Coursera

Next-Generation AI Assistant: Claude by Anthropic (Coursera)

Offered by Coursera Instructor Network,
Next-Generation AI Assistant: Claude by Anthropic (Coursera)

In this guided project, you will explore the capabilities of Claude AI, Anthropic's advanced AI assistant, and learn how to effectively apply its features in your work. This journey involves mastering the skills needed to create compelling materials, persuasive messaging, and strategic campaigns that engage your target audience and enhance your projects.

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

Through a blend of hands-on tasks (such as setting up an account, engaging with the AI platform, crafting engaging product descriptions, and designing promotional campaigns and launch plans) and practical exercises, you will develop skills in using Claude AI's sophisticated text generation features. These tools are designed to help you create persuasive and emotionally resonant content that can significantly impact your work. This project is suitable for those with basic experience in AI text-generation tools, like ChatGPT, and aims to build upon that foundation to expand your expertise.

What you'll learn

  • Create targeted marketing materials and promotional content by utilizing Claude AI.
  • Leverage Claude AI to effectively devise and execute successful product launch strategies.
  • Craft a compelling promo campaign with Claude AI that highlights specific products and services.

Syllabus

Next-Generation AI Assistant: Claude by Anthropic
In this task, learners will sign up for an Anthropic.AI account and gain access to the platform. They will discover the power of Claude, the ultimate AI assistant, and distinguish it from a simple chatbot. Through practical exercises, learners will craft effective prompts to harness Claude's capabilities and explore the nuances of prompt design. All the prompt's will be created around the idea of Launching a Digital Product.

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

Related Courses

Guided Tour of Machine Learning in Finance (Coursera) Coursera
New York University

Guided Tour of Machine Learning in Finance (Coursera)

Data Science

This course aims at providing an introductory and broad overview of the field of ML with the focus on applications on Finance. Supervised Machine Learning methods are used in the capstone project to predict bank closures. Simultaneously, while this course can be taken as a separate course, it serves as a preview of topics that are covered in more details in subsequent modules of the specialization Machine Learning and Reinforcement Learning in Finance.

Jun 8th 2026
4 Weeks
ML Artificial Intelligence Machine Learning
Details
Fundamentals of Reinforcement Learning (Coursera) Coursera
University of Alberta,Alberta Machine Intelligence Institute

Fundamentals of Reinforcement Learning (Coursera)

Data Science

Reinforcement Learning is a subfield of Machine Learning, but is also a general purpose formalism for automated decision-making and AI. This course introduces you to statistical learning techniques where an agent explicitly takes actions and interacts with the world. Understanding the importance and challenges of learning agents that make decisions is of vital importance today, with more and more companies interested in interactive agents and intelligent decision-making.

Jun 8th 2026
4 Weeks
ML Artificial Intelligence Machine Learning
Details
Comportamiento adaptativo (Coursera) Coursera
Universidad Nacional Autónoma de México

Comportamiento adaptativo (Coursera)

Computer Science

Los seres vivos han evolucionado en entornos cambiantes, por lo que han desarrollado mecanismos que les permiten exhibir comportamiento adaptativo. Usando el método sintético, podemos construir sistemas artificiales adaptativos que implementen dichos mecanismos, con lo cual también podemos incrementar nuestra comprensión de los sistemas naturales.

Jun 8th 2026
4 Weeks
Artificial Intelligence Cybernetics Adaptation
Details
Python for Data Science, AI & Development (Coursera) Coursera
IBM

Python for Data Science, AI & Development (Coursera)

CS: Programming Data Science

Kickstart your learning of Python for data science, as well as programming in general, with this beginner-friendly introduction to Python. Python is one of the world’s most popular programming languages, and there has never been greater demand for professionals with the ability to apply Python fundamentals to drive business solutions across industries.

Jun 9th 2026
5-12 Weeks
Programming Python Artificial Intelligence
Details
Fundamentals of Machine Learning for Healthcare (Coursera) Coursera
Stanford University

Fundamentals of Machine Learning for Healthcare (Coursera)

Data Science

Machine learning and artificial intelligence hold the potential to transform healthcare and open up a world of incredible promise. But we will never realize the potential of these technologies unless all stakeholders have basic competencies in both healthcare and machine learning concepts and principles. This course will introduce the fundamental concepts and principles of machine learning as it applies to medicine and healthcare.

Jun 8th 2026
5-12 Weeks
Healthcare Artificial Intelligence Machine Learning
Details
How Entrepreneurs in Emerging Markets can master the Blockchain Technology (Coursera) Coursera
University of Cape Town

How Entrepreneurs in Emerging Markets can master the Blockchain Technology (Coursera)

Economics & Finance Business

In this course, you will gain a thorough understanding of the blockchain and distributed ledger technologies, including an introduction to the necessary foundations in cryptography. The course will discuss blockchain as a distributed ledger and introduce distributed consensus as a mechanism to maintain the integrity of the blockchain. The other revolutionary technologies that are changing the world as we speak are artificial intelligence and machine learning. You will learn about the three major types of AI algorithms: supervised and unsupervised machine learning, as well as reinforcement learning.

Jun 8th 2026
4 Weeks
Cryptography Artificial Intelligence Fintech
Details
Big Data Analytical Platform on Alibaba Cloud (Coursera) Coursera
Alibaba Cloud Academy

Big Data Analytical Platform on Alibaba Cloud (Coursera)

CS: Information & Technology

Building an Analytical Platform on Alibaba Cloud can empower how you take in, analyze, and demonstrate clear metrics from a set of Big Data. This course is designed to teach engineers how to use Alibaba Cloud Big Data products. It covers basic distributed system theory and Alibaba Cloud's core products like MaxCompute, DataWorks, E-MapReduce as well as a bundle of ecosystem tools.

Jun 8th 2026
5-12 Weeks
Artificial Intelligence SQL Big Data
Details
Deep learning in Electronic Health Records - CDSS 2 (Coursera) Coursera
University of Glasgow

Deep learning in Electronic Health Records - CDSS 2 (Coursera)

Data Science

Overview of the main principles of Deep Learning along with common architectures. Formulate the problem for time-series classification and apply it to vital signals such as ECG. Applying this methods in Electronic Health Records is challenging due to the missing values and the heterogeneity in EHR, which include both continuous, ordinal and categorical variables. Subsequently, explore imputation techniques and different encoding strategies to address these issues. Apply these approaches to formulate clinical prediction benchmarks derived from information available in MIMIC-III database.

Jun 8th 2026
4 Weeks
Artificial Intelligence Deep Learning Recurrent Neural Networks
Details
A Complete Reinforcement Learning System (Capstone) (Coursera) Coursera
University of Alberta,Alberta Machine Intelligence Institute

A Complete Reinforcement Learning System (Capstone) (Coursera)

Data Science

In this final course, you will put together your knowledge from Courses 1, 2 and 3 to implement a complete RL solution to a problem. This capstone will let you see how each component---problem formulation, algorithm selection, parameter selection and representation design---fits together into a complete solution, and how to make appropriate choices when deploying RL in the real world. This project will require you to implement both the environment to stimulate your problem, and a control agent with Neural Network function approximation. In addition, you will conduct a scientific study of your learning system to develop your ability to assess the robustness of RL agents. To use RL in the real world, it is critical to (a) appropriately formalize the problem as an MDP, (b) select appropriate algorithms, (c ) identify what choices in your implementation will have large impacts on performance and (d) validate the expected behaviour of your algorithms.

Jun 8th 2026
5-12 Weeks
Artificial Intelligence Algorithms Machine Learning
Details