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Course Skill Level:


Course Duration:

4 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    AI / Machine Learning

  • Course Code:


Who should attend & recommended skills:

Those with Python experience and Basic IT & Linux skills seeking to implement supervised & unsupervised ML algorithms

Who should attend & recommended skills

  • This course is geared for Python experienced developers, analysts or others 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.
  • Skill-level: Foundation-level Machine Learning with C++ skills for Intermediate skilled team members. This is not a basic class.
  • IT Skills: Basic to Intermediate (1-5 years’ experience)
  • Linux: Basic (1-2 years’ experience), including familiarity with command-line options such as ls, cd, cp, and su
  • Attendees without a programming background like Python may view labs as follow along exercises or team with others to complete them.

About this course

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 course makes 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 will 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 will explore neural networks and deep learning using examples such as image classification and sentiment analysis, which will help you solve various problems. Later, you will 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.

Skills acquired & topics covered

  • Data processing, performance measuring, and model selection using various C++ libraries
  • Implementing practical machine learning and deep learning techniques to build smart models
  • Deploying machine learning models to work on mobile and embedded devices
  • Loading and preprocessing various data types to suitable C++ data structures
  • Employing key machine learning algorithms with various C++ libraries
  • The grid-search approach to find the best parameters for a machine learning model
  • Implementing an algorithm for filtering anomalies in user data using Gaussian distribution
  • Improving collaborative filtering to deal with dynamic user preferences
  • Using C++ libraries and APIs to manage model structures and parameters
  • Implementing a C++ program to solve image classification tasks with LeNet architecture

Course breakdown / modules

  • Understanding the fundamentals of ML
  • An overview of linear algebra
  • An overview of linear regression

  • 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

  • Technical requirements
  • Performance metrics for ML models
  • Understanding the bias and variance characteristics
  • Model selection with the grid search technique

  • 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++

  • Technical requirements
  • Exploring the applications of anomaly detection
  • Learning approaches for anomaly detection
  • Examples of using different C++ libraries for anomaly detection

  • 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

  • Technical requirements
  • An overview of classification methods
  • Exploring various classification methods
  • Examples of using C++ libraries for dealing with the classification task

  • Technical requirements
  • An overview of recommender system algorithms
  • Understanding collaborative filtering method details
  • Examples of item-based collaborative filtering with C++

  • Ensemble Learning
  • Technical requirements
  • An overview of ensemble learning
  • Examples of using C++ libraries for creating ensembles

  • 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

  • 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

  • Technical requirements
  • ML model serialization APIs in C++ libraries
  • Delving into ONNX format

  • Technical requirements
  • Image classification on Android mobile
  • Machine learning in the cloud using Google Compute Engine