Machine Learning with C++: From Fundamentals to Deployment

Learn to parse data formats in C++, including CSV, JSON, and XML, implement simple readers, and perform data preprocessing to prepare datasets for machine learning.

Explore image manipulation in C++ with OpenCV and Boost.GIL, covering loading, displaying, resizing, grayscale conversion, drawing shapes and text, Gaussian blur, thresholding, and Canny edge detection.

Master data normalization in machine learning with C++ by implementing min-max scaling, z-score normalization, robust scaling, and one-hot encoding, ensuring consistent training, validation, and test splits.

Explore grid search and randomized search for hyperparameter tuning, use cross-validation to evaluate SVM configurations, and learn how kernel type, C, and gamma shape model performance.

Explore unsupervised clustering, including k-means, dbscan, and mean shift, with Euclidean and Manhattan distances, visualization, and applications in customer segmentation and fraud detection.

Explore clustering types, including k-means, dbscan, mean shift, and hierarchical clustering, and implement them in c++ with mlpack, visualizing results for marketing, healthcare, finance, and retail.

Explore learning approaches for anomaly detection in C++, covering Z-score, isolation forest, DBSCAN, SVM, one-class SVM, K-means, and autoencoders, with practical training and deployment insights.

Explore classification methods, including logistic regression in C++, KNN, and SVM, and how these linear techniques apply to data analysis.

Explore key classification methods in machine learning, including logistic regression, k-nearest neighbors, SVM, decision trees, and random forests, and learn how ensemble learning can boost performance, with C++ implementations.

Explore how recommender systems analyze user interactions to predict personalized items across platforms. Implement content-based filtering with cosine similarity and collaborative filtering with Pearson correlation, addressing cold-start and recency.

Explore collaborative filtering in recommender systems, using an item interaction matrix and user similarities to generate recommendations, and implement cosine similarity and matrix factorization methods like SVD and ALS.

Explore ensemble learning by combining multiple base learners via bagging and boosting, with C++ demos of bootstrap sampling and decision trees, and weighted averaging to improve accuracy and reduce overfitting.

Learn stacking ensembles in C++ by combining a decision tree and logistic regression with a meta learner, validated via k-fold cross-validation and grid search for hyperparameters.

Implement a convolutional neural network in C++ for image classification with mNIST, using convolutional and pooling layers and a fully connected layer of ten neurons.

Leverage transfer learning with BERT for sentiment analysis by loading a pre-trained model, adding a dropout and a classification head, and training only the new layers.

Explore model serialization APIs in Dlib, Flashlight, Mlpack, and PyTorch, saving and loading trained models to enable efficient deployment and immediate inference.

Learn to set up MLflow, create experiments, and log parameters, metrics, and artifacts via the MLflow rest API using C++ and curl to streamline machine learning workflows.

Deploy mobile machine learning models with C++ and PyTorch on Android, building a lightweight YOLOv5 detector, loading with TorchScript, preprocessing images, running inference, and handling post-processing.

Configure an android app to access the camera with runtime permissions and a surface view, then process frames via jni c++ using opencv for real-time yolov5 object detection with visualization.