Digital Analytics Fundamentals - - Unit 2 - Getting started with digital analytics Analytics Academy Login Unit 2 - Getting started with digital analytics The importance of digital analytics Text Version Lesson objectives Understand what "digital measurement" meansRecognize why digital analytics tools like Google Analytics are important to implement for your business Additional resources Activity 2.1 - The importance of digital analytics Complete the definition of "digital analytics" by choosing the best option to fill in the blanks for the statement below. Previous Page Next Page Advice on Learning Deep Learning ( Neural Networks) manu prakash wrote: I think of starting with a small project like Digit recognition, and learn the techniques needed to complete that small project. You could use the UFLDL Tutorial by Andrew Ng as a starting point. He even uses MATLAB and a digit recognition task to teach you the main ideas of Unsupervised Feature Learning and Deep Learning. With this you could delve into the digit recognition competition. However, my personal advise is to get familiar with machine learning / statistical learning and programming first, get your feet wet. Shreekanth wrote: If you are interested in learning about handling big data and you have already have a good grasp of Machine Learning/Statistical Learning then you should take a look at Hadoop and NoSQL databases. Week 1:MapReduceRageRank I hope that helps
Computational Neuroscience About the Course This course provides an introduction to basic computational methods for understanding what nervous systems do and for determining how they function. We will explore the computational principles governing various aspects of vision, sensory-motor control, learning, and memory. Specific topics that will be covered include representation of information by spiking neurons, processing of information in neural networks, and algorithms for adaptation and learning. We will make use of Matlab demonstrations and exercises to gain a deeper understanding of concepts and methods introduced in the course. The course is primarily aimed at third- or fourth-year undergraduates and beginning graduate students, as well as professionals and distance learners interested in learning how the brain processes information. Course Syllabus Topics covered include: 1. Recommended Background Familiarity with basic concepts in linear algebra, calculus, and probability theory. In-course Textbooks
Best Machine Learning Resources for Getting Started This was a really hard post to write because I want it to be really valuable. I sat down with a blank page and asked the really hard question of what are the very best libraries, courses, papers and books I would recommend to an absolute beginner in the field of Machine Learning. I really agonised over what to include and what to exclude. I had to work hard to put my self in the shoes of a programmer and beginner at machine learning and think about what resources would best benefit them. I picked the best for each type of resource. If you are a true beginner and excited to get started in the field of machine learning, I hope you find something useful. Programming Libraries I am an advocate of “learn just enough to be dangerous and start trying things”. This is how I learned to program and I’m sure many other people learned that way too. Find a library and read the documentation, follow the tutorials and start trying things out. Video Courses Overview Papers Beginner Machine Learning Books
ranzato-06.pdf Data Wrangling with MongoDB Class Summary In this course, we will explore how to wrangle data from diverse sources and shape it to enable data-driven applications. Some data scientists spend the bulk of their time doing this! Students will learn how to gather and extract data from widely used data formats. This is a great course for those interested in entry-level data science positions as well as current business/data analysts looking to add big data to their repertoire, and managers working with data professionals or looking to leverage big data. What Will I Learn? At the end of the class, students should be able to: Programmatically extract data stored in common formats such as csv, Microsoft Excel, JSON, XML and scrape web sites to parse data from HTML.Audit data for quality (validity, accuracy, completeness, consistency, and uniformity) and critically assess options for cleaning data in different contexts. What Should I Know? The ideal student should have the following skills: Syllabus Lesson 3: Data Quality
Where are the Deep Learning Courses? — Data Community DC This is a guest post by John Kaufhold. Dr. Kaufhold is a data scientist and managing partner of Deep Learning Analytics, a data science company based in Arlington, VA. He presented an introduction to Deep Learning at the March Data Science DC. Why aren't there more Deep Learning talks, tutorials, or workshops in DC2? It's been about two months since my Deep Learning talk at Artisphere for DC2. First some preemptive answers to the “FAQ” downstream of the talk: Mary Galvin wrote a blog review of this event.Yes, the slides are available.Yes, corresponding audio is also available (thanks Geoff Moes).A recently "reconstructed" talk combining the slides and audio is also now available! There actually was a class... Aaron Schumacher and Tommy Shen invited me to come talk in April for General Assemb.ly's Data Science course. Resources to learn Deep Learning This is the first reason I don't think it's all that valuable for DC to have more of its own Deep Learning “academic” tutorials.
Logic 101 Logic 101 These lectures cover introductory sentential logic, a method used to draw inferences based off of an argument's premises. Logic is ubiquitous--individuals thinking of pursuing a career in law, computer science, mathematics, or social science must have a firm understanding of basic logic to succeed. Even someone who occasionally programs in Microsoft Excel would benefit greatly. Lectures Prerequisites Logic 101 is the ground floor--there are no prerequisites other than being willing to think through problems. Syllabus This class will cover eight topics: Simple Sentences and OperationsTruth TablesReplacement RulesRules of InferenceProofsConditional ProofsProof by ContradictionFormal Fallacies Additional information Teacher qualifications I am a PhD Candidate at the University of Rochester.
Une introduction aux arbres de décision Les arbres de décision sont l’une des structures de données majeures de l’apprentissage statistique. Leur fonctionnement repose sur des heuristiques qui, tout en satisfaisant l’intuition, donnent des résultats remarquables en pratique (notamment lorsqu’ils sont utilisés en « forêts aléatoires »). Leur structure arborescente les rend également lisibles par un être humain, contrairement à d’autres approches où le prédicteur construit est une « boîte noire ». L’introduction que nous proposons ici décrit les bases de leur fonctionnement tout en apportant quelques justifications théoriques. Suivez le lien pour la version PDF. Table des matières Un arbre de décision modélise une hiérarchie de tests sur les valeurs d’un ensemble de variables appelées attributs. Un ensemble de valeurs pour les différents attributs est appelé une « instance », que l’on note généralement (x, y) où y est la valeur de l’attribut que l’on souhaite prédire et x = x1, …, xm désignent les valeurs des m autres attributs.
Deep Learning Tutorials — DeepLearning 0.1 documentation Deep Learning is a new area of Machine Learning research, which has been introduced with the objective of moving Machine Learning closer to one of its original goals: Artificial Intelligence. See these course notes for a brief introduction to Machine Learning for AI and an introduction to Deep Learning algorithms. Deep Learning is about learning multiple levels of representation and abstraction that help to make sense of data such as images, sound, and text. For more about deep learning algorithms, see for example: The tutorials presented here will introduce you to some of the most important deep learning algorithms and will also show you how to run them using Theano. The algorithm tutorials have some prerequisites. The code is available on the Deep Learning Tutorial repositories. The purely supervised learning algorithms are meant to be read in order: Building towards including the mcRBM model, we have a new tutorial on sampling from energy models: LSTM network for sentiment analysis:
Intro to Hadoop and MapReduce Intro to Hadoop and MapReduce How to Process Big Data Intermediate Approx. 1 month Assumes 6hr/wk (work at your own pace) Built by Join 85,474 Students Enroll in Course $199 /month after 14-day trial Best for learners serious about course completion & career advancement You get Instructor videos See All Instructor videos Learn by doing exercises Projects with reviews Stuck? Verified Certificate Instructor videos Watch concise videos that teach you not just how, but why. Learn by doing exercises Learn by doing exercises and quizzes in between instructional videos. Projects with feedback Showcase your skills with a project: . Stuck? Get unstuck with support from Udacity Coaches in the forums and scheduled, live sessions. Verified Certificate Stand out to employers with a certificate that counts. Access Course Materials Free Learn by doing exercises and view project instructions Projects with reviews Stuck? Verified Certificate These features are available when you enroll View Trailer Course Summary Projects Syllabus