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Our Courses
Machine Learning for Supply Chains
This specialization is intended for students who wish to use machine language to analyze and predict product usage and other similar tasks. There is no specific prerequisite but some general knowledge of supply chain will be helpful, as well as general statistics and calculus.
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Course by
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Self Paced
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English
Computer Vision Basics
By the end of this course, learners will understand what computer vision is, as well as its mission of making computers see and interpret the world as humans do, by learning core concepts of the field and receiving an introduction to human vision capabilities. They are equipped to identify some key application areas of computer vision and understand the digital imaging process. The course covers crucial elements that enable computer vision: digital signal processing, neuroscience and artificial intelligence.
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13 hours
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English
Autonomous Vehicle Engineering
This specialization is intended for students seeking to explore the Autonomous Vehicle sector, which is undergoing profound transformation in today’s world.
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English
Data Understanding and Visualization
The "Data Understanding and Visualization" course provides students with essential statistical concepts to comprehend and analyze datasets effectively. Participants will learn about central tendency, variation, location, correlation, and other fundamental statistical measures. Additionally, the course introduces data visualization techniques using Pandas, Matplotlib, and Seaborn packages, enabling students to present data visually with appropriate plots for accurate and efficient communication. Learning Objectives: 1.
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Course by
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25 hours
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English
Clinical Decision Support Systems - CDSS 4
Machine learning systems used in Clinical Decision Support Systems (CDSS) require further external validation, calibration analysis, assessment of bias and fairness. In this course, the main concepts of machine learning evaluation adopted in CDSS will be explained. Furthermore, decision curve analysis along with human-centred CDSS that need to be explainable will be discussed. Finally, privacy concerns of deep learning models and potential adversarial attacks will be presented along with the vision for a new generation of explainable and privacy-preserved CDSS.
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Course by
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Self Paced
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8 hours
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English
Programming in Python: A Hands-on Introduction
This specialization is intended for people without programming experience who seek to develop python programming skills and learn about the underlying computer science concepts that will allow them to pick up other programming languages quickly. In these four courses, you will cover everything from fundamentals to object-oriented design. These topics will help prepare you to write anything from small programs to automate repetitive tasks to larger applications, giving you enough understanding of python to tackle more specialized topics such as Data Science and Artificial Intelligence.
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Course by
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Self Paced
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English
Managing Machine Learning Projects
This second course of the AI Product Management Specialization by Duke University's Pratt School of Engineering focuses on the practical aspects of managing machine learning projects. The course walks through the keys steps of a ML project from how to identify good opportunities for ML through data collection, model building, deployment, and monitoring and maintenance of production systems.
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18 hours
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English
Practical Predictive Analytics: Models and Methods
Statistical experiment design and analytics are at the heart of data science. In this course you will design statistical experiments and analyze the results using modern methods. You will also explore the common pitfalls in interpreting statistical arguments, especially those associated with big data. Collectively, this course will help you internalize a core set of practical and effective machine learning methods and concepts, and apply them to solve some real world problems. Learning Goals: After completing this course, you will be able to: 1.
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7 hours
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English
BigQuery Soccer Data Analytical Insight
This is a self-paced lab that takes place in the Google Cloud console. Learn how to create deeper analytical insights from soccer event data using BigQuery. BigQuery can be used to perform more sophisticated data analysis. In this lab, you will analyze soccer event data to achieve real insight from the dataset.
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Course by
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Self Paced
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1 hour
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English
Excel Skills for Data Analytics and Visualization
As data becomes the modern currency, so the ability to quickly and accurately analyse data has become of paramount importance. Therefore, data analytics and visualization are two of the most sought after skills for high paying jobs with strong future growth prospects. According to an IBM report, the Excel tools for data analytics and visualization are among the top 10 competencies projected to show double-digit growth in their demand.
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Course by
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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
AI Workflow: Machine Learning, Visual Recognition and NLP
This is the fourth course in the IBM AI Enterprise Workflow Certification specialization. You are STRONGLY encouraged to complete these courses in order as they are not individual independent courses, but part of a workflow where each course builds on the previous ones. Course 4 covers the next stage of the workflow, setting up models and their associated data pipelines for a hypothetical streaming media company. The first topic covers the complex topic of evaluation metrics, where you will learn best practices for a number of different metrics including regressi
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Course by
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Self Paced
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14 hours
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English
Applied Data Science with Python
The 5 courses in this University of Michigan specialization introduce learners to data science through the python programming language.
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Course by
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Self Paced
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English
Recommender Systems
This specialization 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 specialization. This specialization will teach you to build advanced recommender systems using machine learning and AI. You will begin by learning Python to evaluate datasets and create content-based and collaborative filtering systems.
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English
Python Basics: Automation and Bots
Understanding the flow of running code is a major part of learning to think in code and of coding itself. In this course we will study the flow of code through several demonstrations and walkthroughs. We'll experience turning logic into useful work by running Python that automatically reads all of Shakespeare, and by setting Python up to give you a call on the phone. In technical terms, this course will demonstrate Python loops, list comprehensions, and conditional statements, while at a higher level we'll discuss code style and good practices for code.
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Course by
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Self Paced
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13 hours
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English
Build your first Search Engine using AWS Kendra
This project is focused on building your first search engine using Amazon Kendra without writing a single line of code. By the end of this guided project, you will be able to build your first enterprise search engine by leveraging Amazon’s Kendra. Search as a capability is an important feature which is required by almost all medium and large enterprises as search helps filter relevant and required information in the world of big data. Search helps find relevant information quickly and saves time to go through vast information.
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4 hours
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English
A Complete Reinforcement Learning System (Capstone)
In this final course, you will put together your knowledge from Courses 1, 2 and 3 to implement a complete RL solution to a problem. This capstone will let you see how each component---problem formulation, algorithm selection, parameter selection and representation design---fits together into a complete solution, and how to make appropriate choices when deploying RL in the real world. This project will require you to implement both the environment to stimulate your problem, and a control agent with Neural Network function approximation.
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Course by
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Self Paced
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16 hours
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English
Quantitative Text Analysis and Measures of Readability in R
By the end of this project, you will be able to load textual data into R and turn it into a corpus object.
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Course by
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Self Paced
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3 hours
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English
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? When you know econometrics, you are able to translate data into models to make forecasts and to support decision making in a wide variety of fields, ranging from macroeconomics to finance and marketing.
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Course by
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Self Paced
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66 hours
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English
Bayesian Statistics: Capstone Project
This is the capstone project for UC Santa Cruz's Bayesian Statistics Specialization. It is an opportunity for you to demonstrate a wide range of skills and knowledge in Bayesian statistics and to apply what you know to real-world data. You will review essential concepts in Bayesian statistics with lecture videos and quizzes, and you will perform a complex data analysis and compose a report on your methods and results.
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12 hours
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English
Deep Learning and Reinforcement Learning
This course introduces you to two of the most sought-after disciplines in Machine Learning: Deep Learning and Reinforcement Learning. Deep Learning is a subset of Machine Learning that has applications in both Supervised and Unsupervised Learning, and is frequently used to power most of the AI applications that we use on a daily basis. First you will learn about the theory behind Neural Networks, which are the basis of Deep Learning, as well as several modern architectures of Deep Learning.
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32 hours
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English
Recommender Systems: Evaluation and Metrics
In this course you will learn how to evaluate recommender systems. You will gain familiarity with several families of metrics, including ones to measure prediction accuracy, rank accuracy, decision-support, and other factors such as diversity, product coverage, and serendipity. You will learn how different metrics relate to different user goals and business goals. You will also learn how to rigorously conduct offline evaluations (i.e., how to prepare and sample data, and how to aggregate results). And you will learn about online (experimental) evaluation.
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Course by
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Self Paced
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7 hours
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English
Mind and Machine
This specialization examines the ways in which our current understanding of human thinking is both illuminated and challenged by the evolving techniques and ideas of artificial intelligence and computer science. Our collective understanding of “minds” – both biological and computational – has been revolutionized over the past half-century by themes originating in fields like cognitive psychology, machine learning, neuroscience, evolutionary psychology, and game theory, among others.
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English
Line Balancing With MILP Optimization In RStudio
By the end of this project, you will learn to use R lpSolveAPI.
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Course by
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Self Paced
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3 hours
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English
Launching into Machine Learning - 한국어
이 과정에서는 먼저 데이터에 관해 논의하면서 데이터 품질을 개선하고 탐색적 데이터 분석을 수행하는 방법을 알아봅니다. Vertex AI AutoML과 코드를 한 줄도 작성하지 않고 ML 모델을 빌드하고, 학습시키고, 배포하는 방법을 설명합니다. 학습자는 Big Query ML의 이점을 이해할 수 있습니다. 그런 다음, 머신러닝(ML) 모델 최적화 방법과 일반화 및 샘플링으로 커스텀 학습용 ML 모델 품질을 평가하는 방법을 다룹니다.
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Course by
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Self Paced
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English