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Machine Learning with C#

  • Course Code: Artificial Intelligence - Machine Learning with C#
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 3 Days Audience: This course is geared for those who wants to Explore supervised and unsupervised learning techniques and add smart features to your applications

Course Snapshot 

  • Duration: 3 days 
  • Skill-level: Foundation-level C# Machine Learning skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for those who wants to Explore supervised and unsupervised learning techniques and add smart features to your applications 
  • Hands-on Learning: This course is approximately 50% hands-on lab to 50% lecture ratio, combining engaging lecture, demos, group activities and discussions with machine-based student labs and exercises. Student machines are required. 
  • Delivery Format: This course is available for onsite private classroom presentation. 
  • Customizable: This course may be tailored to target your specific training skills objectives, tools of choice and learning goals. 

The necessity for machine learning is everywhere, and most production enterprise applications are written in C# using tools such as Visual Studio, SQL Server, and Microsoft Azur2e. Hands-On Machine Learning with C# uniquely blends together an understanding of various machine learning concepts, techniques of machine learning, and various available machine learning tools through which users can add intelligent features. These tools include image and motion detection, Bayes intuition, and deep learning, to C# .NET applications. Using this course, you will learn to implement supervised and unsupervised learning algorithms and will be better equipped to create excellent predictive models. In addition, you will learn both supervised and unsupervised forms of regression, mainly logistic and linear regression, in depth. Next, you will use the nuML machine learning framework to learn how to create a simple decision tree. In the concluding lessons, you will use the Accord.Net machine learning framework to learn sequence recognition of handwritten numbers using dynamic time warping. We will also cover advanced concepts such as artificial neural networks, autoencoders, and reinforcement learning. By the end of this course, you will have developed a machine learning mindset and will be able to leverage C# tools, techniques, and packages to build smart, predictive, and real-world business applications. 

Working in a hands-on learning environment, led by our C# Machine Learning expert instructor, students will learn about and explore: 

  • Leverage machine learning techniques to build real-world applications 
  • Use the Accord.NET machine learning framework for reinforcement learning 
  • Implement machine learning techniques using Accord, nuML, and Encog 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below 

  • Learn to parameterize a probabilistic problem 
  • Use Naive Bayes to visually plot and analyze data 
  • Plot a text-based representation of a decision tree using nuML 
  • Use the Accord.NET machine learning framework for associative rule-based learning 
  • Develop machine learning algorithms utilizing fuzzy logic 
  • Explore support vector machines for image recognition 
  • Understand dynamic time warping for sequence recognition 

Audience & Pre-Requisites 

This course is geared for attendees who wish to Explore supervised and unsupervised learning techniques and add smart features to your applications 

Pre-Requisites:  Students should have  

  • Good foundational mathematics or logic skills 
  • Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su 

Course Agenda / Topics 

  1. Machine Learning Basics 
  • Machine Learning Basics 
  • Introduction to machine learning 
  • Data mining 
  • Artificial Intelligence 
  • Bio-AI 
  • Deep learning 
  • Probability and statistics 
  • Approaching your machine learning project 
  • Iris dataset 
  • Supervised learning 
  • Unsupervised learning 
  • Reinforcement learning 
  • Build, buy, or open source 
  1. ReflectInsight – Real-Time Monitoring 
  • ReflectInsight – Real-Time Monitoring 
  • Router 
  • Log Viewer 
  • Live Viewer 
  1. Bayes Intuition – Solving the Hit and Run Mystery and Performing Data Analysis 
  • Bayes Intuition – Solving the Hit and Run Mystery and Performing Data Analysis 
  • Overviewing Bayes’ theorem 
  • Overviewing Naive Bayes and plotting data 
  1. Risk versus Reward – Reinforcement Learning 
  • Risk versus Reward – Reinforcement Learning 
  • Overviewing reinforcement learning 
  • Types of learning 
  • Q-learning 
  • SARSA 
  • Running our application 
  • Tower of Hanoi 
  1. Fuzzy Logic – Navigating the Obstacle Course 
  • Fuzzy Logic – Navigating the Obstacle Course 
  • Fuzzy logic 
  1. Color Blending – Self-Organizing Maps and Elastic Neural Networks 
  • Color Blending – Self-Organizing Maps and Elastic Neural Networks 
  • Under the hood of an SOM 
  1. Facial and Motion Detection – Imaging Filters 
  • Facial and Motion Detection – Imaging Filters 
  • Facial detection 
  • Motion detection 
  1. Encyclopedias and Neurons – Traveling Salesman Problem 
  • Encyclopedias and Neurons – Traveling Salesman Problem 
  • Traveling salesman problem 
  • Learning rate parameter 
  1. Should I Take the Job – Decision Trees in Action 
  • Should I Take the Job – Decision Trees in Action 
  • Decision tree 
  • Should I take the job? 
  • numl 
  • Accord.NET decision trees 
  1. Deep Belief – Deep Networks and Dreaming 
  • Deep Belief – Deep Networks and Dreaming 
  • Restricted Boltzmann Machines 
  • What does a computer dream? 
  1. Microbenchmarking and Activation Functions 
  • Microbenchmarking and Activation Functions 
  • Visual activation function plotting 
  1. Intuitive Deep Learning in C# .NET 
  • Intuitive Deep Learning in C# .NET 
  • What is deep learning? 
  • The Kelp.Net Framework 
  1. Quantum Computing – The Future 
  • Quantum Computing – The Future 
  • Superposition 
  • Teleportation 
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