Our Courses

Methods and Application in Human-Robot Interaction
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Data Analytics and Visualization in Health Care
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Introduction to Robotics with Webots

Introduction to Robotics with Webots

The overarching learning goal of this specialization in robotics is to create an appreciation for the tight interplay between mechanism, sensor, and control in the design of intelligent systems. By the end of this specia…

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Machine Learning Implementation and Operations in AWS

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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Exploratory Data Analysis in AWS

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.

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Healthcare Analytics and Decision Making

Healthcare Analytics and Decision Making

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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 indus…

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Econometrics: Methods and Applications

Econometrics: Methods and Applications

Welcome!
Do you wish to know how to analyze and solve business and economic questions with data analysis tools? Then Econometrics by Erasmus University Rotterdam is the right course for you, as you learn how to translate data into models to make forecasts and to support decision making.
* What do I learn?

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  • 66 hours
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Prompt Engineering for ChatGPT

Prompt Engineering for ChatGPT

ChatGPT and other large language models are going to be more important in your life and business than your smartphone, if you use them right. ChatGPT can tutor your child in math, generate a meal plan and recipes, write software applications for your business, help you improve your personal cybersecurity, and that is just in the first hour that you use it. This course will teach you how to be an expert user of these generative AI tools.

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  • 19 hours
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Sport Analytics: Data Driven Decision Making

Sport Analytics: Data Driven Decision Making

Analytics have revolutionized sport, providing a competitive advantage to organizational decision-making both on and off the field. This course introduces best practices utilized in sport business analytics. The course touches on data collection, fact finding, visualization, and metrics that guide strategic decision-making in the sport industry. This course will touch on many aspects of the sports industry, including professional sports.

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Big Data Computing with Spark

Big Data Computing with Spark

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Extract, Transform and Load Data in Power BI

Extract, Transform and Load Data in Power BI

This course forms part of the Microsoft Power BI Analyst Professional Certificate. This Professional Certificate consists of a series of courses that offers a good starting point for a career in data analysis using Microsoft Power BI.
In this course, you will learn the process of Extract, Transform and Load or ETL. You will identify how to collect data from and configure multiple sources in Power BI and prepare and clean data using Power Query. You’ll also have the opportunity to inspect and analyze ingested data to ensure data integrity.

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  • 20 hours
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Build a Deep Learning Based Image Classifier with R

Build a Deep Learning Based Image Classifier with R

In this 45-min guided project, you will learn the basics of using the Keras interface to R with Tensorflow as its backend to solve an image classification problem.

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Introduction to Machine Learning: Supervised Learning

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.

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  • 40 hours
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IBM AI Foundations for Business

IBM AI Foundations for Business

This specialization will explain and describe the overall focus areas for business leaders considering AI-based solutions for business challenges. The first course provides a business-oriented summary of technologies and…

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Data Collection and Integration

Data Collection and Integration

The "Data Collection and Integration" course provides students with comprehensive techniques for gathering data from diverse sources, including files, relational databases, web pages, and APIs. Participants will gain practical experience in collecting and integrating data for further processing and analysis. The course emphasizes the utilization of appropriate tools and packages, such as Pandas, Beautiful Soup, and SQL, to effectively handle real-life datasets and address data integration challenges.

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Try It: Introduction to Google Analytics

Try It: Introduction to Google Analytics

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Analytics for Decision Making

Analytics for Decision Making

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Explainable deep learning models for healthcare - CDSS 3

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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  • 30 hours
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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, the…

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Introduction to Human-Robot Interaction

Introduction to Human-Robot Interaction

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Python for AI & Development Project

Python for AI & Development Project

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Foundations of Data Analytics

Foundations of Data Analytics

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Modern Robotics:  Mechanics, Planning, and Control

Modern Robotics: Mechanics, Planning, and Control

This Specialization provides a rigorous treatment of spatial motion and the dynamics of rigid bodies, employing representations from modern screw theory and the product of exponentials formula. Students with a freshman-l…

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PyTorch Basics for Machine Learning

PyTorch Basics for Machine Learning

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