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1
- Introduction
2
- Decision Tree
3
- Random Forests
4
- Logistic Regression
5
- K-Nearest Neighbors KNN
6
- Support Vector Machines SVM
7
- Naïve Bayes
8
- K-Means Clustering
9
- Hierarchical Clustering
10
- Principal Component Analysis
11
- Linear Discriminant Analysis
12
- Apriori
13
- Eclat
14
- Artificial Neural Networks
15
- Convolutional Neural Networks
16
- Recurrent Neural Networks
17
- YOLO v1- v3 You Only Look Once
18
- YOLOv4 Object Detection
19
- Mask RCNN
20
- Pose Estimation - OpenPose
21
- DeepSORT Object Tracking
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it 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.

Machine Learning Course for Beginners - Theory

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