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Fundamentals of Machine Learning for Healthcare
by Matthew LungrenTop Instructor , Serena YeungTop Instructor- 4.8
Approx. 12 hours to complete
Machine learning and artificial intelligence hold the potential to transform healthcare and open up a world of incredible promise. This course will introduce the fundamental concepts and principles of machine learning as it applies to medicine and healthcare. Why machine learning in healthcare? Supervised Machine Learning Approaches: Regression and the "No Free Lunch" Theorem...
Machine Learning A-Z: Logistic Regression Using Python ©
by AI Sciences- 4.1
8.5 hours on-demand video
This comprehensive course will be your guide to learning how to use the power of Python to train your machine such that your machine starts learning just like human and based on that learning, your machine starts making predictions as well! This course is for you if you want to learn how to program in Python for Machine Learning...
$12.99
Machine Learning Under the Hood: The Technical Tips, Tricks, and Pitfalls
by Eric Siegel- 4.8
Approx. 17 hours to complete
This course will show you how machine learning works. And this course continues beyond machine learning standards to also cover cutting-edge, advanced methods, as well as preparing you to circumvent prevalent pitfalls that seldom receive the attention they deserve. This course includes illuminating software demos of machine learning in action using SAS products....
Apply Creative Machine Learning
by Mick Grierson , Louis McCallum , Rebecca Fiebrink- 0.0
4
Discover the creative side of machine learning with this free course using hands-on examples. Basic JavaScript (via a provided crash course as additional content)What machine learning isWhat creative machine learning isHow to create simple machine learning systems in javascriptHow to better understand core concepts in machine learningUnderstanding classificationUnderstanding regression...
Free
Data for Machine Learning
by Anna Koop- 4.4
Approx. 12 hours to complete
This course is all about data and how it is critical to the success of your applied machine learning model. This is the third course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute. Machine Learning Process Lifecycle Review Preparing your Data for Machine Learning Success...
Machine Learning with Python: from Linear Models to Deep Learning
by Regina Barzilay , Tommi Jaakkola , Karene Chu- 0.0
15 Weeks
An in-depth introduction to the field of machine learning, from linear models to deep learning and reinforcement learning, through hands-on Python projects. Understand principles behind machine learning problems such as classification, regression, clustering, and reinforcement learning Implement and organize machine learning projects, from training, validation, parameter tuning, to feature engineering....
$300
Machine Learning - A-Z™ Full Course
by Programming Academy- Learn and Create- 4.6
14.5 hours on-demand video
Learn Machine Learning Algorithms in Python and R, Build Real Life Projects Like Speech and Face Recognition. 23% of North American enterprises have machine learning embedded in at least one company function as of last year. Scopes of Machine Learning You will Get MIT press Notes on Machine learning and yes some of E- Books...
$12.99
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The Art & Science of Product Management
by Nancy Wang- 4.6
Approx. 11 hours to complete
MIT Sloan Review: The Experience Disruptors PMing with AI and Machine Learning Please Review Our Course & Specialization!...
Doing Clinical Research: Biostatistics with the Wolfram Language
by Juan H Klopper- 4.7
Approx. 15 hours to complete
This course follows the modern trend of learning statistical analysis through the use of a computer language. If you wanting even more, there are optional Honors lessons on machine learning that cover the support in the Wolfram Language for deep learning. 09: Introduction to Wolfram Language machine learning 10: Automated Machine Learning...
Data Science in Stratified Healthcare and Precision Medicine
by Dr Areti Manataki , Dr Frances Wong- 4.6
Approx. 17 hours to complete
Topics include: (i) Sequence Processing, (ii) Image Analysis, (iii) Network Modelling, (iv) Probabilistic Modelling, (v) Machine Learning, (vi) Natural Language Processing, (vii) Process Modelling and (viii) Graph Data. Watch the course promo video here: http://edin. Welcome to the Course About the Course Course Logistics Course Team Supervised Learning Unsupervised Learning Course Conclusion...