Getting Started with AWS Machine Learning (Coursera)

Offered by AWS,
Getting Started with AWS Machine Learning (Coursera)

Machine learning (ML) is one of the fastest growing areas in technology and a highly sought after skillset in today’s job market. The World Economic Forum states the growth of artificial intelligence (AI) could create 58 million net new jobs in the next few years, yet it’s estimated that currently there are 300,000 AI engineers worldwide, but millions are needed.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

This means there is a unique and immediate opportunity for you to get started with learning the essential ML concepts that are used to build AI applications – no matter what your skill levels are. Learning the foundations of ML now, will help you keep pace with this growth, expand your skills and even help advance your career.
This course will teach you how to get started with AWS Machine Learning. Key topics include: Machine Learning on AWS, Computer Vision on AWS, and Natural Language Processing (NLP) on AWS. Each topic consists of several modules deep-diving into variety of ML concepts, AWS services as well as insights from experts to put the concepts into practice.

What You Will Learn

  • Key problems that Machine Learning can address and ultimately help solve.
  • How to build intelligent applications using Amazon AI services like Amazon Comprehend, Amazon Rekognition, Amazon Translate and others.
  • How to build, train and deploy a model using Amazon SageMaker with built-in algorithms and Jupyter Notebook instance.
  • Sneak peek into AWS DeepLens - The world’s first deep learning enabled video camera for developers.

Syllabus

WEEK 1: Introduction to Machine Learning
WEEK 2: Machine Learning Pipeline
WEEK 3: Amazon AI Services: Computer Vision
WEEK 4: Amazon AI Services: NLP
WEEK 5: Introduction to Amazon SageMaker

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

Prompt Engineering for Web Developers (Coursera) Coursera
Scrimba

Prompt Engineering for Web Developers (Coursera)

Not quite getting the results you want from ChatGPT? Wondering how you can use AI language models to your advantage? Then this course is for you! If you’ve spent any amount of time with AI language models like ChatGPT and Google Bard, you may have noticed the results can sometimes be, well, frustrating. When it comes to leveraging AI language models, your output is often only as good as your input. In other words, it’s all about learning how best to communicate your desired results. Effective prompt engineering is the secret sauce for getting the most out of AI.

Oct 12th 2026
3 Weeks
Deep Learning Applications for Computer Vision (Coursera) Coursera
University of Colorado Boulder

Deep Learning Applications for Computer Vision (Coursera)

This course can be taken for academic credit as part of CU Boulder’s Master of Science in Data Science (MS-DS) degree offered on the Coursera platform. The MS-DS is an interdisciplinary degree that brings together faculty from CU Boulder’s departments of Applied Mathematics, Computer Science, Information Science, and others. With performance-based admissions and no application process, the MS-DS is ideal for individuals with a broad range of undergraduate education and/or professional experience in computer science, information science, mathematics, and statistics.

Oct 12th 2026
5-12 Weeks
Leveraging Virtual Assistants for Personal Productivity (Coursera) Coursera
Coursera Instructor Network

Leveraging Virtual Assistants for Personal Productivity (Coursera)

In a fast-paced world, managing personal productivity is crucial. This course introduces beginners to the world of virtual assistants and chatbots and how they can be harnessed to enhance personal productivity. Learn how to use these AI-driven tools effectively to streamline tasks, manage time, and boost overall efficiency.

Oct 12th 2026
1 Week
Internet of Things: Multimedia Technologies (Coursera) Coursera
University of California, San Diego

Internet of Things: Multimedia Technologies (Coursera)

Content is an eminent example of the features that contributed to the success of wireless Internet. Mobile platforms such as the Snapdragon™ processor have special hardware and software capabilities to make acquisition, processing and rendering of multimedia content efficient and cost-effective.

Oct 12th 2026
3 Weeks
Machine Learning Rapid Prototyping with IBM Watson Studio (Coursera) Coursera
IBM

Machine Learning Rapid Prototyping with IBM Watson Studio (Coursera)

An emerging trend in AI is the availability of technologies in which automation is used to select a best-fit model, perform feature engineering and improve model performance via hyperparameter optimization. This automation will provide rapid-prototyping of models and allow the Data Scientist to focus their efforts on applying domain knowledge to fine-tune models. This course will take the learner through the creation of an end-to-end automated pipeline built by Watson Studio’s AutoAI experiment tool, explaining the underlying technology at work as developed by IBM Research.

Oct 12th 2026
4 Weeks
Introduction to Image Generation (Coursera) Coursera
Google Cloud

Introduction to Image Generation (Coursera)

This course introduces diffusion models, a family of machine learning models that recently showed promise in the image generation space. Diffusion models draw inspiration from physics, specifically thermodynamics. Within the last few years, diffusion models became popular in both research and industry. Diffusion models underpin many state-of-the-art image generation models and tools on Google Cloud. This course introduces you to the theory behind diffusion models and how to train and deploy them on Vertex AI.

Oct 12th 2026
3 Weeks
Probabilistic Graphical Models 2: Inference (Coursera) Coursera
Stanford University

Probabilistic Graphical Models 2: Inference (Coursera)

Probabilistic graphical models (PGMs) are a rich framework for encoding probability distributions over complex domains: joint (multivariate) distributions over large numbers of random variables that interact with each other. These representations sit at the intersection of statistics and computer science, relying on concepts from probability theory, graph algorithms, machine learning, and more.

Oct 12th 2026
5-12 Weeks
ML Pipelines on Google Cloud (Coursera) Coursera
Google Cloud

ML Pipelines on Google Cloud (Coursera)

In this course, you will be learning from ML Engineers and Trainers who work with the state-of-the-art development of ML pipelines here at Google Cloud. The first few modules will cover about TensorFlow Extended (or TFX), which is Google’s production machine learning platform based on TensorFlow for management of ML pipelines and metadata. You will learn about pipeline components and pipeline orchestration with TFX. You will also learn how you can automate your pipeline through continuous integration and continuous deployment, and how to manage ML metadata.

Oct 12th 2026
4 Weeks
Technologies and platforms for Artificial Intelligence (Coursera) Coursera
Politecnico di Milano

Technologies and platforms for Artificial Intelligence (Coursera)

This course will address the hardware technologies for machine and deep learning (from the units of an Internet-of-Things system to a large-scale data centers) and will explore the families of machine and deep learning platforms (libraries and frameworks) for the design and development of smart applications and systems.

Oct 12th 2026
4 Weeks