AI Core Machine Learning Skills

Core machine learning skills are essential for developing, understanding,

and applying machine learning algorithms and models. Here’s an in-depth look at these skills:

Understanding Machine Learning Algorithms
Supervised Learning
Linear Regression:

Used for predicting continuous outcomes.
Skills: Understanding of least squares, gradient descent, and model evaluation metrics like R-squared.
Logistic Regression:

Used for binary classification problems.
Skills: Understanding of the logistic function, decision boundaries, and metrics like accuracy, precision, recall, and F1 score.
Decision Trees and Random Forests:

Used for classification and regression tasks.
Skills: Knowledge of tree construction, splitting criteria (Gini impurity, entropy), overfitting prevention (pruning, max depth), and ensemble methods.
Support Vector Machines (SVM):

Used for classification tasks.
Skills: Understanding of the concept of hyperplanes, kernel functions, and regularization.
Unsupervised Learning
K-Means Clustering:

Used for grouping similar data points into clusters.
Skills: Understanding of distance metrics, centroid initialization, and evaluation metrics like the silhouette score.
Hierarchical Clustering:

Used for creating a hierarchy of clusters.
Skills: Knowledge of agglomerative and divisive methods, linkage criteria, and dendrogram interpretation.
Principal Component Analysis (PCA):

Used for dimensionality reduction.
Skills: Understanding of covariance matrices, eigenvalues, eigenvectors, and variance explanation.
Reinforcement Learning
Basics of Reinforcement Learning:
Used for learning optimal actions through trial and error.
Skills: Understanding of agents, environments, reward signals, policies, value functions, and exploration vs. exploitation.