Course Content
Introduction
In this section you will learn about the course introduction and agenda
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Course Introduction and Structure
03:02
AWS Certified Data Analytics – Specialty New Exam Vs Old Exam
07:52
Sample Exam Question and Solution
08:34
Understanding Jupyter Notebook for Machine Learning Analysis
In this section you will learn and understand Jupyter Notebook for Machine Learning Analysis
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What is Jupyter Notebook and Jupyter Installation
04:19
Configuring Jupyter and Virtual Environment With Anaconda
07:36
Jupyter Kernels
05:20
Data Analysis With Jupyter
06:44
Plotting Matplotlib: Working with Jupyter
03:44
Machine Learning In Action: Hands-On
In this section you will learn machine learning in action and hands-on data analysis
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Machine Learning On AWS
07:26
First steps in building a Machine Learning model
10:46
Understanding AWS Datasources
09:55
Machine Learning Training Models in AWS
07:24
Importance of Feature Transformation
06:26
Evaluating Models
13:40
Creating a Data Source and ML Model
07:02
Serverless machine learning inference with AWS Lambda
08:32
Advanced Machine Learning
07:52
Supervised Learning in Machine Learning
08:46
Unsupervised Learning in Machine Learning
09:55
Machine Learning: Preparing Data
03:34
Deep Neural Networks in Machine Learning
14:00
Neural Network Techniques in Machine Learning
20:51
Reinforcement Machine Learning
In this section you will reinforcement machine learning concepts and hands-on labs
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Understanding Reinforcement Machine Learning
06:49
Reinforcement Learning In Action
06:49
Dynamic Programming
10:28
Dynamic Programming In Action
13:11
How to model an environment
10:58
What Is Q Learning?
06:38
Q Learning Shortest Path
04:44
Q Learning In Action
04:49
AWS SageMaker
In this section you will learn how to work with machine learning models with AWS Sagemaker
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What is AWS SageMaker?
05:00
Machine Learning in AWS SageMaker
05:30
Introduction to Linear Learning
05:27
Linear Learning Data Preparation
06:44
Linear Learning Training
07:18
Creating a Hyper-parameter Training Job
09:55
Working With AWS QuickSight – AWS Athena and ETL Techniques
In this section you will how to perform Machine Learning Data Analysis and Management with AWS QuickSight, AWS Athena, AWS Glue and work with ETL operations
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AWS QuickSight: Creating Your First Visual
15:40
AWS QuickSight UI
06:10
Uploading DataSet to S3 Bucket
04:32
Creating Database and Table in AWS
13:26
Making Data Connection Using AWS Athena
11:10
AWS UI Sample DataSet
11:30
AWS DataLake with AWS Athena, AWS Glue and AWS S3
12:38
Upgrading QuickSight To Enterprise and Create VPC
06:09
Importing CSV File
06:41
Advanced Data Analysis
In this section you will learn advanced data analysis within ETL operations
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Creating a Basic Filter
06:43
Creating a Calculated Field
06:34
Function Reference
05:06
Creating Your First Story
06:46
Publishing Dashboard
04:02
Exporting Visuals
03:13
Connecting To Twitter
03:43
Using Autograph
06:27
AWS Athena – Glue Concepts
12:21
Machine Learning Essential Steps and Process
In this section you will in depth AWS machine Learning processes and 5 essential steps
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What You Will Learn
04:50
When To Use Machine Learning
04:34
Machine Learning Business Problem Framing
09:44
Types Of Machine Learning
09:59
Machine Learning Algorithms
15:36
Different Types Of Data
04:45
Data Collection and Integration
06:55
Data Cleaning in ML
05:50
Data Shuffling and Splitting
07:35
Features and Labels in ML
04:03
Types of Data Visualizations
05:19
Feature Engineering Revisited
06:13
Numeric Value Binning
07:01
Parameter Tuning to Optimize ML
05:19
Overfitting and Underfitting Machine Learning Models
07:26
Machine Learning Goal Evaluation
04:22
Data Augmentation in ML
05:38
Machine Learning Data Prediction
05:05
Creating a Data Source and Machine Learning Model
07:02
Course Conclusion and Resources
In this section you will learn about FREE ClayDesk Web Hosting, course recap and download all resources for this course
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FREE ClayDesk WebHosting
03:34
AWS Certified Data Analytics – Specialty (DAS-C01) Course
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