AI Nanodegree Program Syllabus: Term 2 (Deep Learning), In Depth

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Here at Udacity, we are tremendously excited to announce the kick-off of the second term of our Artificial Intelligence Nanodegree program. Because we are able to provide a depth of education that is commensurate with university education; because we are bridging the gap between universities and industry by providing you with hands-on projects and partnering with the top industries in the field; and last but certainly not least, because we are able to bring this education to many more people across the globe, at a cost that makes a top-notch AI education realistic for all aspiring learners. During the first term, you've enjoyed learning about Game Playing Agents, Simulated Annealing, Constraint Satisfaction, Logic and Planning, and Probabilistic AI from some of the biggest names in the field: Sebastian Thrun, Peter Norvig, and Thad Starner. Term 2 will be focused on one of the cutting-edge advancements of AI -- Deep Learning. In this Term, you will learn about the foundations of neural networks, understand how to train these neural networks with techniques such as gradient descent and backpropagation, and learn different types of architectures that make neural networks work for a variety of different applications.


Data Mining with Rattle Udemy

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Data Mining with Rattle is a unique course that instructs with respect to both the concepts of data mining, as well as to the "hands-on" use of a popular, contemporary data mining software tool, "Data Miner," also known as the'Rattle' package in R software. Rattle is a popular GUI-based software tool which'fits on top of' R software. The course focuses on life-cycle issues, processes, and tasks related to supporting a'cradle-to-grave' data mining project. These include: data exploration and visualization; testing data for random variable family characteristics and distributional assumptions; transforming data by scale or by data type; performing cluster analyses; creating, analyzing and interpreting association rules; and creating and evaluating predictive models that may utilize: regression; generalized linear modeling (GLMs); decision trees; recursive partitioning; random forests; boosting; and/or support vector machine (SVM) paradigms. It is both a conceptual and a practical course as it teaches and instructs about data mining, and provides ample demonstrations of conducting data mining tasks using the Rattle R package.


How to Win a Data Science Competition: Learn from Top Kagglers Coursera

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About this course: If you want to break into competitive data science, then this course is for you! Participating in predictive modelling competitions can help you gain practical experience, improve and harness your data modelling skills in various domains such as credit, insurance, marketing, natural language processing, sales' forecasting and computer vision to name a few. At the same time you get to do it in a competitive context against thousands of participants where each one tries to build the most predictive algorithm. Pushing each other to the limit can result in better performance and smaller prediction errors. Being able to achieve high ranks consistently can help you accelerate your career in data science.


Deep learning Calculus - Data Science - Machine Learning AI - BuzzTechy

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Udemy Online Course - Deep learning Calculus - Data Science - Machine Learning AI Mastering Calculus for Deep learning / Machine learning / Data Science / Data Analysis / AI using Python You start by learning the definition of function and move your way up for fitting the data to the function which is the core for any Machine learning, Deep Learning, Artificial intelligence, Data Science Application. Once you have mastered the concepts of this course, you will never be blind while applying the algorithm to your data, instead you have the intuition as how each code is working in background. What you'll learn Build Mathematical intuition especially Calculus required for Deep learning, Data Science and Machine Learning The Calculus intuition required to become a Data Scientist / Machine Learning / Deep learning Practitioner How to take their Data Science / Machine Learning / Deep learning career to the next level Hacks, tips & tricks for their Data Science / Machine Learning / Deep learning career Implement Machine Learning / Deep learning Algorithms better Learn core concept to Implement in Machine Learning / Deep learning Who this course is for: Data Scientists who wish to improve their career in Data Science. Deep learning / Machine learning practitioner who wants to take the career to next level Any one who wants to understand the underpinnings of Maths in Data Science, Machine Learning, Deep Learning and Artificial intelligence Any Data Science / Machine Learning / Deep learning enthusiast Any student or professional who wants to start or transition to a career in Data Science / Machine Learning / Deep learning Students who want to refresh and learn important maths concepts required for Machine Learning, Deep Learning & Data Science. Data Scientists who wish to improve their career in Data Science.


Bayesian Statistics: Techniques and Models Coursera

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About this course: This is the second of a two-course sequence introducing the fundamentals of Bayesian statistics. It builds on the course Bayesian Statistics: From Concept to Data Analysis, which introduces Bayesian methods through use of simple conjugate models. Real-world data often require more sophisticated models to reach realistic conclusions. This course aims to expand our "Bayesian toolbox" with more general models, and computational techniques to fit them. In particular, we will introduce Markov chain Monte Carlo (MCMC) methods, which allow sampling from posterior distributions that have no analytical solution.