Data Science & AI
intermediate
Machine Learning Algorithms
A comprehensive course covering Machine learning. Topics include supervised learning, unsupervised learning, regression, classification, decision trees, random forests, SVM, k-means clustering, and model evaluation metrics. Designed for intermediate level learners.
14 hours
3 modules
12 lessons
1 enrolled
Dr
Dr. Priya Patel
Professor of Data Science & Artificial Intelligence
Free
Full access to all course materials
Course Content
Foundations of Unsupervised Learning
4 lessons
Supervised Learning
29 min
Unsupervised Learning
20 min
Regression
30 min
Applying Supervised Learning
31 min
Understanding Classification
4 lessons
Classification
28 min
Decision Trees
23 min
Random Forests
24 min
Applying Classification
28 min
Working with Model Evaluation Metrics
4 lessons
SVM
28 min
K-means Clustering
29 min
Model Evaluation Metrics
21 min
Applying SVM
34 min