Machine Learning with SageMaker (AWS)
Attain a thorough understanding of popular ML algorithms, their applicability & limitations, and practise applying them in the Amazon SageMaker environment.
Get Course Info
Audience: Data Scientists & Software Engineers
Duration: 3 days
Format: Lectures and hands-on labs (50% lecture, 50% lab)
Overview
Amazon SageMaker is a fully managed Machine-Learning service. This course balances ML theory with hands-on implementation in SageMaker, enabling participants to train, tune, and deploy models at scale using built-in or custom algorithms.
Objective
Attain a thorough understanding of popular ML algorithms, their applicability & limitations, and practise applying them in the Amazon SageMaker environment.
What You Will Learn
- ML concepts: supervised & unsupervised algorithms
- Data ETL on AWS (incl. Redshift)
- SageMaker architecture & components
- Training & deploying models with built-in algorithms
- Bringing your own algorithms (TensorFlow, MXNet, Spark)
- Authentication, access control, monitoring & logging in SageMaker
Course Details
Audience: Data Scientists & Software Engineers
Duration: 3 days
Format: Lectures and hands-on labs (50% lecture, 50% lab)
- Programming in at least one language, Linux command-line, basic AWS familiarity
Setup: Training AWS account provided • SSH client & browser • Zero-install on student machines
Detailed Outline
- Course intro
- ML goals & tasks
- Where SageMaker fits
- ETL example on Redshift
- Pointers for self-study
- Linear & Logistic Regression
- SVM
- Decision Trees
- Random Forests
- Neural Networks
- Labs for each
- K-Means
- Hierarchical clustering
- Mixture models
- DBSCAN
- Model visualisation examples
- Further resources
- Built-in vs. custom algorithms
- Using TensorFlow, MXNet, Spark
- SageMaker libraries
- Auth & access control
- Monitoring & optimisation
Ready to Get Started?
Contact us to learn more about this course and schedule your training.