Description:

Machine Learning is a comprehensive course that introduces students to the principles and applications of machine learning. Throughout this course, students will gain a deep understanding of various machine learning algorithms and techniques.

Key Highlights:

• Explore the fundamentals of machine learning
• Learn about different types of machine learning algorithms
• Evaluate machine learning models

What you will learn:

• Introduction to Machine Learning
  In this module, you will get an overview of machine learning and its applications. You will also learn about supervised and unsupervised learning techniques.
• Regression Analysis
  This module covers the concept of regression analysis and its various algorithms, such as linear regression and decision tree regression.
• Classification Algorithms
  Here, you will learn about classification algorithms, including logistic regression, decision trees, and support vector machines.
• Clustering
  In this module, you will explore clustering algorithms such as K-means clustering and hierarchical clustering.
• Dimensionality Reduction
  This module focuses on dimensionality reduction techniques like Principal Component Analysis (PCA).

Topics Covered:

• Supervised Learning: regression and classification problems, simple linear regression, multiple linear regression, ridge regression, logistic regression, k-nearest neighbour, naive Bayes classifier, linear discriminant analysis, support vector machine, decision trees, bias variance trade-off, cross-validation methods such as leave-one-out (LOO) cross-validation, k-folds cross-validation, multi-layer perceptron, feed-forward neural network;
• Unsupervised Learning: clustering algorithms, k-means/k-medoid, hierarchical clustering, top-down, bottom-up: single-linkage, multiple-linkage, dimensionality reduction, principal component analysis.