Tools to Analyze Algorithms (Part 1) - Columbia University

Course link: https://www.edx.org/course/machine-learning-for-data-science-and-analytics Dear Students, I want to point out some helpful resources that can assist you in your learning for machine learning. Below are some of the website to search for helpful tutorials, tips, or anything problems in programming: 1. https://medium.com (tutorials) 2. https://github.com/ (sample codes) 3. https://www.reddit.com/r/MachineLearning/ 4. https://stackoverflow.com/ (programmming help) These are some of the thing or places that I often go to. But, there are tons of other material avaliable to you on the internet. Have fun exploring machine leanring! Welcome to Machine Learning for Data Science and Analytics course! This is the second course of the Data Science and Analytics in Context XSeries. The course is centered around Algorithms, Machine Learning along with Applications. In the first part of this course, you will learn about algorithmic techniques including sorting, searching, greedy algorithms and how machine learning uses algorithms to search for patterns in data. You will also learn about practical applications of algorithms and discuss hashing and search trees, data structures for representing sets of objects that support the basic operations of insertion, deletion, lookup (search), and other queries. Dynamic Programming, one of the main algorithmic design methods and Linear Programming are also discussed which allows for the modeling and efficient solution of optimization problems from many areas. The module concludes with NP-completeness, a theory that relates and explains the intrinsic complexity of many problems from different fields for which no efficient algorithms are known. These modules on algorithms will conclude with the applications of algorithms to genomics. We introduce the context of personal genomics, handling the raw genomic data, billions of short snippets of DNA that are collected in parallel and need to be assembled to reveal the content of a personal genome. Next, we address the analysis of genomes in cohorts and review case studies, from a disease perspective and from a personal perspective. Following this first part on algorithms, the second part of course 2 will address Machine Learning. Although the subject can get very technical, a few key ideas help to understand what problems may or may not be solvable using machine learning. Machine learning methods are computer algorithms that search for patterns in data sets, and use these patterns to make decisions or predictions. The modules cover a basic introduction to machine learning and explain how it is related to statistics and data analysis. During two modules, we illustrate the basic principles of machine learning with elementary but widely used methods. In particular, we discuss classification, including linear classifiers and random forests, model selection, and cross validation. The Machine Learning part will also feature probabilistic topic models that enable to uncover hidden thematic structure in large collections of documents. This is a useful technique to explore corpora, summarize texts, and form predictions. The course will conclude with a case study pertaining to the prediction of preterm birth. The application illustrates the basic concepts of machine learning and its methods. Through this challenging and complex real world problem, we show how the problem of preterm birth pushes the boundary of state-of-the-art machine learning methodologies. Support me financially on Patreon : https://www.patreon.com/Mohamed_Elbana if you need any help, contact me at the following email. : galaxyofscience2@gmail.com If you want to learn anything in the best way at all, and from the top universities in the world. following websites EDX : https://www.edx.org/ Udacity : https://www.udacity.com/ Coursera : https://www.coursera.org/ Udemy : https://www.udemy.com/ google digital garage : https://learndigital.withgoogle.com/digitalgarage skill share : https://www.skillshare.com/ future learn : https://www.futurelearn.com/ Alison : https://alison.com/ Lynda : https://www.lynda.com/ Evanto Tuts+ : https://tutsplus.com/ open culture : http://www.openculture.com/ TED : https://www.ted.com/ Open learn : https://www.open.edu/openlearn/ Linked in learning : https://bit.ly/3fDIy00 or shorturl.at/cLM25 University of the people : https://www.uopeople.edu/ Facebook blueprint : https://www.facebookblueprint.com/student/catalog Saylor : https://www.saylor.org/ Microsoft Learn : https://docs.microsoft.com/en-us/learn/browse/ GitHub Learning Lab : https://lab.github.com/ khan academy : https://www.khanacademy.org/ Zad academy : https://www.zad-academy.com/ My blogger: http://www.elbana1.com/ Channel page on Facebook : https://www.facebook.com/Mohamed.Elbana0 #Mohamed_Elbana TO learn more about the CS50 offered by Harvard from the following link : https://bit.ly/3fJIfRd or shorturl.at/iEJTX

Видео Tools to Analyze Algorithms (Part 1) - Columbia University автора Мир Чисел и Данных