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Home » Technology » AI / Machine Learning » C# Machine Learning Projects

Course Skill Level:

Foundational

Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:

    CSMLPRL21E09

Who should attend & recommended skills:

Those with basic Linux skills who want to power C# & .NET applications with machine learning models and modular projects

Who should attend & recommended skills

  • This course is geared for those who wish to power C# and .NET applications with exciting machine learning models and modular projects.
  • Skill-level: Foundation-level C# Machine Learning skills for Intermediate skilled team members. This is not a basic class.
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su

About this course

Machine learning is applied in almost all kinds of real-world surroundings and industries, right from medicine to advertising; from finance to scientific 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 classification 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.

Skills acquired & topics covered

  • Working in a hands-on learning environment, led by our C# Machine Learning expert instructor, students will learn about and explore:
  • Producing classification, regression, association, and clustering models
  • Expanding your understanding of machine learning and C#
  • Getting to grips with C# packages such as Accord.net, LiveCharts, and Deedle
  • Setting up the C# environment for machine learning with required packages
  • Building classification models for spam email filtering
  • Getting to grips with feature engineering using NLP techniques for Twittersentiment analysis
  • Forecasting foreign exchange rates using continuous and time-series data
  • Making a recommendation model for music genre recommendation
  • Familiarizing yourself with munging image data and Neural Network modelsfor handwritten-digit recognition
  • Using Principal Component Analysis (PCA) for cyber attack detection
  • One-Class Support Vector Machine for credit card fraud detection

Course breakdown / modules

  • Key ML tasks and applications
  • Steps in building ML models
  • Setting up a C# environment for ML

  • 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

  • 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

  • Problem definition
  • Data preparation
  • Time series data analysis
  • Feature engineering
  • Linear regression versus SVM
  • Model validations

  • Problem definition
  • Categorical versus continuous variables
  • Feature engineering and encoding
  • Linear regression versus SVM with kernels
  • Model validations

  • 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

  • Problem definition
  • Data analysis for the audio features dataset
  • ML models for music genre classification
  • Ensembling base learning models
  • Evaluating recommendation/rank-ordering models

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