers the essentials of Machine Learning on AWS and prepares a candidate to sit for the AWS Machine Learning-Specialty (ML-S) Certification exam. Four main categories are covered: Data Engineering, EDA (Exploratory Data Analysis), Modeling, and Operations. Description This 7+ hour Complete Video Course is fully geared toward the AWS Machine Learning-Specialty (ML-S) Certification exam. The course offers a modular lesson and sublesson approach, with a mix of screencasting and headhsot treatment.
- Data Engineering instruction covers the ingestion, cleaning, and maintenance of data on AWS.
- Exploratory Data Analysis covers topics including data visualization, descriptive statistics, and dimension reduction and includes information on relevant AWS services.
- Machine Learning Modeling covers topics including feature engineering, performance metrics, overfitting, and algorithm selection.
- Operations covers deploying models, A/B testing, using AI services versus training your own model, and proper cost utilization.
The supporting code for this LiveLesson is located at http://www.informit.com/store/aws-certified-machine-learning-specialty-ml-s-complete-9780135556511.About the Instructor
Noah Gift is a lecturer and consultant at both the UC Davis Graduate School of Management MSBA program and the Graduate Data Science program, MSDS, at Northwestern. He teaches and designs graduate machine learning, AI, data science courses, and consulting on machine learning and cloud architecture for students and faculty. These responsibilities include leading a multi-cloud certification initiative for students. Noah is a Python Software Foundation Fellow, AWS Subject Matter Expert (SME) on Machine Learning, AWS Certified Solutions Architect, AWS Academy accredited instructor, Google Certified Professional Cloud Architect, and Microsoft MTA on Python. Noah has published close to 100 technical publications including two books on subjects ranging from cloud machine learning to DevOps.
Noah received an MBA from UC Davis, a M.S. in Computer Information Systems from Cal State Los Angeles, and a B.S. in Nutritional Science from Cal Poly San Luis Obispo. Currently he consults for startups and other companies on machine learning, cloud architecture, and CTO-level consulting as the founder of Pragmatic AI Labs. His most recent publications are Pragmatic AI: An introduction to Cloud-Based Machine Learning(Pearson, 2018) and Essential Machine Learning and AI with Python and Jupyter Notebook LiveLessons (Video Training).
What You Will Learn
- How to perform data engineering tasks on AWS
- How to use Exploratory Data Analysis (EDA) to solve machine learning problems on AWS
- How to perform machine learning modeling tasks on the AWS platform
- How to operationalize machine learning models and deploy them to production on the AWS platform
- How to think about the AWS Machine Learning-Specialty (ML-S) Certification exam to optimize for the best outcome
- DevOps engineers who want to understand how to operationalize ML workloads
- Software engineers who want to ensure they have a mastery of machine learning terminology and practice on AWS
- Machine learning engineers who want to solidify their knowledge about AWS machine learning practices
- Product managers who need to understand the AWS machine learning lifecycle
- Data scientists who run machine learning workloads on AWS
One to two years of experience with AWS and six months using ML tools. Ideally, candidates will have already passed the AWS Cloud Practitioner certification.
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Table of Contents
Lesson 1: AWS Machine Learning-Specialty (ML-S) Certification
- Learning objectives 00:01:13
- 1.1 Get an overview of the certification 00:05:56
- 1.2 Use exam study resources 00:02:56
- 1.3 Review the exam guide 00:10:45
- 1.4 Learn the exam strategy 00:02:47
- 1.5 Learn the best practices of ML on AWS 00:03:33
- 1.6 Learn the techniques to accelerate hands-on practice 00:02:55
- 1.7 Understand important ML related services 00:22:44
Lesson 2: Data Engineering for ML on AWS
- Learning objectives 00:01:10
- 2.1 Learn data ingestion concepts 00:23:15
- 2.2 Using data cleaning and preparation 00:05:37
- 2.3 Learn data storage concepts 00:07:21
- 2.4 Learn ETL solutions (Extract-Transform-Load) 00:12:55
- 2.5 Understand data batch vs data streaming 00:03:36
- 2.6 Understand data security 00:05:12
- 2.7 Learn data backup and recovery concepts 00:06:45
Lesson 3: Exploratory Data Analysis on AWS
- Learning objectives 00:00:57
- 3.1 Understand data visualization: Overview 00:07:20
- 3.2 Learn Clustering 00:05:11
- 3.3 Use Summary Statistics 00:03:36
- 3.4 Implement Heatmap 00:01:44
- 3.5 Understand Principal Component Analysis (PCA) 00:02:57
- 3.6 Understand data distributions 00:03:19
- 3.7 Use data normalization techniques 00:03:37
Lesson 4: Machine Learning Modeling on AWS
- Learning objectives 00:00:49
- 4.1 Understand AWS ML Systems: Overview (Sagemaker, AWS ML, EMR, MXNet) 00:16:26
- 4.2 Use Feature Engineering 00:10:43
- 4.3 Train a Model 00:03:45
- 4.4 Evaluate a Model 00:05:04
- 4.5 Tune a Model 00:02:45
- 4.6 Understand ML Inference 00:05:02
- 4.7 Understand Deep Learning on AWS 00:09:36
Lesson 5: Operationalize Machine Learning on AWS
- Learning objectives 00:01:06
- 5.1 Understand ML operations: Overview 00:06:07
- 5.2 Use Containerization with Machine Learning and Deep Learning 00:07:32
- 5.3 Implement continuous deployment and delivery for Machine Learning 00:05:45
- 5.4 Understand A/B Testing production deployment 00:02:38
- 5.5 Troubleshoot production deployment 00:05:21
- 5.6 Understand production security 00:06:43
- 5.7 Understand cost and efficiency of ML systems 00:07:24
- Lesson 6: Create a Production Machine Learning Application
- Lesson 7: Case Studies
- Title: AWS Certified Machine Learning-Specialty (ML-S)
- Release date: February 2019
- Publisher(s): Pearson IT Certification
- ISBN: 9780135556597