University of Toronto
Public University • CA
Showing 45 courses from UofT
Neural Networks for Machine Learning - Geoffrey Hinton
University of Toronto (via Coursera)
Neural Networks for Machine Learning - Geoffrey Hinton is a comprehensive intermediate-level resource offered by University of Toronto, focused on building practical skills in artificial intelligence and machine learning. Whether you're a complete beginner looking to start a new career or a professional aiming to upgrade your skills, this resource provides a thorough learning experience. This is a structured online course with a carefully designed curriculum. Each module builds on the previous one, creating a logical progression from fundamentals to advanced topics. The course typically includes video lectures, reading materials, hands-on exercises, quizzes, and sometimes peer-reviewed assignments. This structured approach ensures you don't miss any critical concepts and build a solid foundation. This resource covers topics essential for success in artificial intelligence and machine learning, including machine learning algorithms, deep learning, NLP, computer vision, and model deployment. The curriculum is structured to build your knowledge progressively — starting with foundational concepts and advancing to real-world applications. By the end, you should be able to: Build supervised and unsupervised ML models with scikit-learn Master regression, classification, and clustering algorithms Evaluate models using cross-validation and proper metrics Deploy ML models to production Duration: Estimated duration: 20 hours of content, designed to be completed in 2-4 weeks at a comfortable pace. Basic familiarity with the subject area is recommended. You should have completed a beginner-level course or have equivalent self-taught knowledge. Comfort with using a computer and basic problem-solving skills will help. This resource is designed for a wide audience: Students (B.Tech, BCA, MCA, BSc) looking to complement their academic learning with practical, industry-relevant skills Fresh graduates preparing for campus placements or off-campus interviews Working professionals looking to upskill, switch domains, or advance their careers Career changers transitioning from non-tech backgrounds into artificial intelligence and machine learning Freelancers wanting to add new services to their portfolio Self-learners passionate about artificial intelligence and machine learning and wanting structured guidance Pricing: This resource is completely free with no hidden charges. Completing this resource and building related skills can prepare you for roles such as ML Engineer, AI Engineer, Data Scientist, Research Scientist. Realistic salary bands in India (2025-2026), based on Naukri/AmbitionBox data: Freshers / 0-2 years: Rs 8-15 LPA Mid-level / 2-5 years: Rs 18-35 LPA Senior / 5+ years: Rs 40-80 LPA Actual offers vary heavily by city, company tier, and how strong your portfolio or interview performance is. Companies actively hiring in this space include Google, Microsoft, OpenAI, Indian AI startups, research labs. India is the second-largest AI talent pool globally, and the demand far exceeds supply. The Indian AI market is expected to reach $17 billion by 2027. Every major Indian tech company — from Infosys to Reliance to Jio — is investing heavily in AI capabilities. The emergence of generative AI has created entirely new job categories that didn't exist two years ago. ML engineers with LLM experience are commanding Rs 30-60 LPA even at early career stages. University of Toronto is a well-established platform trusted by millions of learners worldwide. This particular resource has been selected by our editorial team based on: Content quality — comprehensive coverage with clear explanations Practical focus — emphasis on hands-on skills over pure theory Student outcomes — positive reviews and career success stories Indian relevance — content applicable to the Indian job market and interview patterns Updated curriculum — material reflects current industry practices and tools We regularly review and update our recommendations to ensure they remain relevant and high-quality.
Neural Networks for Machine Learning - Geoffrey Hinton
University of Toronto (via Coursera)
Neural Networks for Machine Learning - Geoffrey Hinton is a comprehensive advanced-level resource offered by University of Toronto, focused on building practical skills in data science and analytics. Whether you're a complete beginner looking to start a new career or a professional aiming to upgrade your skills, this resource provides a thorough learning experience. This is a structured online course with a carefully designed curriculum. Each module builds on the previous one, creating a logical progression from fundamentals to advanced topics. The course typically includes video lectures, reading materials, hands-on exercises, quizzes, and sometimes peer-reviewed assignments. This structured approach ensures you don't miss any critical concepts and build a solid foundation. This resource covers topics essential for success in data science and analytics, including Python, SQL, Pandas, NumPy, data visualization, statistics, and machine learning basics. The curriculum is structured to build your knowledge progressively — starting with foundational concepts and advancing to real-world applications. By the end, you should be able to: Build supervised and unsupervised ML models with scikit-learn Master regression, classification, and clustering algorithms Evaluate models using cross-validation and proper metrics Deploy ML models to production Duration: Estimated duration: 40 hours of content, designed to be completed in 4-8 weeks at a comfortable pace. This is an advanced resource meant for learners who already have solid fundamentals. You should have at least 6 months of hands-on experience or have completed intermediate-level courses in this area. This resource is designed for a wide audience: Students (B.Tech, BCA, MCA, BSc) looking to complement their academic learning with practical, industry-relevant skills Fresh graduates preparing for campus placements or off-campus interviews Working professionals looking to upskill, switch domains, or advance their careers Career changers transitioning from non-tech backgrounds into data science and analytics Freelancers wanting to add new services to their portfolio Self-learners passionate about data science and analytics and wanting structured guidance Pricing: This resource is completely free with no hidden charges. Completing this resource and building related skills can prepare you for roles such as Data Analyst, Business Analyst, Data Scientist, Analytics Engineer. Realistic salary bands in India (2025-2026), based on Naukri/AmbitionBox data: Freshers / 0-2 years: Rs 4-8 LPA Mid-level / 2-5 years: Rs 10-22 LPA Senior / 5+ years: Rs 25-50 LPA Actual offers vary heavily by city, company tier, and how strong your portfolio or interview performance is. Companies actively hiring in this space include TCS, Infosys, Flipkart, Amazon, Swiggy, Zomato, PhonePe. The data science industry in India is projected to grow at 27% CAGR through 2028. Companies across all sectors — from banking (HDFC, ICICI) to e-commerce (Flipkart, Amazon) to healthcare (Practo, PharmEasy) — are building data teams. India currently has a shortage of 200,000+ data professionals, making this one of the best fields to enter right now. Cities like Bangalore, Hyderabad, Pune, and Gurgaon have the highest concentration of data science jobs. University of Toronto is a well-established platform trusted by millions of learners worldwide. This particular resource has been selected by our editorial team based on: Content quality — comprehensive coverage with clear explanations Practical focus — emphasis on hands-on skills over pure theory Student outcomes — positive reviews and career success stories Indian relevance — content applicable to the Indian job market and interview patterns Updated curriculum — material reflects current industry practices and tools We regularly review and update our recommendations to ensure they remain relevant and high-quality.
Self-Driving Cars Specialization - University of Toronto
University of Toronto (via Coursera)
Self-Driving Cars Specialization - University of Toronto is a comprehensive advanced-level resource offered by University of Toronto, focused on building practical skills in data science and analytics. Whether you're a complete beginner looking to start a new career or a professional aiming to upgrade your skills, this resource provides a thorough learning experience. This is a structured online course with a carefully designed curriculum. Each module builds on the previous one, creating a logical progression from fundamentals to advanced topics. The course typically includes video lectures, reading materials, hands-on exercises, quizzes, and sometimes peer-reviewed assignments. This structured approach ensures you don't miss any critical concepts and build a solid foundation. This resource covers topics essential for success in data science and analytics, including Python, SQL, Pandas, NumPy, data visualization, statistics, and machine learning basics. The curriculum is structured to build your knowledge progressively — starting with foundational concepts and advancing to real-world applications. By the end, you should be able to: Understand the core concepts and theoretical foundations Apply your knowledge through hands-on exercises and small projects Build the practical skills employers actually screen for Develop the problem-solving approach used by working professionals Duration: Estimated duration: 20 hours of content, designed to be completed in 2-4 weeks at a comfortable pace. This is an advanced resource meant for learners who already have solid fundamentals. You should have at least 6 months of hands-on experience or have completed intermediate-level courses in this area. This resource is designed for a wide audience: Students (B.Tech, BCA, MCA, BSc) looking to complement their academic learning with practical, industry-relevant skills Fresh graduates preparing for campus placements or off-campus interviews Working professionals looking to upskill, switch domains, or advance their careers Career changers transitioning from non-tech backgrounds into data science and analytics Freelancers wanting to add new services to their portfolio Self-learners passionate about data science and analytics and wanting structured guidance Pricing: This resource is completely free with no hidden charges. Completing this resource and building related skills can prepare you for roles such as Data Analyst, Business Analyst, Data Scientist, Analytics Engineer. Realistic salary bands in India (2025-2026), based on Naukri/AmbitionBox data: Freshers / 0-2 years: Rs 4-8 LPA Mid-level / 2-5 years: Rs 10-22 LPA Senior / 5+ years: Rs 25-50 LPA Actual offers vary heavily by city, company tier, and how strong your portfolio or interview performance is. Companies actively hiring in this space include TCS, Infosys, Flipkart, Amazon, Swiggy, Zomato, PhonePe. The data science industry in India is projected to grow at 27% CAGR through 2028. Companies across all sectors — from banking (HDFC, ICICI) to e-commerce (Flipkart, Amazon) to healthcare (Practo, PharmEasy) — are building data teams. India currently has a shortage of 200,000+ data professionals, making this one of the best fields to enter right now. Cities like Bangalore, Hyderabad, Pune, and Gurgaon have the highest concentration of data science jobs. University of Toronto is a well-established platform trusted by millions of learners worldwide. This particular resource has been selected by our editorial team based on: Content quality — comprehensive coverage with clear explanations Practical focus — emphasis on hands-on skills over pure theory Student outcomes — positive reviews and career success stories Indian relevance — content applicable to the Indian job market and interview patterns Updated curriculum — material reflects current industry practices and tools We regularly review and update our recommendations to ensure they remain relevant and high-quality.
Introduction to Artificial Intelligence - University of Toronto
University of Toronto (via Coursera)
Introduction to Artificial Intelligence - University of Toronto is a comprehensive beginner-level resource offered by University of Toronto, focused on building practical skills in artificial intelligence and machine learning. Whether you're a complete beginner looking to start a new career or a professional aiming to upgrade your skills, this resource provides a thorough learning experience. This is a structured online course with a carefully designed curriculum. Each module builds on the previous one, creating a logical progression from fundamentals to advanced topics. The course typically includes video lectures, reading materials, hands-on exercises, quizzes, and sometimes peer-reviewed assignments. This structured approach ensures you don't miss any critical concepts and build a solid foundation. This resource covers topics essential for success in artificial intelligence and machine learning, including machine learning algorithms, deep learning, NLP, computer vision, and model deployment. The curriculum is structured to build your knowledge progressively — starting with foundational concepts and advancing to real-world applications. By the end, you should be able to: Understand the core concepts and theoretical foundations Apply your knowledge through hands-on exercises and small projects Build the practical skills employers actually screen for Develop the problem-solving approach used by working professionals Duration: Estimated duration: 15 hours of content, designed to be completed in 2-3 weeks at a comfortable pace. No prior experience is required. This course starts from the absolute basics and gradually builds up complexity. A computer with internet access is all you need to get started. This resource is designed for a wide audience: Students (B.Tech, BCA, MCA, BSc) looking to complement their academic learning with practical, industry-relevant skills Fresh graduates preparing for campus placements or off-campus interviews Working professionals looking to upskill, switch domains, or advance their careers Career changers transitioning from non-tech backgrounds into artificial intelligence and machine learning Freelancers wanting to add new services to their portfolio Self-learners passionate about artificial intelligence and machine learning and wanting structured guidance Pricing: This resource is completely free with no hidden charges. Completing this resource and building related skills can prepare you for roles such as ML Engineer, AI Engineer, Data Scientist, Research Scientist. Realistic salary bands in India (2025-2026), based on Naukri/AmbitionBox data: Freshers / 0-2 years: Rs 8-15 LPA Mid-level / 2-5 years: Rs 18-35 LPA Senior / 5+ years: Rs 40-80 LPA Actual offers vary heavily by city, company tier, and how strong your portfolio or interview performance is. Companies actively hiring in this space include Google, Microsoft, OpenAI, Indian AI startups, research labs. India is the second-largest AI talent pool globally, and the demand far exceeds supply. The Indian AI market is expected to reach $17 billion by 2027. Every major Indian tech company — from Infosys to Reliance to Jio — is investing heavily in AI capabilities. The emergence of generative AI has created entirely new job categories that didn't exist two years ago. ML engineers with LLM experience are commanding Rs 30-60 LPA even at early career stages. University of Toronto is a well-established platform trusted by millions of learners worldwide. This particular resource has been selected by our editorial team based on: Content quality — comprehensive coverage with clear explanations Practical focus — emphasis on hands-on skills over pure theory Student outcomes — positive reviews and career success stories Indian relevance — content applicable to the Indian job market and interview patterns Updated curriculum — material reflects current industry practices and tools We regularly review and update our recommendations to ensure they remain relevant and high-quality.
Introduction to Graph Neural Networks - University of Toronto
University of Toronto (via Coursera)
Introduction to Graph Neural Networks - University of Toronto is a comprehensive advanced-level resource offered by University of Toronto, focused on building practical skills in artificial intelligence and machine learning. Whether you're a complete beginner looking to start a new career or a professional aiming to upgrade your skills, this resource provides a thorough learning experience. This is a structured online course with a carefully designed curriculum. Each module builds on the previous one, creating a logical progression from fundamentals to advanced topics. The course typically includes video lectures, reading materials, hands-on exercises, quizzes, and sometimes peer-reviewed assignments. This structured approach ensures you don't miss any critical concepts and build a solid foundation. This resource covers topics essential for success in artificial intelligence and machine learning, including machine learning algorithms, deep learning, NLP, computer vision, and model deployment. The curriculum is structured to build your knowledge progressively — starting with foundational concepts and advancing to real-world applications. By the end, you should be able to: Understand the core concepts and theoretical foundations Apply your knowledge through hands-on exercises and small projects Build the practical skills employers actually screen for Develop the problem-solving approach used by working professionals Duration: Estimated duration: 15 hours of content, designed to be completed in 2-3 weeks at a comfortable pace. This is an advanced resource meant for learners who already have solid fundamentals. You should have at least 6 months of hands-on experience or have completed intermediate-level courses in this area. This resource is designed for a wide audience: Students (B.Tech, BCA, MCA, BSc) looking to complement their academic learning with practical, industry-relevant skills Fresh graduates preparing for campus placements or off-campus interviews Working professionals looking to upskill, switch domains, or advance their careers Career changers transitioning from non-tech backgrounds into artificial intelligence and machine learning Freelancers wanting to add new services to their portfolio Self-learners passionate about artificial intelligence and machine learning and wanting structured guidance Pricing: This resource is completely free with no hidden charges. Completing this resource and building related skills can prepare you for roles such as ML Engineer, AI Engineer, Data Scientist, Research Scientist. Realistic salary bands in India (2025-2026), based on Naukri/AmbitionBox data: Freshers / 0-2 years: Rs 8-15 LPA Mid-level / 2-5 years: Rs 18-35 LPA Senior / 5+ years: Rs 40-80 LPA Actual offers vary heavily by city, company tier, and how strong your portfolio or interview performance is. Companies actively hiring in this space include Google, Microsoft, OpenAI, Indian AI startups, research labs. India is the second-largest AI talent pool globally, and the demand far exceeds supply. The Indian AI market is expected to reach $17 billion by 2027. Every major Indian tech company — from Infosys to Reliance to Jio — is investing heavily in AI capabilities. The emergence of generative AI has created entirely new job categories that didn't exist two years ago. ML engineers with LLM experience are commanding Rs 30-60 LPA even at early career stages. University of Toronto is a well-established platform trusted by millions of learners worldwide. This particular resource has been selected by our editorial team based on: Content quality — comprehensive coverage with clear explanations Practical focus — emphasis on hands-on skills over pure theory Student outcomes — positive reviews and career success stories Indian relevance — content applicable to the Indian job market and interview patterns Updated curriculum — material reflects current industry practices and tools We regularly review and update our recommendations to ensure they remain relevant and high-quality.
App Design and Development for iOS
University of Toronto (via Coursera)
In App Design and Development for iOS, the third course of the iOS App Development with Swift specialization, you will be developing foundational programming skills to support graphical element presentation and data manipulation from basic functions through to advanced processing. You will continue to build your skill set to use and apply core graphics, touch handling and gestures, animations and transitions, alerts and actions as well as advanced algorithms, threading and more. By the end of this course you will be able to develop a more advanced, fully functioning app. Currently this course is taught using Swift 2. The team is aware of the release of Swift 3 and will be making edits to the course in time. Please be aware that at this time the instruction is entirely with Swift 2.
The City and You: Find Your Best Place
University of Toronto (via Coursera)
Welcome to The City and You: Find Your Best Place. I'm excited to have you in the class and look forward to your contributions to the other learners in our community. This course will provide the knowledge and the tools needed to understand what cities do, why they matter, the forces shaping the greatest wave of urbanization in history, and how to pick the right place for you. The course will also help you develop critical thinking skills. We'll accomplish this by providing evidence of the importance of cities, and why and how they matter to you. Then we’ll ask you to apply what you’ve learned in an exercise which will help you assess your own community and find your best place. This course is accessible and open to anyone who is interested in learning more about cities and the ways they affect our lives. It is organized around five key modules: (1) Why Cities Matter, (2) A World of Cities, (3) The Creative City, (4) The Divided City and the New Urban Crisis, and (5) How to Find the Best Place for You. After completing the course, you will be able to: (1) Identify why cities are the drivers of economic prosperity; (2) Explain the drivers and implications of fast-growing urbanization worldwide; (3) Outline the key characteristics of a creative and innovative city; (4) Describe the social divides and challenges facing cities and the solutions cities are using to address them; and (5) Recognize the trade-offs of staying in your current city versus moving, and identify the best place for you and your family to live. Of course the world has changed a great deal since I developed this course. The Global COVID-19 Pandemic is affecting cities, businesses and people around the world. It is something I have been writing about and working with mayors, city managers, economic developers, urban leaders and communities across the globe. I have put together a new module on Cities and the Coronavirus to help you better understand the ways in which the Covid-19 crisis is ...
Learn to Program: Crafting Quality Code
University of Toronto (via Coursera)
Not all programs are created equal. In this course, we'll focus on writing quality code that runs correctly and efficiently. We'll design, code and validate our programs and learn how to compare programs that are addressing the same task.
Bioinformatic Methods II
University of Toronto (via Coursera)
Large-scale biology projects such as the sequencing of the human genome and gene expression surveys using RNA-seq, microarrays and other technologies have created a wealth of data for biologists. However, the challenge facing scientists is analyzing and even accessing these data to extract useful information pertaining to the system being studied. This course focuses on employing existing bioinformatic resources – mainly web-based programs and databases – to access the wealth of data to answer questions relevant to the average biologist, and is highly hands-on. Topics covered include multiple sequence alignments, phylogenetics, gene expression data analysis, and protein interaction networks, in two separate parts. The first part, Bioinformatic Methods I, dealt with databases, Blast, multiple sequence alignments, phylogenetics, selection analysis and metagenomics. This, the second part, Bioinformatic Methods II, will cover motif searching, protein-protein interactions, structural bioinformatics, gene expression data analysis, and cis-element predictions. This pair of courses is useful to any student considering graduate school in the biological sciences, as well as students considering molecular medicine. These courses are based on one taught at the University of Toronto to upper-level undergraduates who have some understanding of basic molecular biology. If you're not familiar with this, something like https://learn.saylor.org/course/view.php?id=889 might be helpful. No programming is required for this course although some command line work (though within a web browser) occurs in the 5th module. Bioinformatic Methods II is regularly updated, and was last updated for February 2026.
Stratégies de communication à l’ère du virtuel
University of Toronto (via Coursera)
La communication a changé ! Les règles traditionnelles pour parler et faire des présentations, coordonner les réunions, influencer les gens, négocier et vendre des idées ne s'appliquent plus dans un monde où règnent Skype, Messenger, la vidéoconférence et la téléconférence. Ce cours va vous présenter plusieurs concepts qui pourraient individuellement faire l’objet d’un cours à part entière et notre objectif est de vous donner des outils que vous pouvez mettre en pratique et perfectionner par vous-même. À la fin de ce cours, vous serez en mesure de : • Mettre en application les principes et techniques de communication pour les équipes en personne et virtuelles • Utiliser une approche scientifique pour créer des présentations percutantes • Affiner votre style de communication pour mieux persuader et influencer les autres • Organiser des réunions plus efficaces et percutantes • Incorporer des stratégies pour avoir des conversations positives et difficiles et faire en sorte que les gens se sentent valorisés et écoutés Dans le cadre de ce cours, vous devrez vous enregistrer lorsque vous parlez. Par conséquent, vous devez être équipé d’un téléphone/ordinateur avec une caméra et un microphone fonctionnels. QUI DEVRAIT SUIVRE CE COURS ? Toute personne souhaitant se développer sur le plan professionnel ou souhaitant développer son leadership. Ce cours fait principalement référence à des exemples de l'environnement commercial et professionnel. Si vous cherchez à évoluer au sein de l’organisation dont vous faites actuellement partie ou à améliorer votre valeur personnelle pour des employeurs potentiels, ce cours est fait pour vous. EN QUOI CE COURS EST-IL EFFICACE ? De nombreuses expériences éducatives décrivent et expliquent les choses, mais dans ce cours, nous allons mettre en application et démontrer. Nous enseignons des concepts pratiques et éprouvés, vous montrons comment les appliquer et vous donnons la possibilité de les mettre en pratique da...
The Social Context of Mental Health and Illness
University of Toronto (via Coursera)
Learn how social factors promote mental health, influence the onset and course of mental illness, and affect how mental illnesses are diagnosed and treated. This course explores how our understanding of mental health and illness has been influenced by social attitudes and social developments in North America and around the world. The course begins by situating our contemporary mental health practices in historical context, then looks at different aspects of mental health, mental illness and mental health services and their connections to what’s going on in our social environment.
Managing Your Health: The Role of Physical Therapy and Exercise
University of Toronto (via Coursera)
Managing Your Health: The Role of Physical Therapy and Exercise will introduce learners to the concepts and benefits of physical therapy and exercise. Over six weeks learners will explore: Why physical activity and exercise are important, Exercise and Cardiovascular Disease, Exercise and Osteoporosis, Exercise and Cancer, Common Sports Injuries, Exercise and Arthritis
Introduction to Psychology
University of Toronto (via Coursera)
This course will highlight the most interesting experiments within the field of psychology, discussing the implications of those studies for our understanding of the human mind and human behavior. We will explore the brain and some of the cognitive abilities it supports like memory, learning, attention, perception and consciousness. We will examine human development - both in terms of growing up and growing old - and will discuss the manner in which the behavior of others affect our own thoughts and behavior. Finally we will discuss various forms of mental illness and the treatments that are used to help those who suffer from them. The fact of the matter is that humans routinely do amazing things without appreciating how interesting they are. However, we are also routinely influenced by people and events without always being aware of those influences. By the end of this course you will have gained a much better understanding and appreciation of who you are and how you work. And I can guarantee you that you'll learn things that you'll be telling your friends and family about, things that will fundamentally change the way you think of yourself and others. How can you resist that?!
Spatial Analysis and Satellite Imagery in a GIS
University of Toronto (via Coursera)
In this course, you will learn how to analyze map data using different data types and methods to answer geographic questions. First, you will learn how to filter a data set using different types of queries to find just the data you need to answer a particular question. Then, we will discuss simple yet powerful analysis methods that use vector data to find spatial relationships within and between data sets. In this section, you will also learn about how to use ModelBuilder, a simple but powerful tool for building analysis flowcharts that can then also be run as models. You will then learn how to find, understand, and use remotely sensed data such as satellite imagery, as a rich source of GIS data. You will then learn how to analyze raster data. Finally, you will complete your own project where you get to try out the new skills and tools you have learned about in this course. Note: software is not provided for this course.
Learn Interpersonal Psychotherapy
University of Toronto (via Coursera)
How can psychotherapy help people struggling with depression and stressful interpersonal life events? How do therapists facilitate effective management of interpersonal experiences such as loss/grief, social role transitions, role disputes or interpersonal sensitivity? Interpersonal Psychotherapy (IPT) is a brief, structured, evidence-proven treatment that helps individuals resolve interpersonal issues associated with the onset, worsening, or maintenance of depression. It is recommended by the World Health Organization and expert consensus guidelines as a depression treatment. This case-based, interactive online course provides teaching on the theories, indications and clinical practice guidelines for IPT. The course features interactive learning exercises and video-taped demonstrations of clinical principles-in-practice. By the end of the course, learners will better understand how IPT can help patients address and resolve depression-related interpersonal problems, thereby improving their mood.
Learn to Program: The Fundamentals
University of Toronto (via Coursera)
Behind every mouse click and touch-screen tap, there is a computer program that makes things happen. This course introduces the fundamental building blocks of programming and teaches you how to write fun and useful programs using the Python language.
Introduction To Swift Programming
University of Toronto (via Coursera)
Introduction to Swift Programming is the first course in a four part specialization series that will provide you with the tools and skills necessary to develop an iOS App from scratch. By the end of this first course you will be able to demonstrate intermediate application of programming in Swift, the powerful new programming language for iOS. Guided by best practices you will become proficient with syntax, object oriented principles, memory management, functional concepts and more in programming with Swift. This course is unique in its dedication to teaching Swift programming. With new features and capabilities you will be at the forefront of writing iOS apps. Currently this course is taught using Swift 2. The team is aware of the release of Swift 3 and will be making edits to the course in time. Please be aware that at this time the instruction is entirely with Swift 2. Please note that to take part in this course (and the full specialization) it is required to have a Mac computer and, though not required, ideally an iPhone, iPod, or an iPad. NOTE: This course has been designed and tested (and content delivered) on a Mac. While we are aware of hacks and workarounds for running Mac in a virtual machine on windows we do not recommended a PC. We hope you have fun on this new adventure.
GIS Data Acquisition and Map Design
University of Toronto (via Coursera)
In this course, you will learn how to find GIS data for your own projects, and how to create a well-designed map that effectively communicates your message. The first section focuses on the basic building blocks of GIS data, so that you know what types of GIS files exist, and the implications of choosing one type over another. Next, we'll discuss metadata (which is information about a data set) so you know how to evaluate a data set before you decide to use it, as well as preparing data by merging and clipping files as needed. We'll then talk about how to take non-GIS data, such as a list of addresses, and convert it into "mappable" data using geocoding. Finally, you'll learn about how to take data that you have found and design a map using cartographic principles. In the course project, you will find your own data and create your own quantitative map. Note: software is not provided for this course.
Kommunikationsstrategien für ein virtuelles Zeitalter
University of Toronto (via Coursera)
Kommunikation hat sich geändert! Die traditionellen Regeln für Reden und Präsentieren, Koordination von Meetings, das Beeinflussen von Menschen, das Verhandeln und Verkaufen von Ideen gelten in einer Welt von Skype, Messenger, Video- und Telefonkonferenz nicht mehr. Dieser Kurs bietet einen Überblick über verschiedene Konzepte, von denen jedes ein eigener Kurs sein könnte. Unser Ziel ist es, Ihnen Werkzeuge zur Verfügung zu stellen, die Sie selbst üben und perfektionieren können. Am Ende dieses Kurses werden Sie in der Lage sein: • Kommunikationsprinzipien und -techniken für persönliche und virtuelle Teams anzuwenden • Einen wissenschaftlich fundierten Ansatz für die Erstellung wirkungsvoller Präsentationen zu nutzen • Ihren Kommunikationsstil zu verfeinern, um andere besser zu überzeugen und zu beeinflussen • Effektivere und wirkungsvollere Meetings zu führen • Strategien zu integrieren, um positive, schwierige Gespräche zu führen und den Menschen das Gefühl zu geben, geschätzt und angehört zu werden Dieser Kurs erfordert, dass Sie sich selbst beim Sprechen aufzeichnen. Daher benötigen Sie ein Handy/einen Computer mit einer funktionierenden Kamera und einem Mikrofon. FÜR WEN IST DIESER KURS? Für jeden, der sich beruflich oder als Führungskraft weiterbilden will. Diese Lektionen nutzen hauptsächlich Beispiele aus dem professionellen Geschäftsumfeld. Wenn Sie sich in Ihrer derzeitigen Organisation weiterentwickeln oder Ihren persönlichen Wert für potenzielle Arbeitgeber steigern möchten, ist dieser Kurs genau das Richtige für Sie. WAS MACHT DIESEN KURS EFFEKTIV? Viele pädagogische Erfahrungen beschreiben und erklären, aber in diesem Kurs werden wir anwenden und vorführen. Wir vermitteln praktische und bewährte Konzepte, zeigen Ihnen, wie Sie sie anwenden können, und geben Ihnen die Möglichkeit, sie in einer sicheren und unterstützenden Umgebung zu üben. Dieser Kurs bietet viele Möglichkeiten, die vorgestellten Ideen in die Praxis u...
The 360º Corporation: Tools for Achieving Corporate Purpose
University of Toronto (via Coursera)
If you’ve heard the terms stakeholder capitalism, or sustainability, or ESG, corporate social responsibility, CSR, conscious capitalism, sustainable development goals, shared value, corporate citizenship, or purpose-driven company but don’t know exactly what they mean—or don’t know how to make these ideas come to life—then this course is for you. Every business model and every operating decision has stakeholder trade-offs embedded in it. Profits to the bottom line are not always compatible with the interests of the stakeholders that surround the corporation. Based on Professor Sarah Kaplan’s award-winning course at the University of Toronto’s Rotman School of Management and her book "The 360º Corporation: From Stakeholder Trade-offs to Transformation," this course is designed to help you analyze these trade-offs (Mode 1) and learn how to address them either by rethinking them (Mode 2), innovating around them (Mode 3), or thriving within them (Mode 4). The 360° Corporation is an organization that can productively and effectively manage trade-offs. By taking this course, you will be joining a community of executives around the world who are looking to transform their organizations to meet the needs of the 21st century. The COVID-19 pandemic has gotten us all to rethink our careers and focus on how to make our work as meaningful and rewarding as possible. This course will aid you on that journey by helping you connect social responsibility to your own decisions. And, it will help you reexamine the role of business in society. This is not just a course for people with “social responsibility” in their job title. While it will be useful for anyone whose role is to spread sustainability, gender and racial equity, social responsibility and other stakeholder strategies throughout their organizations, the course is equally important for every executive and line manager because everything you do matters for the social and environmental impact of your products, services...
Mental Health and Resilience for Healthcare Workers
University of Toronto (via Coursera)
This course will help institutions and individuals better manage the mental health challenges of being a healthcare worker. Healthcare providers such as the University Health Network (UHN) address the mental health needs of their staff through several initiatives intended to help build resilience and to provide respite from the demands of their work. This was critical during the pandemic but, of course, healthcare workers encounter high levels of stress even without a pandemic. The primary purpose of this course was to document and explain lessons learned with the hopes of informing healthcare institutions and healthcare workers about effective strategies and why they work. Dr. Heather Gordon will highlight strategies she has employed within the UHN during the pandemic, and Professor Steve Joordens will discuss the psychology underlying these interventions.
Human-Centered Design for Inclusive Innovation
University of Toronto (via Coursera)
This course introduces the principles and practices of human-centered design (also sometimes called “design thinking”) which are essential for developing innovative and inclusive products, services, processes and policies. You will learn by doing, experiencing the design process through exercises and a mini-bootcamp. In this course, you will learn about and experience key human-centered design practices: empathize, reframe, ideate, prototype and test. You will learn why human-centered design is a central component of Gender Analytics. You will develop skills in problem finding (and not just problem solving) by understanding users', stakeholders’ and beneficiaries' lived experiences. You will learn to co-create with diverse stakeholders, develop prototypes, and iterate to develop more innovative solutions. This is the third course of the Gender Analytics Specialization offered by the Institute for Gender and the Economy (GATE) at the University of Toronto's Rotman School of Management. It's great on its own, and you will get even more out of it if you take it as part of the Specialization.
Build Your Own iOS App
University of Toronto (via Coursera)
In the Build Your Own iOS App capstone you will expand your repertoire of additional features and more advanced functions that may be implemented within the iOS environment. You will refine your development skill set and will apply your accumulated skills over the entire specialization series in an applied application development capstone project. This particular course is project based and structured around you building a high quality app as a capstone to the specialization. Currently this course is taught using Swift 2. The team is aware of the release of Swift 3 and will be making edits to the course in time. Please be aware that at this time the instruction is entirely with Swift 2.
Introduction to Self-Driving Cars
University of Toronto (via Coursera)
Welcome to Introduction to Self-Driving Cars, the first course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the terminology, design considerations and safety assessment of self-driving cars. By the end of this course, you will be able to: Understand commonly used hardware used for self-driving cars Identify the main components of the self-driving software stack Program vehicle modelling and control Analyze the safety frameworks and current industry practices for vehicle development For the final project in this course, you will develop control code to navigate a self-driving car around a racetrack in the CARLA simulation environment. You will construct longitudinal and lateral dynamic models for a vehicle and create controllers that regulate speed and path tracking performance using Python. You’ll test the limits of your control design and learn the challenges inherent in driving at the limit of vehicle performance. This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics. To succeed in this course, you should have programming experience in Python 3.0, familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses), Statistics (Gaussian probability distributions), Calculus and Physics (forces, moments, inertia, Newton's Laws). You will also need certain hardware and software specifications in order to effectively run the CARLA simulator: Windows 7 64-bit (or later) or Ubuntu 16.04 (or later), Quad-core Intel or AMD processor (2.5 GHz or faster), NVIDIA GeForce 470 GTX or AMD Radeon 6870 HD series card or higher, 8 GB RAM, and OpenGL 3 or greater (for Linux computers).
Plant Bioinformatics
University of Toronto (via Coursera)
The past 15 years have been exciting ones in plant biology. Hundreds of plant genomes have been sequenced, RNA-seq has enabled transcriptome-wide expression profiling, and a proliferation of "-seq"-based methods has permitted protein-protein and protein-DNA interactions to be determined cheaply and in a high-throughput manner. These data sets in turn allow us to generate hypotheses at the click of a mouse. For instance, knowing where and when a gene is expressed can help us narrow down the phenotypic search space when we don't see a phenotype in a gene mutant under "normal" growth conditions. Coexpression analyses and association networks can provide high-quality candidate genes involved in a biological process of interest. Using Gene Ontology enrichment analysis and pathway visualization tools can help us make sense of our own 'omics experiments and answer the question "what processes/pathways are being perturbed in our mutant of interest?" Structure: each of the 6 week hands-on modules consists of a ~2 minute intro, a ~20 minute theory mini-lecture, a 1.5 hour hands-on lab, an optional ~20 minute lab discussion if experiencing difficulties with lab, and a ~2 minute summary. Tools covered [Material updated in June 2025]: Module 1: GENOMIC DBs / PRECOMPUTED GENE TREES / PROTEIN TOOLS. Araport, TAIR, Gramene, EnsemblPlants Compara, PLAZA, SUBA5 and Cell eFP Browser, 1001 Genomes Browser, PlantConnectome Module 2: EXPRESSION TOOLS. eFP Browser / eFP-Seq Browser, Araport, ARDB, TravaDB, NCBI Genome Data Viewer for exploring RNA-seq data for many plant species, MPSS database for small RNAs, SCEA and Ecker Lab Seed-to-Seed scAtlas Module 3: COEXPRESSION TOOLS. ATTED II, Expression Angler, AraNet, AtCAST2 Module 4: PROMOTER ANALYSIS. Cistome, MEME, ePlant Module 5: GO ENRICHMENT ANALYSIS AND PATHWAY VIZUALIZATION. AgriGO, AmiGO, Classification SuperViewer, TAIR, g:profiler, AraCyc, MapMan (optional: Plant Reactome) Module 6: NETWORK EXPLORATION. Arabidopsis Interactions...
Mind Control: Managing Your Mental Health During COVID-19
University of Toronto (via Coursera)
Never in the history of humanity have so many people been feeling intense anxiety related to COVID-19 and the world it will leave in its wake. The intent of this course is to give you a deeper understanding of the anxiety reaction as it relates to various aspects of our current life, ranging from our consumption of news to the way we talk to our children about this. I will also give you clear strategies for managing and, in fact, turning off the anxiety response at least for short periods. My sincere hope is that you will leave this course with a better understanding of how your brain reacts to crises, along with some powerful tools for managing it before it manages you. In this course we will cover: 1.1 Introduction and Overview 1.2 Understanding the Anxiety Response 1.3 The Necessity of Strategies to Manage Anxiety 1.4 Achieving Relaxation: A Skill We All Need to Learn Now 2.1 Why Watching the News is Addicting and How to Manage Your Consumption 2.2 The Critical Art of Mental Distraction to Crowd Out Stressors 2.3 How We Think About Physical Distancing and Explaining it to Our Children 3.1 The Effects of Isolation 3.2 Some Strategies to Make Isolation More Tolerable 3.3 The Importance of Social Connection in a Physical Distancing World 4.1 The Need to Guard Against Depression: The Importance of Control 4.2 Bring it Together: Practice Makes Proficient 4.3 Invitation to Suggest Additional Videos
GIS, Mapping, and Spatial Analysis Capstone
University of Toronto (via Coursera)
In this capstone course, you will apply everything you have learned by designing and then completing your own GIS project. You will plan out your project by writing a brief proposal that explains what you plan to do and why. You will then find data for a topic and location of your choice, and perform analysis and create maps that allow you to try out different tools and data sets. The results of your work will be assembled into an Esri story map, which is a web site with maps, images, text, and video. The goal is for you to have a finished product that you can share, and that demonstrates what you have learned. Note: software is not provided for this course.
Understanding and Managing the Stresses of Police Work
University of Toronto (via Coursera)
Policing has always been psychological challenging. On any given shift police officers may encounter a range of psychological challenges including domestic violence, interacting with people experiencing mental health issues, violent crime, even attending the aftermath of horrible accidents. The long exhausting shifts can also result in stressful person interactions within one’s personal life. The presence of COVID and political issues related to instances of over-policing have increased these stresses even more. This course has two goals. First, we want to inform officers how their stress system works and why they sometimes feel as they do. With this as a foundation we then describe some strategies officers can use to manage this system, giving themselves much needed breaks from the stress response and overall empowering them with a greater sense of control over how their bodies react to stress.
Bioinformatic Methods I
University of Toronto (via Coursera)
Large-scale biology projects such as the sequencing of the human genome and gene expression surveys using RNA-seq, microarrays and other technologies have created a wealth of data for biologists. However, the challenge facing scientists is analyzing and even accessing these data to extract useful information pertaining to the system being studied. This course focuses on employing existing bioinformatic resources – mainly web-based programs and databases – to access the wealth of data to answer questions relevant to the average biologist, and is highly hands-on. Topics covered include multiple sequence alignments, phylogenetics, gene expression data analysis, and protein interaction networks, in two separate parts. The first part, Bioinformatic Methods I (this one), deals with databases, Blast, multiple sequence alignments, phylogenetics, selection analysis and metagenomics. The second part, Bioinformatic Methods II, covers motif searching, protein-protein interactions, structural bioinformatics, gene expression data analysis, and cis-element predictions. This pair of courses is useful to any student considering graduate school in the biological sciences, as well as students considering molecular medicine. Both provide an overview of the many different bioinformatic tools that are out there. These courses are based on one taught at the University of Toronto to upper-level undergraduates who have some understanding of basic molecular biology. If you're not familiar with this, something like BIO101 from Saylor Academy (https://learn.saylor.org/course/view.php?id=889) might be helpful. No programming is required for this course. Bioinformatic Methods I is regularly updated, and was completely updated for January 2026.
Plant Bioinformatics Capstone
University of Toronto (via Coursera)
The past 15 years have been exciting ones in plant biology. Hundreds of plant genomes have been sequenced, RNA-seq has enabled transcriptome-wide expression profiling, and a proliferation of "-seq"-based methods has permitted protein-protein and protein-DNA interactions to be determined cheaply and in a high-throughput manner. These data sets in turn allow us to generate hypotheses at the click of a mouse or tap of a finger. In Plant Bioinformatics on Coursera.org, we covered 33 plant-specific online tools from genome browsers to transcriptomic data mining to promoter/network analyses and others, and in this Plant Bioinformatics Capstone we'll use these tools to hypothesize a biological role for a gene of unknown function, summarized in a written lab report. This course is part of a Plant Bioinformatics Specialization on Coursera, which introduces core bioinformatic competencies and resources, such as NCBI's Genbank, Blast, multiple sequence alignments, phylogenetics in Bioinformatic Methods I, followed by protein-protein interactions, structural bioinformatics and RNA-seq analysis in Bioinformatic Methods II, in addition to the plant-specific concepts and tools introduced in Plant Bioinformatics and the Plant Bioinformatics Capstone. This course/capstone was developed with funding from the University of Toronto's Faculty of Arts and Science Open Course Initiative Fund (OCIF) and was implemented by Eddi Esteban, Will Heikoop and Nicholas Provart. Asher Pasha programmed a gene ID randomizer.
Aboriginal Worldviews and Education
University of Toronto (via Coursera)
Intended for both Aboriginal and non-Aboriginal learners, this course will explore indigenous ways of knowing and how they can benefit all students. Topics include historical, social, and political issues in Aboriginal education; terminology; cultural, spiritual and philosophical themes in Aboriginal worldviews; and how Aboriginal worldviews can inform professional programs and practices, including but not limited to the field of education.
Motion Planning for Self-Driving Cars
University of Toronto (via Coursera)
Welcome to Motion Planning for Self-Driving Cars, the fourth course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the main planning tasks in autonomous driving, including mission planning, behavior planning and local planning. By the end of this course, you will be able to find the shortest path over a graph or road network using Dijkstra's and the A* algorithm, use finite state machines to select safe behaviors to execute, and design optimal, smooth paths and velocity profiles to navigate safely around obstacles while obeying traffic laws. You'll also build occupancy grid maps of static elements in the environment and learn how to use them for efficient collision checking. This course will give you the ability to construct a full self-driving planning solution, to take you from home to work while behaving like a typical driving and keeping the vehicle safe at all times. For the final project in this course, you will implement a hierarchical motion planner to navigate through a sequence of scenarios in the CARLA simulator, including avoiding a vehicle parked in your lane, following a lead vehicle and safely navigating an intersection. You'll face real-world randomness and need to work to ensure your solution is robust to changes in the environment. This is an intermediate course, intended for learners with some background in robotics, and it builds on the models and controllers devised in Course 1 of this specialization. To succeed in this course, you should have programming experience in Python 3.0, and familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses) and calculus (ordinary differential equations, integration).
Visual Perception for Self-Driving Cars
University of Toronto (via Coursera)
Welcome to Visual Perception for Self-Driving Cars, the third course in University of Toronto’s Self-Driving Cars Specialization. This course will introduce you to the main perception tasks in autonomous driving, static and dynamic object detection, and will survey common computer vision methods for robotic perception. By the end of this course, you will be able to work with the pinhole camera model, perform intrinsic and extrinsic camera calibration, detect, describe and match image features and design your own convolutional neural networks. You'll apply these methods to visual odometry, object detection and tracking, and semantic segmentation for drivable surface estimation. These techniques represent the main building blocks of the perception system for self-driving cars. For the final project in this course, you will develop algorithms that identify bounding boxes for objects in the scene, and define the boundaries of the drivable surface. You'll work with synthetic and real image data, and evaluate your performance on a realistic dataset. This is an advanced course, intended for learners with a background in computer vision and deep learning. To succeed in this course, you should have programming experience in Python 3.0, and familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses).
Gender Analytics Capstone Project
University of Toronto (via Coursera)
It’s time to use your Gender Analytics skills to tackle a real-world challenge in your capstone project. In the final course in the Gender Analytics Specialization offered by the Institute for Gender and the Economy (GATE) at the University of Toronto's Rotman School of Management, you will practice your Gender Analytics skills in an applied learning project that will take you through the entire process for using gender-based insights to generate innovative solutions. You will be expected to think about the problem using an intersectional gender lens, explore both quantitative and qualitative data, apply human-centered design methodologies to developing prototypes, and plan for roll out in an organization, market or community. This activity provides a hands-on learning opportunity to practice critical skills for Gender Analytics.
Data Visualization for Genome Biology
University of Toronto (via Coursera)
The past decade has seen a vast increase in the amount of data available to biologists, driven by the dramatic decrease in cost and concomitant rise in throughput of various next-generation sequencing technologies, such that a project unimaginable 10 years ago was recently proposed, the Earth BioGenomes Project, which aims to sequence the genomes of all eukaryotic species on the planet within the next 10 years. So while data are no longer limiting, accessing and interpreting those data has become a bottleneck. One important aspect of interpreting data is data visualization. This course introduces theoretical topics in data visualization through mini-lectures, and applied aspects in the form of hands-on labs. The labs use both web-based tools and R, so students at all computer skill levels can benefit. Syllabus may be viewed at https://tinyurl.com/DataViz4GenomeBio.
Transformational Leadership for Inclusive Innovation
University of Toronto (via Coursera)
Gender Analytics underpins inclusive innovation, and inclusive innovation will require organizational transformation. The missing link between insights and actions is change leadership. How can you get people to collaborate? How can you overcome resistance to change? How can you embed intersectional, gender-based insights in everything an organization does? In this course, you will learn to be an inspiring and effective change agent by developing a toolbox of leadership skills for building and managing diverse teams. You will hear from experts who have led Gender Analytics in various settings from companies, to non-profits, to the government. In short, this course will help you develop skills to be a transformational leader as you work towards creating inclusive products, services, processes and policies. This is the fourth course of the Gender Analytics Specialization offered by the Institute for Gender and the Economy (GATE) at the University of Toronto's Rotman School of Management. It is great on its own, and you will get even more out of it if you take it as part of the Specialization.
Gender Analytics for Innovation
University of Toronto (via Coursera)
Gender Analytics is a way to analyze your products, services, processes and policies with a gender lens to uncover hidden opportunities for innovation and effectiveness. We'll answer questions such as: Why are women 47% more likely than men to be injured when they get in a car accident? Why do financial products fail to meet women’s needs across their life cycles? Why will automation and AI be more likely to impact women than men? Why are gender-neutral policies are not necessarily gender-equal? This is the introductory course in the 5-course Gender Analytics Specialization offered by the Institute for Gender and the Economy (GATE) at the University of Toronto's Rotman School of Management. In this course, you will build the foundations for conducting Gender Analytics. You will get comfortable with the concepts and terms associated with Gender Analytics, including sex, gender, gender identity, sexual orientation and intersectionality. You will examine how policies, products, services and processes may have unintentionally gendered outcomes that miss out on opportunities or create needless risks. You will learn to uncover assumptions underlying these policies, products, services and processes, and to break social, cultural, and or organizational norms that perpetuate exclusion and inequality. You will see how Gender Analytics can lead to transformational innovations. You will also evaluate your own competencies and start your Self Development Plan and begin your journey to build a workplan for your own Gender Analytics project.
Introduction to GIS Mapping
University of Toronto (via Coursera)
Get started learning about the fascinating and useful world of geographic information systems (GIS)! In this first course of the specialization GIS, Mapping, and Spatial Analysis, you'll learn about what a GIS is, how to get started with the software yourself, how things we find in the real world can be represented on a map, how we record locations using coordinates, and how we can make a two-dimensional map from a three-dimensional Earth. In the course project, you will create your own GIS data by tracing geographic features from a satellite image for a location and theme of your choice. This course will give you a strong foundation in mapping and GIS that will give you the understanding you need to start working with GIS, and to succeed in the other courses in this specialization. This course is for anyone who wants to learn about mapping and GIS. You don't have to have any previous experience - just your curiosity! The course includes both practical software training and explanations of the concepts you need to know to make informed decisions as you start your journey to becoming a GIS analyst. You will need a Windows computer with ArcGIS Desktop installed. (software is not provided)
Inclusive Analytic Techniques
University of Toronto (via Coursera)
Many policies, products, services or processes that we think of as gender-neutral actually have gendered outcomes. Everything from snow plowing to car safety to investment advising to infrastructure investment has impacts that differ by gender. These outcomes can be even more biased if we look at important intersections with race, indigeneity, differences in ability, ethnicity, sexual orientation, and other identities. The question is, what can you do to change this? And, how can you avoid the risks of bias or create innovative new offerings using gender-based insights? Inclusive Analytics Techniques will provide you with the tools and analytical techniques to uncover these intersectional insights. The course covers both quantitative and qualitative data collection and analysis, including basic statistical techniques and practical instructions for working with customers, beneficiaries and other stakeholders. You will learn to incorporate multiple sources of rich evidence in order to develop innovative insights into how policies, products, services and processes can be made more equitable or serve unique communities. This is the second course of the Gender Analytics Specialization offered by the Institute for Gender and the Economy (GATE) at the University of Toronto's Rotman School of Management. It's great on its own, and you will get even more out of it if you take it as part of the Specialization.
State Estimation and Localization for Self-Driving Cars
University of Toronto (via Coursera)
Welcome to State Estimation and Localization for Self-Driving Cars, the second course in University of Toronto’s Self-Driving Cars Specialization. We recommend you take the first course in the Specialization prior to taking this course. This course will introduce you to the different sensors and how we can use them for state estimation and localization in a self-driving car. By the end of this course, you will be able to: Understand the key methods for parameter and state estimation used for autonomous driving, such as the method of least-squares Develop a model for typical vehicle localization sensors, including GPS and IMUs Apply extended and unscented Kalman Filters to a vehicle state estimation problem Understand LIDAR scan matching and the Iterative Closest Point algorithm Apply these tools to fuse multiple sensor streams into a single state estimate for a self-driving car For the final project in this course, you will implement the Error-State Extended Kalman Filter (ES-EKF) to localize a vehicle using data from the CARLA simulator. This is an advanced course, intended for learners with a background in mechanical engineering, computer and electrical engineering, or robotics. To succeed in this course, you should have programming experience in Python 3.0, familiarity with Linear Algebra (matrices, vectors, matrix multiplication, rank, Eigenvalues and vectors and inverses), Statistics (Gaussian probability distributions), Calculus and Physics (forces, moments, inertia, Newton's Laws).
Knowledge and Skills for Dementia Care: the SSLD Approach
University of Toronto (via Coursera)
This course is designed and produced by Professor Ka Tat Tsang of the Factor-Inwentash Faculty of Social Work in collaboration with the Institute for Life Course and Aging at the University of Toronto. This course aims to inform learners about dementia and dementia care from an SSLD perspective, including, community care, in-home support, and long-term care. This course will cover the continuum of senior services and support across different settings, including, private caregiving, community services, and institutionalized residential care. Course components are designed to equip learners with practical knowledge regarding dementia and dementia care. This course also features top-notch researchers and practitioners who will be sharing their expertise and experience on recent research developments about dementia and other related topics, including, advance care planning, elder abuse, management of behavioural and psychological symptoms associated with dementia, sexuality and intimacy, consent and capacity, legal issues, principles of designed space and aging-in-place, substance use and addiction in older adults with dementia, senior care models, etc. Upon the completion of this course, learners will possess a holistic understanding of the needs and characteristics of older adults living with dementia, and will also be equipped with the knowledge and skills needed to enhance their competency in providing care.
The Arts and Science of Relationships: Understanding Human Needs
University of Toronto (via Coursera)
This course provides an introduction to: 1. Basic concepts of The Strategies and Skills Learning and Development System (SSLD), their relevance for every day relationships and provide advanced concepts for participants who work in fields of social work and health care . 2. Basic practice principles and methods of SSLD, illustrated by relationship management case studies. 3. The SSLD framework for relationship management assessment; N3C (needs, circumstances, characteristics, capacity) and problem translation. 4. Core competencies in the relationship management application of the SSLD system: Observation learning, simulation, real life implementation, review and monitoring.
iOS App Development Basics
University of Toronto (via Coursera)
iOS App Development Basics, the second course in the iOS App Development with Swift specialization, expands your programming skills and applies them to authentic app development projects. The topics covered in this course include Xcode basics, Core iOS and Cocoa Touch frameworks, simple user interface creation, MVC Architecture and much more. With a focus on using Apple’s components to access sensors like camera, microphone and GPS, by the end of this course you will be able to create a basic App according to specified parameters and guidelines. Currently this course is taught using Swift 2. The team is aware of the release of Swift 3 and will be making edits to the course in time. Please be aware that at this time the instruction is entirely with Swift 2.
Стратегии коммуникации в виртуальную эпоху
University of Toronto (via Coursera)
Коммуникация изменилась! Традиционные правила выступления и презентации, координации совещаний, влияния на людей, переговоров и продаж больше не применимы в мире Skype, мессенджеров, видео и телеконференций. В этом курсе представлен обзор нескольких концепций, каждая из которых может быть темой отдельного курса, и наша цель в том, чтобы дать вам инструменты, которые вы можете использовать и совершенствовать самостоятельно. По окончании этого курса вы сможете: • применять принципы и методы коммуникации с командами как лично, так и виртуально; • использовать научно обоснованный подход для создания эффективных презентаций; • улучшить свой стиль коммуникации, чтобы более эффективно убеждать и влиять на других; • проводить более эффективные и результативные встречи; • внедрить стратегии, чтобы проводить сложные разговоры и получать позитивные результаты, чтобы ваши собеседники чувствовали, что их ценят и слушают На этом курсе потребуется сделать запись вашего выступления. Поэтому у вам будет нужен телефон/компьютер с функциональной камерой и микрофоном. КОМУ НУЖНО ПРОЙТИ ЭТОТ КУРС? Всем, кто хочет добиться профессионального и / или лидерского роста. В этом курсе используются примеры в основном из профессиональной деловой среды. Если вы хотите продвинуться по карьерной лестнице в своей организации или повысить личную ценность для потенциальных работодателей – этот курс для вас. ЧТО ДЕЛАЕТ ЭТОТ КУРС ЭФФЕКТИВНЫМ? Многие образовательные курсы дают описания и объяснения, но в этом курсе мы будем применять и демонстрировать. Мы обучаем практическим и проверенным концепциям, демонстрируем, как их применять и даем вам возможность попрактиковаться в безопасной и благоприятной среде. В этом курсе будет множество возможностей применить представленные идеи на практике и самим проверить их эффективность. ПОЧЕМУ ВАМ НУЖНО ПРОЙТИ ЭТОТ КУРС? Мы бросим вызов существующим идеям о том, что значит быть частью виртуальной команды, и поддержим вас в том, чтобы стать активн...
Communication Strategies for a Virtual Age
University of Toronto (via Coursera)
Communication has changed! The traditional rules for speaking and presenting, meeting coordination, influencing people, negotiating and selling ideas no longer apply in a world of skype, messenger, video and teleconference. This course will act as an overview on several concepts each of which could be a course of their own and our goal is to give you tools that you can practice and perfect on your own. By the end of this course, you will be able to: • Apply communication principles and techniques for in-person and virtual teams • Use a science based approach to create impactful presentations • Refine your communication style to better persuade and influence others • Run more effective and impactful meetings • Incorporate strategies to have positive difficult conversations and make people feel valued and listened to This course will require you to record yourself speaking. Therefore you must have a phone/computer with a functional camera and microphone. WHO SHOULD TAKE THIS COURSE? Anyone looking for professional and/or leadership development. This class mainly uses examples from the professional, business environment. If you are looking to advance at your current organization or to enhancing your personal value for potential employers this course is for you. WHAT MAKES THIS COURSE EFFECTIVE? Many educational experiences describe and explain, but in this course we will apply and demonstrate. We teach practical and proven concepts, show you how to apply them and give you opportunities to practice them in a safe and supportive environment. This course is full of opportunities to put the ideas presented into practice and test their effectiveness for yourself. WHY SHOULD YOU TAKE THIS COURSE? We will challenge the preconceived ideas about what it means to be part of a virtual team, and support you to be a dynamic team contributor no matter where you work. In this course you can expect to be both energized and uncomfortable – like in most exp...