The Global Scholarly Directory.
Discover world-class academic programs curated for the modern intellectual. Search through 19877+ degrees and professional certificates.
Google Cloud
Work with Gemini Models in BigQuery - Português Brasileiro
Este curso demonstra como usar modelos de ML/IA para tarefas generativas no BigQuery. Nele, você vai conhecer o fluxo de trabalho para solucionar um problema comercial com modelos do Gemini utilizando um caso de uso prático que envolve gestão de relacionamento com o cliente. Para facilitar a compreensão, o curso também proporciona instruções detalhadas de soluções de programação que usam consultas SQL e notebooks Python.
Coursera
Secure AI Model Deployments & Lifecycles
If model rollouts feel risky, monitoring is an afterthought, and updates make you nervous, you’re not alone. As AI moves from prototype to production, the stakes rise: model supply chains, promotion workflows, and runtime behavior need guardrails, not just good intentions. This course is your blueprint for shipping with confidence by baking security into every phase of the AI Model lifecycle. You’ll learn to choose the right deployment strategy for your risk profile, enforce provenance and approvals with a model registry, and wire continuous monitoring for data/feature drift, performance, and safety signals. We also cover securing updates with signed artifacts, CI/CD policy gates, and rapid, auditable rollback. ML engineers, MLOps practitioners, and DevOps teams work together to ensure AI models move smoothly from development to production. ML engineers focus on building and training models, MLOps practitioners streamline and automate the model lifecycle, and DevOps teams manage infrastructure and deployment. Together, they create a reliable, scalable, and efficient pipeline for delivering AI solutions that perform consistently in real-world environments. Git & CI/CD basics, Docker or managed ML platform experience, working knowledge of Python ML workflows and environment/package management. By the end, you’ll ship behind structured change control, track lineage from dataset to container, and respond quickly when reality (or your threat model) changes. Whether you run on Kubernetes, serverless, or managed ML platforms, the practical flows, templates, and hands-on exercises in this course help you harden deployments without slowing delivery; turning ad-hoc launches into repeatable, secure lifecycles from commit to canary to continuous oversight.
Coursera
Data Visualization using Bokeh
Welcome to this 1 hour long guided project on data visualization using Bokeh. In this project you will learn the basics of Bokeh and create different plots and impressive data visualizations in detail. You will also learn Glyphs and how to Map Geo data using Bokeh. Please note that you will need prior programming experience ( beginner level) in Python. You will also need familiarity with Pandas. This is a practical, hands on guided project for learners who already have theoretical understanding of Pandas and Python.
Google Cloud
Preparación para el proceso de certificación Associate Cloud Engineer
Este curso te permite estructurar tu preparación para el examen de Associate Cloud Engineer. Aprenderás sobre los dominios de Google Cloud que se incluyen en el examen y la forma de crear un plan de estudio para saber más de ellos.
University of Arizona
Marketing Communications: The Customer's Journey
How do customers move from first hearing about a brand to becoming loyal advocates? This non-credit online course in marketing communications, offered by the University of Arizona Online through Coursera, is designed for working professionals and adult learners who want to up-skill or re-skill in advertising and media strategy while exploring college-level coursework on a flexible basis. You’ll learn to map the customer journey, apply integrated marketing communication principles, and select the right mix of traditional, digital, paid, owned, and earned media channels for awareness, engagement, conversion, and loyalty. Through real-world examples and hands-on exercises, you’ll develop media strategies with SMART objectives and KPIs, and gain practical skills to create campaigns that move customers from awareness to advocacy. This course offers a low-risk way to experience college-aligned learning, making it an excellent starting point for professionals considering continued education or a future pathway into University of Arizona credit-bearing programs. By the end of this course, you will be able to: 1. Explain the stages of the customer journey and how advertising objectives differ at each stage. 2. Evaluate traditional, digital, owned, and earned media channels to select the best options for awareness, engagement, conversion, and loyalty. 3. Develop a media strategy aligned with customer goals, including SMART objectives and key performance indicators (KPIs). 4. Apply integrated marketing communication principles to create campaigns that move customers from awareness to advocacy. This the second course in the Management of Marketing Communications specialization. The other two courses are: - Marketing Communications: Intro to Consumer Behavior - Marketing Communications: Culture and Messaging Learners who complete this course and successfully pass the final exam may be eligible to earn University of Arizona credit through the credit‑by‑exam process. This option provides a flexible pathway for those interested in applying their learning toward future academic goals. For questions about eligibility, requirements, or next steps, please contact CAPE‑Info@email.arizona.edu.
Coursera
Responsive Design in Bootstrap: Create a Landing Page
Want to create landing pages that convert visitors into customers? This two-hour hands-on project is designed for web developers seeking to enhance their skills in building professional and visually captivating online storefronts using Bootstrap, a leading front-end framework offering a streamlined and efficient framework for creating responsive and visually appealing web interfaces. In this project, you'll master essential Bootstrap components such as navbars, cards, carousels, and modals, seamlessly integrating them into a responsive landing page design. Through practical exercises, you'll create essential page sections like headers, footers, menus, product categories, and featured product showcases. You'll also gain proficiency in crafting user-friendly navigation, effectively displaying products, and implementing interactive elements like modals to enhance the user experience. By the end of this project, you'll confidently build attractive, functional, and mobile-optimized landing pages that attract visitors and encourage them to take action, whether it's making a purchase, signing up for a newsletter, or exploring further. This project's unique, hands-on approach ensures you acquire practical skills directly applicable to real-world web development scenarios. To succeed in this project, you need familiarity with core HTML tags and page structure.
Coursera
User Awareness and Education for Generative AI
This course aims to empower general users with a friendly and non-technical understanding of Generative AI. It emphasizes the importance of transparency in AI systems, helping learners to comprehend how AI decisions are made. By highlighting the importance of user awareness, transparency, and informed decision-making, learners will be better equipped to make informed choices and interact with AI responsibly and confidently. The strategies and insights provided will help learners explore the creative potential of Generative AI while ensuring ethical practices and safeguarding against potential risks. The course encourages active participation and emphasizes the collective responsibility of users in shaping the future of AI. This course is designed for any employee or manager of a business that is either using or contemplating using AI and Generative AI, or anyone seeking to enhance their knowledge of the subject. The course is designed to give students a plain-language basic understanding of the topic and some of the nuances of using AI. There are no specific prerequisites for this course. A basic understanding of computers and business will be helpful, but not mandatory. An open mind and curiosity about the broader societal impact of AI will enhance the learning experience.
EDUCBA
Implement NGINX Web Servers and Reverse Proxy Solutions
By the end of this course, learners will be able to deploy websites using NGINX, configure reverse proxy architectures, secure web applications with SSL and access controls, implement load balancing strategies, and optimize performance through monitoring and compression techniques. This hands-on, project-based course guides learners from foundational NGINX concepts to advanced, production-ready configurations. Learners begin by deploying and validating a website using NGINX, then progressively build real-world infrastructure skills by configuring backend services, virtual hosts, and reverse proxy routing. The course emphasizes secure server access, authentication, SSL encryption, and IP-based controls to reflect enterprise-grade requirements. Learners will also implement load balancing methods and performance optimizations commonly used in modern web architectures. What makes this course unique is its end-to-end, practical approach. Rather than isolated tutorials, learners complete a cohesive infrastructure project that mirrors real deployment scenarios. Each module builds logically on the previous one, ensuring strong conceptual grounding and applied mastery. This course is ideal for learners seeking job-ready skills in web server administration, DevOps foundations, or backend infrastructure using NGINX.
Coursera
Introduction to R: Basic R syntax
This guided project is for beginners interested in taking their first steps with coding in the statistical language R. It assumes no previous knowledge of R, introduces the RStudio environment, and covers basic concepts, tools, and general syntax. By the end of the exercise, learners will build familiarity with RStudio and the fundamentals of the statistical coding language R.
Illinois Tech
Health Informatics Capstone & Innovation Project
This course is designed for intermediate to advanced students in health informatics who are eager to apply their theoretical knowledge in practical settings. Participants will learn to design and implement real-world projects in both Clinical Health and Public Health environments. The course covers the selection and execution of projects, focusing on innovations that enhance patient and provider experiences through Electronic Health Record (EHR) systems and FHIR servers. Students will gain hands-on experience in integrating with EHR systems, simulating patient journeys, and analyzing population-level trends using large-scale data frameworks. By the end of the course, participants will be equipped with the skills necessary to tackle complex health informatics challenges and contribute to meaningful improvements in healthcare delivery.
Coursera
Customer Service with Python: Build a Chatbot using ChatGPT
In this guided 2-hour project-based course, you'll learn the intricacies of building and customizing an AI-powered chatbot using Python and the ChatGPT API. You'll start by setting up your coding environment, including importing libraries and configuring the OpenAI API key. Then, you'll engage in direct communication with the ChatGPT model, learning to manage and refine the chatbot's conversation flow for handling FAQs in customer service. The project progresses as you develop skills to initiate and test chatbot conversations, ensuring they are aligned with customer service needs. Finally, you'll apply your newfound knowledge to build and interact with a customized chatbot tailored for specific e-commerce scenarios. This project is designed to guide you through each step, culminating in the creation of a functional customer service chatbot. By the end, you'll possess not only a working chatbot but also the skills to adapt and enhance AI chatbot solutions for various business needs.
DeepLearning.AI
Probability & Statistics for Machine Learning & Data Science
Newly updated for 2024! Mathematics for Machine Learning and Data Science is a foundational online program created by DeepLearning.AI and taught by Luis Serrano. In machine learning, you apply math concepts through programming. And so, in this specialization, you’ll apply the math concepts you learn using Python programming in hands-on lab exercises. As a learner in this program, you'll need basic to intermediate Python programming skills to be successful. After completing this course, you will be able to: • Describe and quantify the uncertainty inherent in predictions made by machine learning models, using the concepts of probability, random variables, and probability distributions. • Visually and intuitively understand the properties of commonly used probability distributions in machine learning and data science like Bernoulli, Binomial, and Gaussian distributions • Apply common statistical methods like maximum likelihood estimation (MLE) and maximum a priori estimation (MAP) to machine learning problems • Assess the performance of machine learning models using interval estimates and margin of errors • Apply concepts of statistical hypothesis testing to commonly used tests in data science like AB testing • Perform Exploratory Data Analysis on a dataset to find, validate, and quantify patterns. Many machine learning engineers and data scientists need help with mathematics, and even experienced practitioners can feel held back by a lack of math skills. This Specialization uses innovative pedagogy in mathematics to help you learn quickly and intuitively, with courses that use easy-to-follow visualizations to help you see how the math behind machine learning actually works. We recommend you have a high school level of mathematics (functions, basic algebra) and familiarity with programming (data structures, loops, functions, conditional statements, debugging). Assignments and labs are written in Python but the course introduces all the machine learning libraries you’ll use.
EDUCBA
Splunk Administration & Performance Tuning
By the end of this course, learners will be able to configure advanced Splunk environments, optimize distributed architectures, and implement high-performance data management strategies. Participants will gain hands-on expertise in HTTP Event Collector setup, data parsing, and timestamp classification to ensure precise data onboarding. They will also design effective regular expressions for event transformation, apply secure lookup integrations, and enforce role-based access control to protect enterprise data integrity. Moving further, learners will explore distributed search, search head clustering, and best practices for Splunk authentication and synchronization across large-scale deployments. The course concludes with advanced performance tuning techniques, real-time search optimization, and diagnostic tools like splunk diag for proactive system health management. This course is uniquely designed for Splunk administrators seeking to elevate their operational and troubleshooting capabilities. Combining theory with applied scenarios, it empowers professionals to analyze, configure, and maintain Splunk deployments at enterprise scale — ensuring security, speed, and scalability across distributed environments.
Logical Operations
PCAP: Python Input, Output, and String Handling
This course will be useful to anyone who has foundational Python experience and wants to expand on their programming skills. You will learn how to perform input/output operations from data files. You'll also work with character encodings and operate on strings. This is the first course in a multi-course Specialization. All of the courses in this Specialization require that you use the provided virtual machine, which includes an installation of Python. The course setup instructions provided in the first module of this course go into more detail about the hardware and software requirements.
Google Cloud
Elastic Cloud Infrastructure: Scaling and Automation em Português Brasileiro
Neste curso intensivo sob demanda, os participantes vão conhecer os serviços abrangentes e flexíveis de infraestrutura e plataforma fornecidos pelo Google Cloud. Com o auxílio de videoaulas, demonstrações e laboratórios práticos, os participantes têm a chance de conhecer e implantar elementos da solução. Isso inclui interconexão segura entre redes, balanceamento de carga, escalonamento automático, automação de infraestrutura e serviços gerenciados.
Packt
Machine Learning for Absolute Beginners - Level 1
This course features Coursera Coach! A smarter way to learn with interactive, real-time conversations that help you test your knowledge, challenge assumptions, and deepen your understanding as you progress through the course. In this beginner-friendly course, you'll embark on a structured journey through the exciting world of Machine Learning (ML). Starting with foundational concepts such as the rise of Artificial Intelligence (AI) and its real-world applications, you will explore essential ML terminology and techniques. The course provides a solid understanding of various machine learning models and their functions, including supervised learning, unsupervised learning, reinforcement learning, and classification. As you move forward, you’ll dive into the magic behind generative AI, examining artificial neural networks, deep learning, and large language models (LLMs). You’ll also get a closer look at how generative AI is transforming industries by creating text, images, and even code. Throughout the course, you will engage with practical examples and use cases, learning how AI can be applied to real-world scenarios such as brainstorming, summarization, and code generation. The course also highlights key challenges and limitations of AI, such as bias, hallucinations, and data privacy concerns, ensuring that you gain a well-rounded understanding of both the potential and the limitations of AI technologies. This course is ideal for those starting their journey in Machine Learning and AI, with no prior experience required. It is especially useful for anyone interested in how generative AI can be applied across various fields. With its hands-on approach and clear explanations, the course gradually introduces you to the exciting and ever-evolving world of AI. By the end of the course, you will be able to understand fundamental machine learning concepts, implement basic ML models, navigate AI challenges, and apply generative AI in creative ways.