Dive into a comprehensive 3-hour video course covering essential theoretical topics in machine learning, neural networks, and computer vision. Learn about decision trees, random forests, logistic regression, K-nearest neighbors, support vector machines, Naïve Bayes, clustering techniques, principal component analysis, and various neural network architectures. Explore advanced concepts like YOLO object detection, Mask R-CNN, pose estimation, and object tracking. Benefit from whiteboard animations that simplify complex concepts, making the learning experience engaging and accessible for beginners. Gain a solid foundation in machine learning theory without getting bogged down in complex mathematics, emerging with expert-level knowledge upon completion.
Overview
Syllabus
- Introduction
- Decision Tree
- Random Forests
- Logistic Regression
- K-Nearest Neighbors KNN
- Support Vector Machines SVM
- Naïve Bayes
- K-Means Clustering
- Hierarchical Clustering
- Principal Component Analysis
- Linear Discriminant Analysis
- Apriori
- Eclat
- Artificial Neural Networks
- Convolutional Neural Networks
- Recurrent Neural Networks
- YOLO v1- v3 You Only Look Once
- YOLOv4 Object Detection
- Mask RCNN
- Pose Estimation - OpenPose
- DeepSORT Object Tracking
Taught by
Augmented Startups
