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Our Courses
AI for Everyone: Master the Basics
Learn what Artificial Intelligence (AI) is by understanding its applications and key concepts including machine learning, deep learning and neural networks.
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33
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English
Machine Learning for Semiconductor Quantum Devices
Learn how to deploy artificial intelligence to control and calibrate semiconductor quantum computing chips
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27
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English
Introduction to Machine Learning on AWS
This course is intended for software developers and engineers taking their first steps with the AWS services that do much of heavy lifting of Machine Learning for you.
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15
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English
Machine Learning at the Edge on Arm: A Practical Introduction
****This course will provide you with the hands-on experience you’ll need to create innovative machine learning applications using ubiquitous Arm-based microcontrollers.
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8
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English
Robotic process and intelligent automation for finance
In this course we explain how automation can play a key role in delivering the requirement to have robust processes and clean data. By using automation tools and machine learning, finance leaders can identify, impl
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Self Paced
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15
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English
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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Self Paced
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English
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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English
AI for Medicine
AI is transforming the practice of medicine. It’s helping doctors diagnose patients more accurately, make predictions about patients’ future health, and recommend better treatments. This three-course Specialization will give you practical experience in applying machine learning to concrete problems in medicine. These courses go beyond the foundations of deep learning to teach you the nuances in applying AI to medical use cases. If you are new to deep learning or want to get a deeper foundation of how neural networks work, we recommend taking the Deep Learning Specialization.
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Self Paced
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English
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
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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Self Paced
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English
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
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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Self Paced
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English
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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Self Paced
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English
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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Self Paced
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English
Explainable deep learning models for healthcare - CDSS 3
This course will introduce the concepts of interpretability and explainability in machine learning applications. The learner will understand the difference between global, local, model-agnostic and model-specific explanations. State-of-the-art explainability methods such as Permutation Feature Importance (PFI), Local Interpretable Model-agnostic Explanations (LIME) and SHapley Additive exPlanation (SHAP) are explained and applied in time-series classification.
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Self Paced
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30 hours
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English
Meta Back-End Developer
Ready to gain new skills and the tools developers use to create websites and web applications? This certificate, designed by the software engineering experts at Meta—the creators of Facebook and Instagram, will prepare you for an entry-level career as a back-end developer. In this program, you’ll learn: Python Syntax—the most popular choice for machine learning, data science and artificial intelligence. In-demand programming skills and how to confidently use code to solve problems. Linux commands and Git repositories to implement version control.
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Self Paced
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English
Natural Language Processing
Natural Language Processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence that uses algorithms to interpret and manipulate human language. This technology is one of the most broadly applied areas of machine learning and is critical in effectively analyzing massive quantities of unstructured, text-heavy data.
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Self Paced
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English
Data Science: Machine Learning
Build a movie recommendation system and learn the science behind one of the most popular and successful data science techniques.
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25
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English
Practical Data Science on the AWS Cloud
Development environments might not have the exact requirements as production environments. Moving data science and machine learning projects from idea to production requires state-of-the-art skills. You need to architect…
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Self Paced
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English
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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Self Paced
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English
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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Self Paced
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English
Deep Learning
An introduction to the field of deep learning, including neural networks, convolutional neural networks, recurrent neural networks, transformers, generative models, neural network compression and transfer learning. This course will benefit students’ careers as a machine learning engineer or data scientist.
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Self Paced
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English
Matrix Methods
Mathematical Matrix Methods lie at the root of most methods of machine learning and data analysis of tabular data. Learn the basics of Matrix Methods, including matrix-matrix multiplication, solving linear equations, orthogonality, and best least squares approximation. Discover the Singular Value Decomposition that plays a fundamental role in dimensionality reduction, Principal Component Analysis, and noise reduction. Optional examples using Python are used to illustrate the concepts and allow the learner to experiment with the algorithms.
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Self Paced
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7 hours
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English
Exploratory Data Analysis in AWS
Exploratory Data Analysis in AWS is the second course in the AWS Certified Machine Learning Specialty specialization. The main focus of this course is to analyze Data Streams and Data Analytics services in AWS along with exploring Data Analysis in AWS. This course is divided into two modules and each module is further segmented by Lessons and Video Lectures. This course facilitates learners with approximately 2:00-2:30 Hours Video lectures that provide both Theory and Hands -On knowledge. Also, Graded and Ungraded Quiz are provided with every module in order to test the ability of learners.
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5 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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Self Paced
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40 hours
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English