English | MP4 | AVC 1280×720 | AAC 48KHz 2ch | 1h 23m | 237 MB
Take a deeper dive into machine learning with Amazon Web Services (AWS). In this practical course, instructor Jonathan Fernandes helps to familiarize you with common machine learning tasks, demonstrating how to approach each one using key techniques: binary classification, multiclass classification, and regression. Throughout the course, he walks through several examples, using Kaggle datasets for hands-on exploration. Plus, he reviews some essential machine learning concepts and helps to familiarize you with other AWS capabilities, including SageMaker and Deep Learning AMIs.
Topics include:
- Learning algorithms and hyperparameters
- Preparing data for AWS
- Using binary, multiclass, and regression techniques
- Creating a datasource
- Generating predictions
- Creating and interpreting batch predictions
- Additional AWS capabilities
Table of Contents
Introduction
1 Welcome
2 What you should know before watching this course
3 Setting up an AWS account
Introduction to Machine Learning
4 Machine learning overview
5 Learning algorithms and hyperparameters
6 Steps in AWS machine learning
Binary Model
7 Exploring our binary model data set
8 Preparing our data for AWS
9 Creating a datasource
10 Confirming AWS machine learning schema
11 Creating a binary classification model
12 Understanding binary model s predictive performance
13 Setting binary model s predictive performance
14 Using the binary classification model to generate predictions
15 Creating batch predictions in AWS machine learning
16 Binary classification model environment cleanup
Multiclass Model
17 Exploring our multiclass model data set
18 Multiclass data preparation
19 AWS multiclass machine learning model
20 Predictions and evaluations of multiclass learning model
21 Generate predictions for AWS multiclass
22 Creating multiclass batch predictions
23 Interpreting batch predictions
24 Clean multiclass model environment
Regression Model
25 Exploring our regression model data set
26 Regression data preparation
27 Creation of an AWS machine learning model
28 Predictions and evaluations of a machine learning model
29 Regression batch predictions
30 Clean regression model environment
Overview of Other AWS Capabilities
31 SageMaker Deep Learning AMI Apache MXNet
Conclusion
32 Next steps
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