Meet the growing demand for machine learning engineers and master the job-ready skills that will take your career to new heights.

Necessary preparation

• At least 40 hours of programming experience.
• Familiarity with data structures like dictionaries and lists.
• Experience with libraries like NumPy and pandas.
• Knowledge of functions, variables, loops, and classes.
• Exposure to Python through Jupyter Notebooks is recommended.
• Experience with constructing and calling HTTP API endpoints is recommended.
• Basic understanding of the machine learning workflow.
• Basic theoretical understanding of ML algorithms such as linear regression, logistic regression, neural network.
• Basic understanding of model training and testing processes.
• Basic knowledge of commonly used metrics for ML models evaluation such as accuracy, precision, recall, and mean square error (MSE).

For those who want to

• Master the skills necessary to become a successful ML engineer.
• Learn the data science and machine learning skills required to build and deploy machine learning models in production using Amazon SageMaker.

The Program 

1. Introduction to Machine Learning.
2. Developing Your First ML Workflow.
3. Deep Learning Topics within Computer Vision and NLP.
4. Operationalizing Machine Learning Projects on SageMaker.
5. CAPSTONE PROJECT: Inventory Monitoring at Distribution Centers.

What will you learn

• About machine learning through high level concepts through AWS SageMaker. 
• How and when to apply the basic concepts of machine learning to real world scenarios.
• Create machine learning workflows, starting with data cleaning and feature engineering, to evaluation and hyperparameter tuning.
• How to create general machine learning workflows on AWS.
• The fundamentals of SageMaker to train, deploy, and evaluate a model.
• How to create a machine learning workflow on AWS utilizing tools like Lambda and Step Functions.
• How to monitor machine learning workflows with services like Model Monitor and Feature Store. 
• How to train, finetune, and deploy deep learning models using Amazon SageMaker. 
• About artificial neurons and neural networks and how to train them.
• About advanced neural network architectures like Convolutional Neural Networks and BERT, as well as how to finetune them for specific tasks.
• About Amazon SageMaker and you will take everything you learned and do them in SageMaker Studio.
• How to maximize output while decreasing costs. 
• How to deploy projects that can handle high traffic and how to work with especially large datasets.