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      Dallas Prerequisites to Learning AI [Mar 17 - Apr 8, 2018] Training | Artificial Intelligence | Machine Learning | Deep Learning | IT Training | Disruptive Technologies in Dallas


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      March 17, 2018

      Saturday  10:00 AM

      Online - please disregard address & map locations.
      Dallas, Texas

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      Dallas Prerequisites to Learning AI [Mar 17 - Apr 8, 2018] Training | Artificial Intelligence | Machine Learning | Deep Learning | IT Training | Disruptive Technologies

      Video Conference Details Will be sent after registration and payment Course Overview The prerequisites to learning Disruptive technologies include:  Probability  Statistics  Linear Algebra  Calculus (Differential, Multivariate)  Data Structures  Algorithms  High level Programming Language such as Python for beginners  Object Oriented Programming Language such as Java (Optional) About this course This course is structured according to the background and existing knowledge of the students. The goal of this course is to learn the prerequisites quickly and move on to learn the disruptive technologies The instructor(s) and the students together decide what they want to skip and what they want to learn based on this comprehensive course outline mentioned below. Instructor can add, edit the course outline to suit the class. What you will learn in this course? In this course, you’ll learn the foundational knowledge which will be useful in learning disruptive technologies. What are the pre-requisites?  No prerequisite is required.  Some statistics, probability, computer and programming background will be helpful Comprehensive and Detailed Course Outline Computer Programming for those with no programming background (if required, otherwise skip to next section) Intended for students without prior programming experience. Basic programming-in-the-small abilities and concepts including procedural programming (methods, parameters, return, values) Basic control structures (sequence, if/else, for loop, while loop) File processing Arrays An introduction to defining objects. Intermediate Computer Programming for those with some programming background Concepts of data abstraction and encapsulation including stacks, queues, linked lists, binary trees, recursion, instruction to complexity and use of predefined collection classes. Data Structures & Algorithms  Fundamental algorithms and data structures for implementation  Techniques for solving problems by programming  Linked lists, stacks, queues, directed graphs.  Trees: representations, traversals.  Searching (hashing, binary search trees, multiway trees).  Garbage collection, memory management.  Internal and external sorting  Abstract data types and structures including dictionaries, balanced trees, hash tables, priority queues, and graphs  Sorting; asymptotic analysis; fundamental graph algorithms including graph search, shortest path, and minimum spanning trees; concurrency and synchronization; Foundations of Computing  Examines fundamentals of logic  Set theory, induction  Algebraic structures with applications to computing  Finite state machines  Limits of computability Probability & Statistics  Visualizing relationships in data  Seeing relationships in data.  Making predictions based on data.  Simpson's paradox.  Probability  Introduction to Probability.  Bayes Rule.  Correlation vs. Causation.  Estimation  Maximum Likelihood Estimation.  Mean, Median, Mode.  Standard Deviation and Variance.  Outliers and Normal Distribution.  Outliers, Quartiles.  Binomial Distribution.  Manipulating Normal Distribution.  Inference.  Confidence Intervals.  Hypothesis Testing.  Regression  Linear regression.  Correlation. Linear algebra  Vectors  Vectors and spaces  Matrix transformations  Alternate coordinate systems (bases) Calculus (Differential calculus, multivariate calculus)  Limits and continuity  Derivatives, Differentiations  Derivatives Applications  Equations Training Dates March 17 - April 8, 2018 Times: Every Sat & Sun; 8:00 AM - 10:00 AM (Pacific Standard Time) Please check local date and time Each session will be recorded and the recordings will be shared after each session with students    Refund Policy 1. There are no refunds.2. If for any reason the course has not been taken, class is cancelled or rescheduled, the payment can be applied towards any future course by Omni212. Omni212 Prime membership Now become an Omni212 Prime member and get $100 off every training course published by Omni212 on eventbrite  Sign up for Omni212 Prime membership: http://bit.ly/2yT72Qu To see all currently published Omni212 courses - Omni212 training and name of your city in the search box.   Unlimited Training Now you can enjoy unlimited training from Omni212. Find out more about our Unlimited training Plan: http://bit.ly/2A1L6R6

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