Machine learning (ML) is a branch of artificial intelligence that empowers computers to learn from data and improve over time without being explicitly
... [Show More] programmed. In supervised learning, the model is trained on labeled data, while in unsupervised learning, the model identifies patterns in unlabeled data. Naive Bayes is a simple probabilistic classifier, while Support Vector Machines (SVM) excel in classification and regression tasks. Decision Trees are versatile algorithms for both classification and regression, and Association Rules are used for discovering relationships in large datasets. These concepts form the foundation of understanding and applying machine learning techniques in various real-world scenarios.
Introduction to Machine Learning (Lec 1):
Machine Learning is a branch of artificial intelligence that empowers computers to learn from data and improve over time without being explicitly programmed. It enables systems to automatically learn and make predictions or decisions based on input data. In this introductory lecture, we delve into the fundamentals of machine learning, discussing its types, namely supervised and unsupervised learning.
Supervised Learning (Lec 2):
Supervised learning is a type of machine learning where the model is trained on labeled data, meaning each input data point is paired with a corresponding target label. The algorithm learns from this labeled data to make predictions or decisions when new data is presented. In this lecture, we explore various supervised learning algorithms and their applications.
Naive Bayes (Lec 3):
Naive Bayes is a simple but effective probabilistic classifier based on applying Bayes' theorem with strong independence assumptions between the features. It is commonly used for text classification, spam filtering, and recommendation systems. In this lecture, we dive into the Naive Bayes algorithm, understanding its principles and how it can be applied in real-world scenarios.
Support Vector Machines (SVM) (Lec 4):
Support Vector Machines (SVM) is a powerful supervised learning algorithm used for classification and regression tasks. SVM works by finding the hyperplane that best separates the classes in the feature space. It is particularly effective in high-dimensional spaces and is widely used in applications such as image classification and bioinformatics. In this lecture, we explore the workings of SVM and its practical implementations.
Decision Trees (Lec 5):
Decision Trees are versatile supervised learning algorithms used for classification and regression tasks. They work by recursively partitioning the input space into regions and assigning a label or value to each region based on the majority class or average value of the training data within that region. Decision Trees are intuitive, easy to interpret, and can handle both numerical and categorical data. In this lecture, we delve into the structure of decision trees and how they are constructed and utilized.
Association Rules (Lec 6):
Association Rules are a technique used in unsupervised learning for discovering interesting relationships between variables in large datasets. It is commonly employed in market basket analysis to identify patterns in consumer behavior, such as which products are frequently bought together. In this lecture, we explore the concept of association rules, understand how they are generated, and discuss their applications in various domains. [Show Less]