Thanks to theidioms.com

Udacity’s AWS Machine Learning Engineer Nanodegree Program – Ratings and Review!

Udacity's AWS Machine Learning Engineer Nanodegree Program - Ratings and Review!
Data Science

Udacity’s AWS Machine Learning Engineer Nanodegree Program – Ratings and Review!

58 Reviews


In Udacity’s AWS Machine Learning Engineer Nanodegree Program, meet the growing demand for machine learning engineers and master the job-ready skills that will take your career to new heights.

Udacity's AWS Machine Learning Engineer Nanodegree Program - Ratings and Review!

Enroll in Udacity’s AWS Machine Learning Engineer Nanodegree Program today!

Overview of the Udacity’s AWS Machine Learning Engineer Nanodegree Program

In Udacity’s AWS Machine Learning Engineer Nanodegree Program, You’ll 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.

Udacity’s AWS Machine Learning Engineer Nanodegree Program Syllabus

1. Introduction to Machine Learning

In this course, you’ll start learning about machine learning through high level concepts through AWS SageMaker. You’ll begin by using SageMaker Studio to perform exploratory data analysis. Know 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. Finally, you’ll build new ML workflows with highly sophisticated models such as XGBoost and AutoGluon.

2. Developing Your First ML Workflow

In this course you will learn how to create general machine learning workflows on AWS. You’ll begin with an introduction to the general principles of machine learning engineering. From there, you’ll learn the fundamentals of SageMaker to train, deploy, and evaluate a model. Following that, you’ll learn how to create a machine learning workflow on AWS utilizing tools like Lambda and Step Functions. Finally, you’ll learn how to monitor machine learning workflows with services like Model Monitor and Feature Store. With all this, you’ll have all the information you need to create an end-to-end machine learning pipeline.

3. Deep Learning Topics within Computer Vision and NLP

In this course you will learn how to train, finetune, and deploy deep learning models using Amazon SageMaker. You’ll begin by learning what deep learning is, where it is used, and which tools are used by deep learning engineers. Next we will learn about artificial neurons and neural networks and how to train them. After that we will learn about advanced neural network architectures like Convolutional Neural Networks and BERT, as well as how to finetune them for specific tasks. Finally, you will learn about Amazon SageMaker and you will take everything you learned and do them in SageMaker Studio.

4. Operationalizing Machine Learning Projects on SageMaker

This course covers advanced topics related to deploying professional machine learning projects on SageMaker. It also covers security applications. You will learn how to maximize output while decreasing costs. You will also learn how to deploy projects that can handle high traffic and how to work with especially large datasets.

5. Capstone Project: Inventory Monitoring at Distribution Centers

Distribution centers often use robots to move objects as a part of their operations. Objects are carried in bins where each bin can contain multiple objects. In this project, students will have to build a model that can count the number of objects in each bin. A system like this can be used to track inventory and make sure that delivery consignments have the correct number of items. To build this project, students will have to use AWS Sagemaker and good machine learning engineering practices to fetch data from a database, preprocess it and then train a machine learning model. This project will serve as a demonstration of end-to-end machine learning engineering skills that will be an important piece of their job-ready portfolio.

Instructors of Udacity’s AWS Machine Learning Engineer Nanodegree Program

Here is a list of instructors associated with the Udacity Nanodegree Program:

1. Matt Maybeno

Matt Maybeno is a Principal Software Engineer at SOCi. With a masters in Bioinformatics from SDSU, he utilizes his cross domain expertise to build solutions in NLP and predictive analytics.

2. Joseph Nicolls

Joseph Nicolls is a senior machine learning scientist at Blue Hexagon. With a major in Biomedical Computation from Stanford University, he currently utilizes machine learning to build malware-detecting solutions at Blue Hexagon.

3. Charles Landau

Charles holds a MPA from George Washington University, where he focused on econometrics and regulatory policy, and holds a BA from Boston University. At Guidehouse, he supports data scientists and developers working on internal and client-facing ML platforms.

4. Soham Chatterjee

Soham is an Intel® Software Innovator and a former Deep Learning Researcher at Saama Technologies. He is currently a Masters by Research student at NTU, Singapore. His research is on Edge Computing, IoT and Neuromorphic Hardware.

5. Bradford Tuckfield

Bradford does independent consulting for machine learning projects related to manufacturing, law, pharmaceutical operations, and other fields. He also writes technical books about programming, algorithms, and data science.

Review: “Definitely worth enrolling in!”

After successfully completing the Nanodegree program, you will find that the program is very comprehensive and teaches you everything promised by the program.

Plus, the certification is also really helpful since this Udacity Nanodegree Program is well-recognized amongst multiple industries.

Enroll in Udacity’s AWS Machine Learning Engineer Nanodegree Program today!

Leave your thought here

Your email address will not be published. Required fields are marked *