Machine Learning Engineering AWS Bootcamp
About This Course
Strengthen your Machine Learning skills and develop practical experience training, validating and evaluating models with Amazon Web Services
Certificate
To share in LinkedIn
You can share your Certificates in the Certifications section of your LinkedIn profile, on your printed resume, or in other documents.
Learning Objectives
Test Python code and build a Python package of their own.
Build predictive models using a variety of unsupervised and supervised machine learning techniques.
Use Amazon SageMaker to deploy machine learning models to production environments, such as a
web application or piece of hardware..
A/B test two different deployed models and evaluate their performance.
Utilize an API to deploy a model to a website such that it responds to user input, dynamically.
Update a deployed model, in response to changes in the underlying data source.
Requirements
- Intermediate Python programming knowledge,
- Intermediate knowledge of machine learning algorithms,
Target Audience
- This program assumes that you are familiar with common supervised and unsupervised machine learning techniques. As such, it is geared towards people who are interested in building and deploying a machine learning product or application. Are you interested in deploying an application that is powered by machine learning? If so, then this program is right for you.
Curriculum
390 Lessons
Welcome to the Nanodegree program
Welcome to the Machine Learning Engineer Program & Projects
Program Structure
Skills that Set You Apart
Access the Career Portal
How Do I Find Time for My Nanodegree?
Introduction to Software Engineering
In this lesson, you’ll write production-level code and practice object-oriented programming, which you can
integrate into machine learning projects.
Software Engineering Practices Pt I
Software Engineering Practices Pt II
OOP
Portfolio Exercise: Upload a Package to PyPi
Cloud Computing
Introduction to Deployment
Learn how to deploy machine learning models to a production environment using Amazon SageMaker.
Building a Model using SageMaker
Implement and use a mode
04. Hyperparameter Tuning
Updating a Model
Project: Deploying a Sentiment Analysis Model
Population Segmentation
Apply machine learning techniques to solve real-world tasks; explore data and deploy both built-in and
custom-made Amazon SageMaker models.
Payment Fraud Detection
Interview Segment: SageMaker
Deploying Custom Models
Time-Series Forecasting
Project
Introduction to NLP
Learn Natural Language Processing one of the fields with the most real applications of Deep Learning
Implementation of RNN _ LSTM
Sentiment Prediction RNN
Convolutional Neural Networks
Transfer Learning
Weight Initialization
Autoencoders
Job Search
Find your dream job with continuous learning and constant effort
Refine Your Entry-Level Resume
Craft Your Cover Letter
Optimize Your GitHub Profile
Develop Your Personal Brand
Material Includes
- Workspaces
- Hands-on Projects
- Quizzes
- Progress Tracker
