Here are 25 core mathematical concepts that form the foundation of every ML/AI project, explained simply: 𝟭 ⤷ Mean (μ) The average of a set of values. Measures central tendency. 𝟮 ⤷ Median The middle value when data is sorted. Useful for skewed distributions. 𝟯 ⤷ Mode The value that appears most frequently in a dataset. 𝟰 ⤷ Variance (σ²) Measures how spread out the values are from the mean. 𝟱 ⤷ Standard Deviation (σ) The square root of variance shows typical deviation from the mean. 𝟲 ⤷ Skewness Measures asymmetry of the distribution. Right skew = long tail on the right. 𝟳 ⤷ Kurtosis Measures peakedness of a distribution. High kurtosis = heavy tails. 𝟴 ⤷ Probability Likelihood of an event occurring, between 0 and 1. 𝟵 ⤷ Conditional Probability P(A|B): Probability of A given B has occurred. 𝟭𝟬 ⤷ Bayes’ Theorem P(A|B) = [P(B|A) × P(A)] / P(B). Key to probabilistic inference in ML. 𝟭𝟭 ⤷ Random Variable A variable whose values are outcomes of a random process. 𝟭𝟮 ⤷ Distribution Describes how values of a random variable are spread. (e.g. Normal, Binomial) 𝟭𝟯 ⤷ Expectation (E[X]) The average value a random variable would take over many trials. 𝟭𝟰 ⤷ Covariance Measures how two variables change together. 𝟭𝟱 ⤷ Correlation Normalized covariance. Range: [-1, 1]. 1 = perfect positive, -1 = perfect negative. 𝟭𝟲 ⤷ Entropy Measure of uncertainty in information theory. Higher entropy = more unpredictable. 𝟭𝟯 ⤷ Gradient Vector of partial derivatives. Shows direction of steepest increase. 𝟭𝟴 ⤷ Loss Function Quantifies how wrong a model’s prediction is (e.g. MSE, Cross-Entropy). 𝟭𝟵 ⤷ Cost Function The average loss over the entire dataset. 𝟮𝟬 ⤷ Optimization Process of minimizing cost using algorithms like Gradient Descent. 𝟮𝟭 ⤷ Overfitting Model performs well on training data but fails on new data. 𝟮𝟮 ⤷ Underfitting Model is too simple and fails to learn patterns. 𝟮𝟯 ⤷ Bias Error from wrong assumptions in the learning algorithm. 𝟮𝟰 ⤷ Variance Error from model sensitivity to training data fluctuations. 𝟮𝟱 ⤷ Regularization Adds penalty to the loss function to reduce overfitting (L1, L2). — 𝐓𝐋;𝐃𝐑: ⤷ You can’t build solid AI/ML projects without core mathematical intuition ⤷ Learn these 25 concepts to speak the language of data #datascience - @techlao