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Our Courses
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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Machine Learning Implementation and Operations in AWS
Machine Learning Implementation Operations in AWS is the fifth Course in the AWS Certified Machine Learning Specialty specialization. The course has a major focus on designing and implementing machine learning solutions for performance, availability, scalability, resiliency, and fault tolerance. This course is divided into two modules and each module is further segmented by Lessons and Video Lectures. This course facilitates learners with approximately 1:00-1:30 Hours Video lectures that provide both Theory and Hands -On knowledge.
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6 hours
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English
Introduction to Machine Learning: Supervised Learning
In this course, you’ll be learning various supervised ML algorithms and prediction tasks applied to different data. You’ll learn when to use which model and why, and how to improve the model performances. We will cover models such as linear and logistic regression, KNN, Decision trees and ensembling methods such as Random Forest and Boosting, kernel methods such as SVM. Prior coding or scripting knowledge is required. We will be utilizing Python extensively throughout the course.
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40 hours
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English
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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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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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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Linear Algebra Basics
Machine learning and data science are the most popular topics of research nowadays. They are applied in all the areas of engineering and sciences. Various machine learning tools provide a data-driven solution to various real-life problems. Basic knowledge of linear algebra is necessary to develop new algorithms for machine learning and data science. In this course, you will learn about the mathematical concepts related to linear algebra, which include vector spaces, subspaces, linear span, basis, and dimension.
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21 hours
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English
MATLAB Programming for Engineers and Scientists
This Specialization aims to take learners with little to no programming experience to being able to create MATLAB programs that solve real-world problems in engineering and the sciences. The focus is on computer programming in general, but the numerous language features that make MATLAB uniquely suited to engineering and scientific computing are also covered in depth.
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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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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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Doing Clinical Research: Biostatistics with the Wolfram Language
This course aims to empower you to do statistical tests, ready for incorporation into your dissertations, research papers, and presentations. The ability to summarize data, create plots and charts, and to do the statistical tests that you commonly see in the literature is a powerful skill indeed. There are powerful tools readily available to achieve these goals. None are quite as easy to learn, yet as powerful to use, as the Wolfram Language. Knowledge is literally built into the language.
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15 hours
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English
Data Engineering, Big Data, and Machine Learning on GCP
87% of Google Cloud certified users feel more confident in their cloud skills.
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Natural Language Processing in Microsoft Azure
Natural language processing supports applications that can see, hear, speak with, and understand users. Using text analytics, translation, and language understanding services, Microsoft Azure makes it easy to build applications that support natural language. In this course, you will learn how to use the Text Analytics service for advanced natural language processing of raw text for sentiment analysis, key phrase extraction, named entity recognition, and language detection. You will learn how to recognize and synthesize speech by using Azure Cognitive Services.
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8 hours
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English
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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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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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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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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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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Creating Multi Task Models With Keras
In this 1 hour long guided project, you will learn to create and train multi-task, multi-output models with Keras. You will learn to use Keras' functional API to create a multi output model which will be trained to learn two different labels given the same input example. The model will have one input but two outputs. A few of the shallow layers will be shared between the two outputs, you will also use a ResNet style skip connection in the model. If you are familiar with Keras, you have probably come across examples of models that are trained to perform multiple tasks.
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3 hours
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English
Developing Applications with Google Cloud
In this specialization, application developers learn how to design, develop, and deploy applications that seamlessly integrate managed services from Google Cloud. Through a combination of presentations, demos, and hands-on labs, participants learn how to use Google Cloud services and pre-trained machine learning APIs to build secure, scalable, and intelligent cloud-native applications.
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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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IBM Machine Learning
Prepare for a career in the field of machine learning. In this program, you’ll learn in-demand skills like AI and Machine Learning to get job-ready in less than 3 months. Machine Learning is the use and development of computer systems that are able to learn and adapt by using algorithms and statistical models to analyze and draw inferences from patterns in data. Machine Learning is a branch of Artificial Intelligence (AI) where computers are taught to imitate human intelligence in that they solve complex tasks.
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Machine Learning Engineering for Production (MLOps)
Understanding machine learning and deep learning concepts is essential, but if you’re looking to build an effective AI career, you need production engineering capabilities as well. Effectively deploying machine learning…
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Mathematics for Machine Learning
For a lot of higher level courses in Machine Learning and Data Science, you find you need to freshen up on the basics in mathematics - stuff you may have studied before in school or university, but which was taught in another context, or not very intuitively, such that you struggle to relate it to how it’s used in Computer Science.
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Introduction to Image Generation - Français
Ce cours présente les modèles de diffusion, une famille de modèles de machine learning qui s'est récemment révélée prometteuse dans le domaine de la génération d'images. Les modèles de diffusion trouvent leur origine dans la physique, et plus précisément dans la thermodynamique. Au cours des dernières années, ils ont gagné en popularité dans la recherche et l'industrie. Ils sont à la base de nombreux modèles et outils Google Cloud avancés de génération d'images.
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