Coursera

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

Sequence Models (Coursera) Coursera
DeepLearning.AI

Sequence Models (Coursera)

Data Science

This course will teach you how to build models for natural language, audio, and other sequence data. Thanks to deep learning, sequence algorithms are working far better than just two years ago, and this is enabling numerous exciting applications in speech recognition, music synthesis, chatbots, machine translation, natural language understanding, and many others.

Jun 22nd 2026
3 Weeks
Machine Learning Deep Learning Artificial Neural Networks
Details
Generative AI Essentials: Overview and Impact (Coursera) Coursera
University of Michigan

Generative AI Essentials: Overview and Impact (Coursera)

Robotics & Computer Vision

With the rise of generative artificial intelligence, there has been a growing demand to explore how to use these powerful tools not only in our work but also in our day-to-day lives. Generative AI Essentials: Overview and Impact introduces learners to large language models and generative AI tools, like ChatGPT. In this course, you’ll explore generative AI essentials, how to ethically use artificial intelligence, its implications for authorship, and what regulations for generative AI could look like.

Jun 26th 2026
1 Week
Artificial Intelligence AI Coursera Plus
Details
Getting started with TensorFlow 2 (Coursera) Coursera
Imperial College London

Getting started with TensorFlow 2 (Coursera)

Data Science

Welcome to this course on Getting started with TensorFlow 2! In this course you will learn a complete end-to-end workflow for developing deep learning models with Tensorflow, from building, training, evaluating and predicting with models using the Sequential API, validating your models and including regularisation, implementing callbacks, and saving and loading models.

Jun 22nd 2026
5-12 Weeks
Machine Learning Data Science APIs
Details
Introduction to Recommender Systems: Non-Personalized and Content-Based (Coursera) Coursera
University of Minnesota

Introduction to Recommender Systems: Non-Personalized and Content-Based (Coursera)

Data Science

This course, which is designed to serve as the first course in the Recommender Systems specialization, introduces the concept of recommender systems, reviews several examples in detail, and leads you through non-personalized recommendation using summary statistics and product associations, basic stereotype-based or demographic recommendations, and content-based filtering recommendations.

Jun 22nd 2026
4 Weeks
Statistics Machine Learning Data Mining
Details
Machine Learning: Classification (Coursera) Coursera
University of Washington

Machine Learning: Classification (Coursera)

Statistics & Data Analysis Data Science

Case Studies: Analyzing Sentiment & Loan Default Prediction. In our case study on analyzing sentiment, you will create models that predict a class (positive/negative sentiment) from input features (text of the reviews, user profile information,...). In our second case study for this course, loan default prediction, you will tackle financial data, and predict when a loan is likely to be risky or safe for the bank.

Jun 22nd 2026
5-12 Weeks
Python Machine Learning Classification
Details
Browser-based Models with TensorFlow.js (Coursera) Coursera
DeepLearning.AI

Browser-based Models with TensorFlow.js (Coursera)

CS: Software Engineering Computer Science

Bringing a machine learning model into the real world involves a lot more than just modeling. This Specialization will teach you how to navigate various deployment scenarios and use data more effectively to train your model. In this first course, you’ll train and run machine learning models in any browser using TensorFlow.js. You’ll learn techniques for handling data in the browser, and at the end you’ll build a computer vision project that recognizes and classifies objects from a webcam.

Jun 22nd 2026
4 Weeks
Machine Learning Object Detection TensorFlow
Details
Structuring Machine Learning Projects (Coursera) Coursera
DeepLearning.AI

Structuring Machine Learning Projects (Coursera)

Data Science

You will learn how to build a successful machine learning project. If you aspire to be a technical leader in AI, and know how to set direction for your team's work, this course will show you how. Much of this content has never been taught elsewhere, and is drawn from my experience building and shipping many deep learning products. This course also has two "flight simulators" that let you practice decision-making as a machine learning project leader. This provides "industry experience" that you might otherwise get only after years of ML work experience.

Jun 22nd 2026
2 Weeks
ML Machine Learning Data Analysis
Details
Probabilistic Graphical Models 2: Inference (Coursera) Coursera
Stanford University

Probabilistic Graphical Models 2: Inference (Coursera)

Statistics & Data Analysis Data Science

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.

Jun 22nd 2026
5-12 Weeks
Machine Learning PGM Inference
Details
Practical Predictive Analytics: Models and Methods (Coursera) Coursera
University of Washington

Practical Predictive Analytics: Models and Methods (Coursera)

Statistics & Data Analysis Data Science

Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems.

Jun 22nd 2026
4 Weeks
Machine Learning Models Methods
Details
Learn to code with AI (Coursera) Coursera
Scrimba

Learn to code with AI (Coursera)

CS: Software Engineering

Imagine waking up tomorrow as a web developer. What would you want to build? With AI tools like ChatGPT, you're already a developer, regardless of your experience, if you know how to work with them. So in this course, you'll build functional, interactive front-end projects while learning how to write effective prompts and debug and refine your code with the help of AI.

Jun 24th 2026
2 Weeks
Programming Artificial Intelligence HTML
Details
Introduction to Artificial Intelligence (AI) (Coursera) Coursera
IBM

Introduction to Artificial Intelligence (AI) (Coursera)

Data Science

In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI. You will also demonstrate AI in action with a mini project.

Jun 22nd 2026
4 Weeks
Artificial Intelligence Machine Learning Job
Details