Machine Learning Using Spark MLLib
Learn to implement machine learning algorithms using Spark MLLib, covering linear regression techniques, data preprocessing, model building, and evaluation methods in a hands-on, practical approach.
YOLO V4 Object Detection Crash Course
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.
The Fun and Easy Guide to Machine Learning - Tutorials
Comprehensive introduction to machine learning algorithms, from regression to neural networks, with practical Python implementations and engaging whiteboard explanations for beginners and enthusiasts.
Practical Augmented Reality Course
Learn to build 6 AR apps using ARCore and Unity, covering motion tracking, light estimation, hit testing, and advanced features like portals and multiplayer.
YOLO V3 - Robust Deep Learning Object Detection Tutorials
Learn to implement YOLO v3 for robust object detection, from web scraping and dataset annotation to model training and deployment, using OpenCV and Python.
Mask R-CNN - Practical Deep Learning Instance Segmentation Tutorials
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.
Pose Estimation - Deep Learning Using OpenPose Tutorials
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.
Detectron2 Tutorials
Learn object detection and segmentation using Detectron2, covering installation, data preparation, and implementation with OpenCV and Python for computer vision applications.
TensorFlow Computer Vision Tutorials - OpenCV Python
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.
Machine Learning Fun and Easy
Explore popular machine learning concepts through engaging lectures covering regression, clustering, neural networks, and more. Gain fundamental knowledge in an accessible, enjoyable format.
YOLOR Object Detection Tutorials - You Only Learn One Representation
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.
YOLOX Object Detection
Learn to implement YOLOX object detection, understand its speed and accuracy, explore non-max suppression, and build analytical dashboards using Plotly Dash for computer vision projects.
From Python to Rust
Comprehensive series bridging Python and Rust, covering fundamental concepts, advanced topics, and practical applications for seamless transition between the two languages.
Operating Systems
Comprehensive exploration of operating system concepts, covering process management, scheduling algorithms, deadlock handling, and memory management techniques.
Data Structures and Algorithms
Comprehensive exploration of data structures and algorithms, covering arrays, linked lists, stacks, queues, trees, graphs, sorting, searching, and hashing techniques with practical implementations.
Dynamic Programming
Explore dynamic programming techniques for solving complex algorithmic problems efficiently, including coin change, knapsack, and longest common subsequence.
Design and Analysis of Algorithms
Explore algorithm fundamentals, analysis techniques, and key concepts for GATE/NET exams. Learn to write effective algorithms and understand their properties.
Docker Tutorial for Beginners
Comprehensive guide to Docker: containerization, installation, commands, architecture, orchestration, AWS integration, monitoring, and Spring Boot deployment with hands-on demonstrations.
IDEs for Machine Learning
Explore open-source IDEs for machine learning, including Jupyter Notebook, Google Colab, Spyder, and RStudio. Learn features, interfaces, and example codes to enhance your coding efficiency in ML projects.
Web Development Projects
Learn to build responsive websites and interactive games using HTML, CSS, JavaScript, Bootstrap, and jQuery through hands-on projects like e-commerce sites and a Ping Pong game.
Artificial Intelligence Projects
Explore AI projects using neural networks for bank default prediction, stock forecasting, and image processing. Learn CNN, RNN, and time series analysis techniques.