Lesson 1, Section 4: Introduction to Machine Learning
Unsupervised learning:
Unsupervised learning is a type of machine learning where the training data consists of unlabeled examples, meaning there are no corresponding output labels available. The goal of unsupervised learning is to discover patterns, structures, or relationships within the data. Let’s explore unsupervised learning in more detail:
A. Clustering: Clustering is a common task in unsupervised learning where the goal is to group similar instances together based on the inherent structure or similarity within the data. Clustering algorithms aim to identify natural clusters or segments within the dataset. Some popular clustering algorithms include K-means clustering, hierarchical clustering, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise).
B. Dimensionality reduction: Dimensionality reduction techniques are used to reduce the number of input features while retaining important information. High-dimensional data can be computationally expensive to process, and some features may be redundant or noise. Dimensionality reduction methods aim to capture the most relevant information in a lower-dimensional space. Principal Component Analysis (PCA) and t-SNE (t-Distributed Stochastic Neighbor Embedding) are commonly used dimensionality reduction techniques.
C. Association rule mining: Association rule mining is a technique used to discover interesting relationships or associations between different items in a dataset. It is commonly used in market basket analysis, where the goal is to identify frequently occurring itemsets or patterns. Apriori and FP-growth are popular association rule mining algorithms.
Unsupervised learning allows us to explore and uncover hidden patterns or structures in the data without prior knowledge of the output labels. It can be useful for tasks such as customer segmentation, anomaly detection, recommender systems, and data exploration.
It’s worth noting that unsupervised learning can also be used in combination with supervised learning. For example, unsupervised learning techniques like clustering or dimensionality reduction can be applied as a preprocessing step to extract meaningful features that can improve the performance of supervised learning models.
In the next part of the lesson, we can explore other topics such as model evaluation, then on to overfitting & underfitting, feature engineering, and popular libraries for machine learning.