Machine Learning Study Guide

Intro: What is Machine Learning? What is AI?

ML theory

1. Preprocessing
2. Regression
3. Classification
4. Clustering
5. Association Rule Learning
6. Reinforcement Rule Learning
7. Natural Language Processing
8. Deep Learning
9. Dimensionality Reduction
10. Model Selection and Boosting

ML on AWS

1. Data Engineering on AWS
   • S3
   • Kinesis
   • Glue Data Catalod
   • Glue ETL
   • Athena
   • Data Pipelines
2. Modeling - Sagemaker
3. High Level ML Services:
   • Amazon Comprehend
   • Amazon Translate
   • Amazon Transcribe
   • Amazon Polly
   • Amazon Rekognition
   • Amazon Forecast
   • Amazon Forecast Algorithms
   • Amazon Lex
   • Amazon Personalize

ML on GCP

Sources:

• https://www.udemy.com/course/machinelearning/
• https://www.udemy.com/course/aws-machine-learning/
• https://cloud.google.com/learn/artificial-intelligence-vs-machine-learning/

Next steps:

• https://www.coursera.org/specializations/machine-learning-introduction
• https://www.coursera.org/specializations/deep-learning
• https://www.coursera.org/specializations/mlops-machine-learning-duke
• https://www.coursera.org/specializations/practical-data-science
• https://www.coursera.org/professional-certificates/preparing-for-google-cloud-machine-learning-engineer-professional-certificate
• https://www.coursera.org/learn/ai-for-medical-diagnosis