English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 5h 37m | 1.07 GB
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.
This 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.
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
Who Should Take This Course
- 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
Table of Contents
1 AWS Certified Machine Learning-Specialty (ML-S) – Introduction
2 Learning objectives
3 1.1 Get an overview of the certification
4 1.2 Use exam study resources
5 1.3 Review the exam guide
6 1.4 Learn the exam strategy
7 1.5 Learn the best practices of ML on AWS
8 1.6 Learn the techniques to accelerate hands-on practice
9 1.7 Understand important ML related services
10 Learning objectives
11 2.1 Learn data ingestion concepts
12 2.2 Using data cleaning and preparation
13 2.3 Learn data storage concepts
14 2.4 Learn ETL solutions (Extract-Transform-Load)
15 2.5 Understand data batch vs data streaming
16 2.6 Understand data security
17 2.7 Learn data backup and recovery concepts
18 Learning objectives
19 3.1 Understand data visualization – Overview
20 3.2 Learn Clustering
21 3.3 Use Summary Statistics
22 3.4 Implement Heatmap
23 3.5 Understand Principal Component Analysis (PCA)
24 3.6 Understand data distributions
25 3.7 Use data normalization techniques
26 Learning objectives
27 4.1 Understand AWS ML Systems – Overview (Sagemaker, AWS ML, EMR, MXNet)
28 4.2 Use Feature Engineering
29 4.3 Train a Model
30 4.4 Evaluate a Model
31 4.5 Tune a Model
32 4.6 Understand ML Inference
33 4.7 Understand Deep Learning on AWS
34 Learning objectives
35 5.1 Understand ML operations – Overview
36 5.2 Use Containerization with Machine Learning and Deep Learning
37 5.3 Implement continuous deployment and delivery for Machine Learning
38 5.4 Understand A_B Testing production deployment
39 5.5 Troubleshoot production deployment
40 5.6 Understand production security
41 5.7 Understand cost and efficiency of ML systems
42 Learning objectives
43 6.1 Create Machine Learning Data Pipeline
44 6.2 Perform Exploratory Data Analysis using AWS Sagemaker
45 6.3 Create Machine Learning Model using AWS Sagemaker
46 6.4 Deploy Machine Learning Model using AWS Sagemaker
47 Learning objectives
48 7.1 Sagemaker Features
49 7.2 DeepLense Features
50 7.3 Kinesis Features
51 7.4 AWS Flavored Python
52 7.5 Cloud9
53 AWS Certified Machine Learning-Specialty (ML-S) – Summary
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