Lesson 1, Section 2: Introduction to Machine Learning
Types of machine learning: Continuing from the previous point, let’s explore supervised learning and unsupervised learning in more detail.
A. Supervised learning: Supervised learning algorithms aim to learn a mapping between input features (X) and corresponding labels or targets (y) from labeled training data. Here are some key concepts related to supervised learning:
i. Classification: In classification tasks, the goal is to predict a discrete label or category for a given set of input features. For instance, classifying whether an email is spam or not, or determining the sentiment of a customer review (positive, negative, or neutral). Common algorithms for classification include logistic regression, support vector machines (SVM), and decision trees.
ii. Regression: Regression tasks involve predicting a continuous value or numeric quantity. Examples include predicting housing prices based on features like size, number of bedrooms, and location, or forecasting stock prices. Linear regression, decision trees, and random forests are commonly used regression algorithms.
B. Unsupervised learning: Unsupervised learning algorithms operate on unlabeled data, where there are no corresponding labels or targets available. The goal is to discover patterns, structures, or relationships within the data. Here are some key concepts related to unsupervised learning:
i. Clustering: Clustering algorithms aim to group similar instances together based on the inherent structure or similarity within the data. This can help in discovering natural groupings or segments within the dataset. K-means clustering, hierarchical clustering, and DBSCAN (Density-Based Spatial Clustering of Applications with Noise) are commonly used clustering algorithms.
ii. Dimensionality reduction: Dimensionality reduction techniques are used to reduce the number of input features while retaining important information. This can be helpful when working with high-dimensional data or reducing computational complexity. Principal Component Analysis (PCA) and t-SNE (t-Distributed Stochastic Neighbor Embedding) are popular dimensionality reduction methods.
iii. Association rule mining: Association rule mining algorithms aim to discover interesting relationships or associations between different items in a dataset. This is commonly used in market basket analysis to identify frequent itemsets or patterns. Apriori and FP-growth are popular association rule mining algorithms.
Understanding the differences between supervised and unsupervised learning will help you choose the appropriate approach based on the task and the availability of labeled data.
Next, we can explore other topics such as model evaluation, feature engineering, and popular libraries for machine learning.