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Advanced Bioconductor
by Rafael Irizarry , Michael Love- 0.0
4 Weeks
Learn advanced approaches to genomic visualization, reproducible analysis, data architecture, and exploration of cloud-scale consortium-generated genomic data. In this course, we begin with approaches to visualization of genome-scale data, and provide tools to build interactive graphical interfaces to speed discovery and interpretation. 2x: Introduction to Linear Models and Matrix Algebra 5x: Introduction to Bioconductor...
$149
Statistical Learning
by Trevor Hastie , Robert Tibshirani- 0.0
9 Weeks
Learn some of the main tools used in statistical modeling and data science. We focus on what we consider to be the important elements of modern data analysis. The pdf for this book is available for free on the book website. Overview of statistical learning Linear regression Linear model selection and regularization...
$149
Unsupervised Learning with Python: Step-by-Step Tutorial!
by Packt Publishing- 2.8
7.5 hours on-demand video
By the end of the course, you’ll apply clustering and dimensionality reduction in Machine Learning using Python as well as Master Unsupervised Learning to solve real-world problems! This course explains the most important Unsupervised Learning algorithms using real-world examples of business applications in Python code. The Linear Algebra Behind PCA The Linear Algebra Behind PCA (Continued)...
$12.99
Digital Systems: From Logic Gates to Processors
by Elena Valderrama , Jean-Pierre Deschamps , Lluis Terés , Merce Rullan , Joaquín Saiz Alcaine , David Bañeres , Juan Antonio Martínez- 4.4
Approx. 29 hours to complete
This course gives you a complete insight into the modern design of digital systems fundamentals from an eminently practical point of view. PDF files Arithmetic components + Introduction to VHDL 2 (1outof2): Introduction to VHDL - Lexicon, syntax and structure 2 (2outof2): Introduction to VHDL - Lexicon, syntax and structure 3 (1outof2): Introduction to VHDL - Sequential sentences...
Computer Vision Basics
by Radhakrishna Dasari , Junsong Yuan- 4.2
Approx. 13 hours to complete
By the end of this course, learners will understand what computer vision is, as well as its mission of making computers see and interpret the world as humans do, by learning core concepts of the field and receiving an introduction to human vision capabilities. Related Fields of Computer Vision Linear Algebra...
Case Studies in Functional Genomics
by Rafael Irizarry , Michael Love , Vincent Carey- 0.0
5 Weeks
Given the diversity in educational background of our students we have divided the series into seven parts. If you are a statistician you should consider skipping the first two or three courses, similarly, if you are biologists you should consider skipping some of the introductory biology lectures. 2x: Introduction to Linear Models and Matrix Algebra...
$149
Applied AI with DeepLearning
by Romeo Kienzler , Niketan Pansare , Tom Hanlon , Max Pumperla , Ilja Rasin- 4.4
Approx. 24 hours to complete
We’ll learn about the fundamentals of Linear Algebra and Neural Networks. If you’re already an expert, this peep under the mental hood will give your ideas for turbocharging successful creation and deployment of DeepLearning models. Also some basic understanding of math (linear algebra) is a plus, but we will cover that part in the first week as well. Linear algebra...
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Digital Signal Processing 1: Basic Concepts and Algorithms
by Paolo Prandoni , Martin Vetterli- 4.6
Approx. 29 hours to complete
To make the best of this class, it is recommended that you are proficient in basic calculus and linear algebra; several programming examples will be provided in the form of Python notebooks but you can use your favorite programming language to test the algorithms described in the course. Signal of the Day: The first man-made signal from outer space...
Differential Equations for Engineers
by Jeffrey R. Chasnov- 4.9
Approx. 27 hours to complete
Change of Variables Transforms a Nonlinear to a Linear Equation Week Four Introduction Definition of the Laplace Transform | Lecture 29 Laplace Transform of a Constant Coefficient ODE | Lecture 30 Solution of an Initial Value Problem | Lecture 31 Solution of a Discontinuous Inhomogeneous Term | Lecture 34 Systems of Homogeneous Linear First-order ODEs | Lecture 39...
Introduction to Self-Driving Cars
by Steven Waslander , Jonathan Kelly- 4.7
Approx. 35 hours to complete
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. 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)....