Machine Learning Course for Beginners - Theory

This beginner course provides a concise theoretical survey of machine learning methods, neural networks, and computer vision models. It covers regression, classification, clustering, association rule learning, dimensionality reduction, object detection, pose estimation, and object tracking without emphasizing complex mathematics.

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