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C# Machine Learning Projects

  • Course Code: Artificial Intelligence - C# Machine Learning Projects
  • Course Dates: Contact us to schedule.
  • Course Category: AI / Machine Learning Duration: 3 Days Audience: This course is geared for those who wants to Power C# and .NET applications with exciting machine learning models and modular projects

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 Power C# and .NET applications with exciting machine learning models and modular projects 
  • 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. 

Machine learning is applied in almost all kinds of real-world surroundings and industries, right from medicine to advertising; from finance to scientifc research. This course will help you learn how to choose a model for your problem, how to evaluate the performance of your models, and how you can use C# to build machine learning models for your future projects. You will get an overview of the machine learning systems and how you, as a C# and .NET developer, can apply your existing knowledge to the wide gamut of intelligent applications, all through a project-based approach. You will start by setting up your C# environment for machine learning with the required packages, Accord.NET, LiveCharts, and Deedle. We will then take you right from building classifcation models for spam email fltering and applying NLP techniques to Twitter sentiment analysis, to time-series and regression analysis for forecasting foreign exchange rates and house prices, as well as drawing insights on customer segments in e-commerce. You will then build a recommendation model for music genre recommendation and an image recognition model for handwritten digits. Lastly, you will learn how to detect anomalies in network and credit card transaction data for cyber attack and credit card fraud detections. By the end of this course, you will be putting your skills in practice and implementing your machine learning knowledge in real projects. 

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

  • Produce classification, regression, association, and clustering models 
  • Expand your understanding of machine learning and C# 
  • Get to grips with C# packages such as Accord.net, LiveCharts, and Deedle 

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

  • Set up the C# environment for machine learning with required packages 
  • Build classification models for spam email filtering 
  • Get to grips with feature engineering using NLP techniques for Twitter sentiment analysis 
  • Forecast foreign exchange rates using continuous and time-series data 
  • Make a recommendation model for music genre recommendation 
  • Familiarize yourself with munging image data and Neural Network models for handwritten-digit recognition 
  • Use Principal Component Analysis (PCA) for cyber attack detection 
  • One-Class Support Vector Machine for credit card fraud detection 

Audience & Pre-Requisites 

This course is geared for attendees who wish to Power C# and .NET applications with exciting machine learning models and modular projects 

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. Basics of Machine Learning Modeling 
  • Basics of Machine Learning Modeling 
  • Key ML tasks and applications 
  • Steps in building ML models 
  • Setting up a C# environment for ML 
  1. Spam Email Filtering 
  • Spam Email Filtering 
  • Problem definition for the spam email filtering project 
  • Data preparation 
  • Email data analysis 
  • Feature engineering for email data 
  • Logistic regression versus Naive Bayes for email spam filtering 
  • Classification model validations 
  1. Twitter Sentiment Analysis 
  • Twitter Sentiment Analysis 
  • Setting up the environment 
  • Problem definition for Twitter sentiment analysis 
  • Data preparation using Stanford CoreNLP 
  • Data analysis using lemmas as tokens 
  • Feature engineering using lemmatization and emoticons 
  • Naive Bayes versus random forest 
  • Model validations – ROC curve and AUC 
  1. Foreign Exchange Rate Forecast 
  • Foreign Exchange Rate Forecast 
  • Problem definition 
  • Data preparation 
  • Time series data analysis 
  • Feature engineering 
  • Linear regression versus SVM 
  • Model validations  
  1. Fair Value of House and Property 
  • Fair Value of House and Property 
  • Problem definition 
  • Categorical versus continuous variables 
  • Feature engineering and encoding 
  • Linear regression versus SVM with kernels 
  • Model validations  
  1. Customer Segmentation 
  • Customer Segmentation 
  • Problem definition 
  • Data analysis for the online retail dataset 
  • Feature engineering and data aggregation 
  • Unsupervised learning – k-means clustering 
  • Clustering model validations using the Silhouette Coefficient 
  1. Music Genre Recommendation 
  • Music Genre Recommendation 
  • Problem definition 
  • Data analysis for the audio features dataset 
  • ML models for music genre classification 
  • Ensembling base learning models 
  • Evaluating recommendation/rank-ordering models 
  1. Handwritten Digit Recognition 
  • Handwritten Digit Recognition 
  • Problem definition 
  • Data analysis for the image dataset 
  • Feature engineering and dimensionality reduction 
  • ML models for handwritten digit recognition 
  • Evaluating multi-class classification models 
  1. Cyber Attack Detection 
  • Cyber Attack Detection 
  • Problem definition 
  • Data analysis for internet traffic data 
  • Feature engineering and PCA 
  • Principal component classifier for anomaly detection 
  • Evaluating anomaly detection models 
  1. Credit Card Fraud Detection 
  • Credit Card Fraud Detection 
  • Problem definition 
  • Data analysis for anonymized credit card data 
  • Feature engineering and PCA 
  • One-class SVM versus PCC 
  • Evaluating anomaly detection models 
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