Harvard University · edX
Fundamentals of TinyML
4.567 ratingsFocusing on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the “language” of TinyML.

Welcome to the Evidence-based Toxicology (EBT) course. In medicine and healthcare, evidence-based medicine has revolutionized the way that information is evaluated transparently and objectively. Over the past ten years, a movement in North America and Europe has attempted to translate this revolution to the field of toxicology.
The Center for Alternatives to Animal Testing (CAAT) within the department of Environmental Health and Engineering at the Johns Hopkins Bloomberg School of Public Health hosts the first chair for EBT and the secretariat for the EBT Collaboration on both sides of the Atlantic. Based on the Cochrane Collaboration in Evidence-based Medicine, the EBT Collaboration was established at the CAAT to foster the development of a process for quality assurance of new toxicity tests for the assessment of safety in humans and the environment.
Regulatory safety sciences have undergone remarkably little change in the past fifty years. At the same time, our knowledge in the life sciences is doubling about every seven years. Systematic review and related evidence-based approaches are beginning to be adapted by regulatory agencies like the Environment Protection Agency (EPA), the European Food Safety Authority (EFSA), and the US National Toxicology Program. They provide transparent, objective, and consistent tools to identify, select, appraise, and extract evidence across studies.
This course will showcase these emerging efforts and address opportunities and challenges to the expanded use of these tools within toxicology.
This module introduces you to the course, outlines the shortcomings of current toxicity testing approaches, and shows how EBT can help to overcome these shortcomings.
This module explains how evidence-based toxicology originated and describes the driving forces for the initiative. In the second lesson, you will learn how to distinguish between correlation and causation as well as the main problems with drawing conclusions on the basis of correlations. The Bradfo…
This module shows how to perform systematic reviews and meta-analyses. You will learn the history of both methods and will receive step-by-step instructions on how to perform systematic reviews and meta-analyses using examples from the research activities of the instructors.
This module teaches you about possible biases that can be introduced at different stages of research. Each bias is explained with examples, including solutions for overcoming those biases. The second lesson covers systematic review of the zebrafish embryotoxicity test as a case study conducted by t…
Quality control is a very important aspect of not only modern toxicology but the entirety of life sciences. The first lesson in this module demonstrates the importance of performing quality control on your experiments. The second lesson is connected with the first one because validation of an alter…
Evidence-based toxicology requires some knowledge of bioinformatics. The first lesson in the module teaches you some biostatistical tools you can apply when analyzing predectivity, specificity, and sensitivity of a method. You will also learn how to identify biases in a study with the help of bioin…
The final week of the course is devoted to completing the Systematic Review Assignment. You will use SysRev to review at least 20 abstracts, apply inclusion and exclusion criteria, render decisions, and resolve conflicts with other reviewers.
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Johns Hopkins University is in Tier 1: world-leading universities and India's top institutes of our institution ranking (93/100).
Harvard University · edX
Focusing on the basics of machine learning and embedded systems, such as smartphones, this course will introduce you to the “language” of TinyML.
Harvard University · edX
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Harvard University · edX
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Harvard University · edX
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Massachusetts Institute of Technology · YouTube
Instructors: David Sontag, Peter Szolovits View the complete course: https://ocw.mit.edu/6-S897S19 Introduces students to machine learning in healthcare, the nature of clinical data, and the use of machine learning for risk stratification, disease progression modeling, precision medicine, diagnosis…
Harvard University · edX
An introduction to programming using Python, a popular language for general-purpose programming, data science, web programming, and more.