
Comprehensive guide to PyTorch: from basics to advanced concepts, covering tensors, neural networks, and model deployment. Ideal for beginners seeking practical skills.

Explore deep learning and neural networks using Python and PyTorch, covering data handling, model building, training, and analysis, with a focus on convolutional networks and GPU acceleration.

Learn PyTorch fundamentals, from installation to advanced applications. Master tensors, backpropagation, and deep learning techniques for real-world data science projects.

Learn PyTorch fundamentals, from linear models to advanced CNNs. Master gradient descent, back-propagation, regression, and neural network architectures for efficient deep learning implementation.

Learn to build an Alexa Skill from scratch, covering internationalization, error handling, slots, persistence, APIs, Alexa Presentation Language, and publishing using the ASK CLI.

Learn to create Alexa Skills using Voiceflow and Node.js. Master voice UI design, configure functionality, and deploy custom voice commands for Alexa devices. Develop, test, and publish skills for business growth.

Comprehensive OSINT training covering domain analysis, threat detection, reconnaissance tools, search engine mastery, and network scanning techniques for enhanced cybersecurity skills.

Comprehensive OSINT techniques covering tools like Maltego, KISMET, and Tor for information gathering, network analysis, and maintaining anonymity in digital investigations.

Explore advanced image analysis techniques using ArcGIS Pro and arcgis.learn, including feature classification, object extraction, and point cloud processing for geospatial applications.

Comprehensive tutorials on deep learning and neural networks, covering key concepts, architectures, and practical implementation using TensorFlow and Google Colab.

Hands-on projects in machine learning and deep learning, covering chatbots, sentiment analysis, image captioning, speech recognition, customer segmentation, and facial expression detection.

This course provides an understanding of natural language processing, its tools, techniques, philosophy and principle. It will cover Tokenization, Stop Words and Word Embedding.

Learn to implement YOLO v4 for object detection: from image labeling and dataset creation to model training and custom object detection, with hands-on coding and practical applications.

Comprehensive introduction to machine learning algorithms, from regression to neural networks, with practical Python implementations and engaging whiteboard explanations for beginners and enthusiasts.

Learn to build 6 AR apps using ARCore and Unity, covering motion tracking, light estimation, hit testing, and advanced features like portals and multiplayer.

Learn to implement YOLO v3 for robust object detection, from web scraping and dataset annotation to model training and deployment, using OpenCV and Python.

Learn to implement and train Mask R-CNN for instance segmentation using OpenCV and Python, covering installation, real-time applications, and custom object detection like potholes.

Learn to implement pose estimation using OpenPose, covering installation, people counting, fall detection, yoga pose recognition, and more. Gain practical skills in computer vision and deep learning applications.

Learn object detection and segmentation using Detectron2, covering installation, data preparation, and implementation with OpenCV and Python for computer vision applications.

Learn essential computer vision techniques using TensorFlow and MediaPipe. Build real-time applications for face detection, pose estimation, hand tracking, and object detection with Python and OpenCV.

Learn advanced object detection techniques with YOLOR, covering implementation, optimization, and real-world applications in computer vision and AI. Gain practical skills for various industries.