Our Courses

MATLAB Programming for Engineers and Scientists

MATLAB Programming for Engineers and Scientists

This Specialization is designed for learners with little to no programming experience and teaches them to create MATLAB programs that solve real-world engineering and scientific problems. While the focus is on general computer programming principles, the courses also provide in-depth coverage of MATLAB's unique features for engineering and scientific computing. The first course covers basic programming concepts. The second course teaches techniques for using ChatGPT to program more productively.

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Data Science: Foundations using R

Data Science: Foundations using R

Ask the right questions, manipulate data sets, and create visualizations to communicate results. This Specialization covers foundational data science tools and techniques, including getting, cleaning, and exploring data, programming in R, and conducting reproducible research. Learners who complete this specialization will be prepared to take the Data Science: Statistics and Machine Learning specialization, in which they build a data product using real-world data. The five courses in this specialization are the very same courses that make up the first half of the Data Science Specialization.

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Building Cloud Computing Solutions at Scale

Building Cloud Computing Solutions at Scale

With more companies leveraging software that runs on the Cloud, there is a growing need to find and hire individuals with the skills needed to build solutions on a variety of Cloud platforms. Employers agree: Cloud talent is hard to find.

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Machine Learning Rock Star – the End-to-End Practice

Machine Learning Rock Star – the End-to-End Practice

Machine learning reinvents industries and runs the world. Harvard Business Review calls it “the most important general-purpose technology of our era.” But while there are so many how-to courses for hands-on techies, there are practically none that also serve the business leadership of machine learning – a striking omission, since success with machine learning relies on a very particular project leadership practice just as much as it relies on adept number crunching. By filling that gap, this course empowers you to generate value with ML.

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AI Foundations for Everyone

AI Foundations for Everyone

Artificial Intelligence (AI) is no longer science fiction. It is rapidly permeating all industries and having a profound impact on virtually every aspect of our existence. Whether you are an executive, a leader, an industry professional, a researcher, or a student - understanding AI, its impact and transformative potential for your organization and our society is of paramount importance. This specialization is designed for those with little or no background in AI, whether you have technology background or not, and does not require any programming skills.

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Hands-on Foundations for Data Science and Machine Learning with Google Cloud Labs

Hands-on Foundations for Data Science and Machine Learning with Google Cloud Labs

In this Google Cloud Labs Specialization, you'll receive hands-on experience building and practicing skills in BigQuery and Cloud Data Fusion.

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Applied Data Science Ethics

Applied Data Science Ethics

AI’s popularity has resulted in numerous well-publicized cases of bias, injustice, and discrimination. Often these harms occur in machine learning projects that have the best of goals, developed by data scientists with good intentions. This course, the second in the data science ethics program for both practitioners and managers, provides guidance and practical tools to build better models and avoid these problems.

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Principles of Data Science Ethics

Principles of Data Science Ethics

Concern about the harmful effects of machine learning algorithms and AI models (bias and more) has resulted in greater attention to the fundamentals of data ethics.

This data science ethics course for both practitioners and managers provides guidance and practical tools to build better models and avoid these problems. The course offers a framework data scientists can use to develop their projects and an audit process to follow in reviewing them. Case studies with Python code are provided.

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PyTorch Basics for Machine Learning

PyTorch Basics for Machine Learning

This course is the first part in a two part course and will teach you the fundamentals of PyTorch. In this course you will implement classic machine learning algorithms, focusing on how PyTorch creates and optimizes models. You will quickly iterate through different aspects of PyTorch giving you strong foundations and all the prerequisites you need before you build deep learning models.

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Developing Industrial Internet of Things

Developing Industrial Internet of Things

The courses in this specialization can also be taken for academic credit as ECEA 5385-5387, part of CU Boulder’s Master of Science in Electrical Engineering degree. Enroll here. In this specialization, you will engage the vast array of technologies that can be used to build an industrial internet of things deployment.

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DevOps on AWS

DevOps on AWS

DevOps on AWS specialization teaches you how to use the combination of DevOps philosophies, practices and tools to develop, deploy, and maintain applications in the AWS Cloud. Benefits of adopting DevOps include: rapid delivery, reliability, scalability, security and improved collaboration. The first course introduces you to essential AWS products, services, and common solutions.

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Accounting Data Analytics

Accounting Data Analytics

This specialization develops learners’ analytics mindset and knowledge of data analytics tools and techniques. Specifically, this specialization develops learners' analytics skills by first introducing an analytic mindset, data preparation, visualization, and analysis using Excel. Next, this specialization develops learners' skills of using Python for data preparation, data visualization, data analysis, and data interpretation and the ability to apply these skills to issues relevant to accounting.

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IBM Introduction to Machine Learning

IBM Introduction to Machine Learning

Machine learning skills are becoming more and more essential in the modern job market. In 2019, Machine Learning Engineer was ranked as the #1 job in the United States, based on the incredible 344% growth of job openings in the field between 2015 to 2018, and the role’s average base salary of $146,085 (Indeed). This four-course Specialization will help you gain the introductory skills to succeed in an in-demand career in machine learning and data science.

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Machine Learning for Marketing

Machine Learning for Marketing

Understand the structure and techniques used in Machine Learning, Text Mining, and Decision Science for Marketing. Explore the fascinating world of Machine Learning and its transformative applications in marketing. Explain how analytics and decision science approaches for marketing can enhance the quality of marketing decision-making. Foundation in digital marketing analytics to understand the consumer journey, intent, and activity on your business website.

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Advanced Data Science with IBM

Advanced Data Science with IBM

As a coursera certified specialization completer you will have a proven deep understanding on massive parallel data processing, data exploration and visualization, and advanced machine learning & deep learning. You'll un…

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CertNexus Certified Artificial Intelligence Practitioner

CertNexus Certified Artificial Intelligence Practitioner

The Certified Artificial Intelligence Practitioner™ (CAIP) specialization prepares learners to earn an industry validated certification which will differentiate themselves from other job candidates and demonstrate proficiency in the concepts of Artificial intelligence (AI) and machine learning (ML) found in CAIP. AI and ML have become an essential part of the toolset for many organizations. When used effectively, these tools provide actionable insights that drive critical decisions and enable organizations to create exciting, new, and innovative products and services.

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Microsoft Azure Data Scientist Associate (DP-100)

Microsoft Azure Data Scientist Associate (DP-100)

This Professional Certificate is intended for data scientists with existing knowledge of Python and machine learning frameworks like Scikit-Learn, PyTorch, and Tensorflow, who want to build and operate machine learning s…

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Mathematics for Machine Learning and Data Science

Mathematics for Machine Learning and 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.

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Data Engineering, Big Data, and Machine Learning on GCP

Data Engineering, Big Data, and Machine Learning on GCP

This five-week, accelerated online specialization provides participants a hands-on introduction to designing and building data processing systems on Google Cloud Platform. Through a combination of presentations, demos, and hand-on labs, participants will learn how to design data processing systems, build end-to-end data pipelines, analyze data and carry out machine learning.

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TensorFlow: Data and Deployment

TensorFlow: Data and Deployment

Continue developing your skills in TensorFlow as you learn to navigate through a wide range of deployment scenarios and discover new ways to use data more effectively when training your machine learning models. In this four-course Specialization, you’ll learn how to get your machine learning models into the hands of real people on all kinds of devices. Start by understanding how to train and run machine learning models in browsers and in mobile applications.

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Digital Health

Digital Health

This specialisation introduces students to the emerging and multidisciplinary field of digital health and the role and application of digital health technologies including mobile applications, wearable technologies, health information systems, telehealth, telemedicine, machine learning, artificial intelligence and big data. These digital health technologies are assessed in terms of their opportunities and challenges to address real-world public health and health care system challenges in order to improve the quality, safety and efficiency of these services.

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IBM AI Enterprise Workflow

IBM AI Enterprise Workflow

This six course specialization is designed to prepare you to take the certification examination for IBM AI Enterprise Workflow V1 Data Science Specialist. IBM AI Enterprise Workflow is a comprehensive, end-to-end process that enables data scientists to build AI solutions, starting with business priorities and working through to taking AI into production.

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Reinforcement Learning

Reinforcement Learning

This course provides an overview of reinforcement learning, a type of machine learning that has the potential to solve control system problems that are too difficult to solve with traditional techniques. You'll work through the basics of the reinforcement problem and how it differs from traditional control techniques. You'll also see how neural networks are used to represent unknown functions and how the agent uses rewards from the environment to train them.

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Machine learning with Python for finance professionals

Machine learning with Python for finance professionals

A machine learning course focused on delivering practical Python skills for finance professionals looking to maximise their use of these time-saving tools within their organisation.

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Dynamic Programming: Applications In Machine Learning and Genomics

Dynamic Programming: Applications In Machine Learning and Genomics

Learn how dynamic programming and Hidden Markov Models can be used to compare genetic strings and uncover evolution.

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