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ANA 680 Machine Learning

Lesson 1 Required Learning Resources

Gift. N and Deza. A. (2021). Practical MLOps: Operationalizing Machine Learning Models. 1st edition. O'Reilly Media.

  1. Chapter 1: Introduction to MLOps
  2. Chapter 2: MLOps Foundations

Mark Treveil, et al. (2021). Introducing MLOps: How to Scale Machine Learning in the Enterprise. 1st edition, O'Reilly Media.

Chapter 4: Developing Models

    1.  

Emmanuel Raj. (2021). Engineering MLOps: Rapidly build, test, and manage production-ready machine learning life cycles at scale. 1st edition. ‎ Packt Publishing.

  1. Chapter 1: Fundamentals of MLOps workflow
  2. Chapter 2: Characterizing Your Machine Learning Problem

​​​​​​​(Article) Commonly used Machine Learning Algorithms (with Python and R Codes): https://www.analyticsvidhya.com/blog/2017/09/common-machine-learning-algorithms/​​​​​​​

Lesson 1 Optional Resources

(Article) A comprehensive Guide to Data Exploration: https://www.analyticsvidhya.com/blog/2016/01/guide-data-exploration/.

A brief but comprehensive walkthrough data exploration, cleaning and engineering in Machine Learning

​​​​​​(Article) Regression– Classification: https://www.saedsayad.com/regression.htm

​​​​​Introduction to machine learning with Jupyter notebooks: An example of Machine Learning project with time series data. https://developers.redhat.com/articles/2021/05/21/introduction-machine-learning-jupyter-notebooks#

​​​​​A Step by Step CART Decision Tree Example: https://sefiks.com/2018/08/27/a-step-by-step-cart-decision-tree-example/. Detailed, hand calculated demonstration of decision tree method in model training 

​​​​​Precision & Recall: https://mlu-explain.github.io/precisin-recall/. A review of the confusion matrix, a simple technique for visualizing the performance of a classification model.

​​​​The Random Forest Algorithm: https://mlu-explain.github.io/random-forest/: An example of ensemble learning where each model is a decision tree. Covers the Bagging method

​​​​​AWS Machine Learning University: https://aws.amazon.com/machine-learning/mlu/ : A great set of lessons and videos on natural language processing, tabular data, computer vision and decision trees and ensemble methods from AWS.

​​​​​Machine Learning Operations: https://ml-ops.org/ : A site discussing all aspects of MLOps in design, development, and operations.