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In this course, you will :
- Understanding of the core machine learning algorithms.
- Strong problem-solving skills developed through hands-on projects.
- Working understanding of applying machine learning algorithms to real-world datasets, dealing with classification, regression, clustering, and dimensionality reduction tasks
- Practical experience evaluating and comparing the performance of machine learning models.
Syllabus :
- Supervised Learning
- Clustering
- Project: Bag of Visual Words
- Generalized Linear Regression
- Face Recognition Using Kernel Linear Discriminant
- Support Vector Machine
- Logistic Regression
- Ensemble Learning
- Early Stage Diabetes Prediction Using Ensemble Learning
- Decoding Dimensions: PCA and Autoencoders
- Image Reconstruction Using PCA
- Image Colorization using Autoencoders
- Colorful Face Generation with VAEs
