Fundamentals of Machine Learning: A Pythonic Introduction

Description

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