AWS Academy Machine Learning Foundation

AWS Academy Machine Learning Foundations will introduce you to the concepts and terminology of AI and machine learning. By the end of this microcredential, you will be able to evaluate and select machine learning algorithms and AWS services to be appropriately applied to different business problems. You will gain theoretical knowledge and practical skills to build a full machine learning pipeline, from data collection, data cleaning, and feature engineering to model training, and model deployment using industry-grade AWS tools and libraries. The module helps you to develop Digital Abertay Attributes.

By the end of this module the student should be able to:

1. To understand and describe fundamental machine learning methods and their applications.
2. To be able to implement a machine learning pipeline, from data collection and pre-processing to model training and deployment, as well as solving problems in forecasting, computer vision and language processing with AWS services.

1 Introduction to Machine Learning

What is ML? ML process, business problem solved with ML, ML tools, Amazon SageMaker, ML challenges, supervised learning (regression, classification), unsupervised learning (clustering, dimensionality reduction), reinforcement learning, etc

2 Implementing a ML pipeline using Amazon Sage Maker

Formulating ML problems, collecting and securing data, extracting, transferring and loading data, evaluating your data, finding corelation, feature engineering, data cleaning, dealing with outliers, training, deployment, performance evaluation, hyperparameters and model tuning

3 Forecasting

Time series analysis, Amazon Forecast, Implementing a forecast model, Stock Predictions

4 Computer Vision

Facial Recognition, Image and Video Analysis, Dataset Preparation

5 Natural Language Processing

Amazon Comprehend, Polly, Translate, and Lex, Creating a chatbot, Alexa, etc