Established in 1827, the University of Toronto has one of the strongest research and teaching faculties in North America, presenting top students at all levels with an intellectual environment unmatched in depth and breadth on any other Canadian campus.
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeBehind 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.
- This course aims to provide knowledge and skills for patients on the topic of biosimilars, a class of biologic drugs or medical therapies that are highly similar to an existing originator medication that is off patent.- Biosimilars present a rapidly growing area in pharmaceutical development, treatment options, and patient care that require an evidence-informed implementation approach.- As awareness and use of biosimilars increases globally, this course will provide information and guidance to support patient understanding of the effective and safe use of biosimilars in clinical settings.- The course will focus on enhancing knowledge and skills for patients and caregivers related to biosimilars.- The course will utilize a modular approach to provide content that is accessible and informative to all learners, and provide an introduction to biosimilar medications including what they are, regulatory processes, access, and discussing biosimilars with your healthcare provider.
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeWelcome 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 developmentFor 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).
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeIn 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.
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeIn 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.
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeCommunication 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 experiences that result in positive growth and change!This course is offered through the University of Toronto School of Continuing Studies (https://learn.utoronto.ca/).
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeThe wind is a part of our everyday experience. Anyone who has lived on a coast or even vacationed there is familiar with waves and tides. That these naturally-occurring fluid flows contain energy is intuitively recognizable and indeed technologies to harvest wind and tidal energy were in use centuries ago. Our current quest for sustainable energy sources has led us to refocus once again on the potential of wind, waves and tides. This course will explain and describe the technologies to extract energy from each. The strengths and weaknesses of these various technologies will be discussed and illustrated through case studies of specific installations.
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeWelcome 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).
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeMaster Swift, design elegant interactions, and create a fully functioning iOS app.This Specialization covers the fundamentals of iOS application development in the Swift programming language. You’ll learn to use development tools such as XCode, design interfaces and interactions and evaluate their usability, and integrate camera, photo, and location information to enhance your app. In the final Capstone Project, you’ll apply your skills to create a fully-functioning photo editing app for iPhone, iPad, and Apple Watch. A Mac computer is required for success in this course.
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeLarge-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/bio101 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 2022.
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeNever 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:1.1Introduction and Overview1.2 Understanding the Anxiety Response1.3 The Necessity of Strategies to Manage Anxiety1.4 Achieving Relaxation: A Skill We All Need to Learn Now2.1 Why Watching the News is Addicting and How to Manage Your Consumption2.2 The Critical Art of Mental Distraction to Crowd Out Stressors2.3 How We Think About Physical Distancing and Explaining it to Our Children3.1 The Effects of Isolation3.2 Some Strategies to Make Isolation More Tolerable3.3 The Importance of Social Connection in a Physical Distancing World4.1 The Need to Guard Against Depression: The Importance of Control4.2 Bring it Together: Practice Makes Proficient4.3 Invitation to Suggest Additional Videos
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreeWelcome 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).
Class Central TipsLearn How to Sign up to Coursera courses for free1600+ Coursera Courses That Are Still Completely FreePolicing 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.
INQ101x is designed with K-12 teachers in mind. Teacher candidates, higher education instructors, and other educators may also find it relevant. In six weeks, we discuss some of the major themes and challenges of integrating inquiry and technology as a community of practitioners. We collect and share resources and exchange ideas about what works for specific topics and age groups.
This global health and life sciences course enables learners to investigate health problems affecting large populations – the whole world in fact! By understanding the big numbers in global mortality and their causes and distributions you will learn how to think numerically about global health. We will use real data from real people to ask the questions: What are the major causes of death in the world? Why do we need cause of death statistics? How does counting the dead help the living? We begin with a historical perspective on global mortality and end with a hopeful look toward future trends. In between, you will learn about how death prior to old age can be avoided, worldwide mortality rates, and specific diseases such as HIV, malaria, childhood conditions, chronic diseases, and risk factors such as smoking. This course will help you use population statistics to understand how rapid gains in health are possible.