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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: 4 Days Audience: This course is geared for Python experienced developers, analysts or others who wants to Implement supervised and unsupervised machine learning algorithms using C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib with the help of real-world examples and datasets

Course Snapshot 

  • Duration: 4 days 
  • Skill-level: Foundation-level Machine Learning with C++ skills for Intermediate skilled team members. This is not a basic class. 
  • Targeted Audience: This course is geared for Python experienced developers, analysts or others who wants to Implement supervised and unsupervised machine learning algorithms using C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib with the help of real-world examples and datasets  
  • 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. 

C++ can make your machine learning models run faster and more efficiently. This handy guide will help you learn the fundamentals of machine learning (ML), showing you how to use C++ libraries to get the most out of your data. This coursemakes machine learning with C++ for beginners easy with its example-based approach, demonstrating how to implement supervised and unsupervised ML algorithms through real-world examples.This course will get you with tuning and optimizing a model for different use cases, assisting you with model selection and the measurement of performance. You’ll cover techniques such as product recommendations, ensemble learning, and anomaly detection using modern C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib. Next, you’ll explore neural networks and deep learning using examples such as image classification and sentiment analysis, which will help you solve various problems. Later, you’ll learn how to handle production and deployment challenges on mobile and cloud platforms, before discovering how to export and import models using the ONNX format. By the end of this C++ course, you will have real-world machine learning and C++ knowledge, as well as the skills to use C++ to build powerful ML systems. 

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

  • Become familiar with data processing, performance measuring, and model selection using various C++ libraries 
  • Implement practical machine learning and deep learning techniques to build smart models 
  • Deploy machine learning models to work on mobile and embedded devices 

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

  • Explore how to load and preprocess various data types to suitable C++ data structures 
  • Employ key machine learning algorithms with various C++ libraries 
  • Understand the grid-search approach to find the best parameters for a machine learning model 
  • Implement an algorithm for filtering anomalies in user data using Gaussian distribution 
  • Improve collaborative filtering to deal with dynamic user preferences 
  • Use C++ libraries and APIs to manage model structures and parameters 
  • Implement a C++ program to solve image classification tasks with LeNet architecture 

Audience & Pre-Requisites 

This course is geared for attendees with Python skills who wish to Implement supervised and unsupervised machine learning algorithms using C++ libraries such as PyTorch C++ API, Caffe2, Shogun, Shark-ML, mlpack, and dlib with the help of real-world examples and datasets 

Pre-Requisites:  Students should have  

  • Basic to Intermediate IT Skills. Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them. 
  • 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. Section 1: Overview of Machine Learning 
  1. Introduction to Machine Learning with C++ 
  • Introduction to Machine Learning with C++ 
  • Understanding the fundamentals of ML 
  • An overview of linear algebra  
  • An overview of linear regression 
  1. Data Processing 
  • Data Processing 
  • Technical requirements 
  • Parsing data formats to C++ data structures 
  • Initializing matrix and tensor objects from C++ data structures 
  • Manipulating images with the OpenCV and Dlib libraries 
  • Transforming images into matrix or tensor objects of various libraries 
  • Normalizing data 
  1. Measuring Performance and Selecting Models 
  • Technical requirements 
  • Performance metrics for ML models 
  • Understanding the bias and variance characteristics 
  • Model selection with the grid search technique 
  1. Section 2: Machine Learning Algorithms 
  1. Clustering 
  • Technical requirements 
  • Measuring distance in clustering 
  • Types of clustering algorithms 
  • Examples of using the Shogun library for dealing with the clustering task samples 
  • Examples of using the Shark-ML library for dealing with the clustering task samples 
  • Examples of using the Dlib library for dealing with the clustering task samples 
  • Plotting data with C++ 
  1. Anomaly Detection 
  • Technical requirements 
  • Exploring the applications of anomaly detection 
  • Learning approaches for anomaly detection 
  • Examples of using different C++ libraries for anomaly detection 
  1. Dimensionality Reduction 
  • Technical requirements 
  • An overview of dimension reduction methods 
  • Exploring linear methods for dimension reduction 
  • Exploring non-linear methods for dimension reduction 
  • Understanding dimension reduction algorithms with various С++ libraries 
  1. Classification 
  • Technical requirements 
  • An overview of classification methods 
  • Exploring various classification methods 
  • Examples of using C++ libraries for dealing with the classification task 
  1. Recommender Systems 
  • Technical requirements 
  • An overview of recommender system algorithms  
  • Understanding collaborative filtering method details  
  • Examples of item-based collaborative filtering with C++ 
  1. Ensemble Learning 
  • Ensemble Learning 
  • Technical requirements 
  • An overview of ensemble learning 
  • Examples of using C++ libraries for creating ensembles 
  1. Section 3: Advanced Examples 
  1. Neural Networks for Image Classification 
  • Technical requirements 
  • An overview of neural networks  
  • Delving into convolutional networks 
  • What is deep learning? 
  • Examples of using C++ libraries to create neural networks 
  • Understanding image classification using the LeNet architecture 
  1. Sentiment Analysis with Recurrent Neural Networks 
  • Sentiment Analysis with Recurrent Neural Networks 
  • Technical requirements 
  • An overview of the RNN concept 
  • Training RNNs using the concept of backpropagation through time
  • Exploring RNN architectures 
  • Understanding natural language processing with RNNs 
  • Sentiment analysis example with an RNN 
  1. Section 4: Production and Deployment Challenges 
  1. Exporting and Importing Models 
  • Technical requirements 
  • ML model serialization APIs in C++ libraries 
  • Delving into ONNX format 
  1. Deploying Models on Mobile and Cloud Platforms 
  • Technical requirements 
  • Image classification on Android mobile 
  • Machine learning in the cloud – using Google Compute Engine 
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