- Machine Learning Basics
- Machine Learning - Home
- Machine Learning - Getting Started
- Machine Learning - Basic Concepts
- Machine Learning - Python Libraries
- Machine Learning - Applications
- Machine Learning - Life Cycle
- Machine Learning - Required Skills
- Machine Learning - Implementation
- Machine Learning - Challenges & Common Issues
- Machine Learning - Limitations
- Machine Learning - Reallife Examples
- Machine Learning - Data Structure
- Machine Learning - Mathematics
- Machine Learning - Artificial Intelligence
- Machine Learning - Neural Networks
- Machine Learning - Deep Learning
- Machine Learning - Getting Datasets
- Machine Learning - Categorical Data
- Machine Learning - Data Loading
- Machine Learning - Data Understanding
- Machine Learning - Data Preparation
- Machine Learning - Models
- Machine Learning - Supervised
- Machine Learning - Unsupervised
- Machine Learning - Semi-supervised
- Machine Learning - Reinforcement
- Machine Learning - Supervised vs. Unsupervised
- Machine Learning Data Visualization
- Machine Learning - Data Visualization
- Machine Learning - Histograms
- Machine Learning - Density Plots
- Machine Learning - Box and Whisker Plots
- Machine Learning - Correlation Matrix Plots
- Machine Learning - Scatter Matrix Plots
- Statistics for Machine Learning
- Machine Learning - Statistics
- Machine Learning - Mean, Median, Mode
- Machine Learning - Standard Deviation
- Machine Learning - Percentiles
- Machine Learning - Data Distribution
- Machine Learning - Skewness and Kurtosis
- Machine Learning - Bias and Variance
- Machine Learning - Hypothesis
- Regression Analysis In ML
- Machine Learning - Regression Analysis
- Machine Learning - Linear Regression
- Machine Learning - Simple Linear Regression
- Machine Learning - Multiple Linear Regression
- Machine Learning - Polynomial Regression
- Classification Algorithms In ML
- Machine Learning - Classification Algorithms
- Machine Learning - Logistic Regression
- Machine Learning - K-Nearest Neighbors (KNN)
- Machine Learning - Naïve Bayes Algorithm
- Machine Learning - Decision Tree Algorithm
- Machine Learning - Support Vector Machine
- Machine Learning - Random Forest
- Machine Learning - Confusion Matrix
- Machine Learning - Stochastic Gradient Descent
- Clustering Algorithms In ML
- Machine Learning - Clustering Algorithms
- Machine Learning - Centroid-Based Clustering
- Machine Learning - K-Means Clustering
- Machine Learning - K-Medoids Clustering
- Machine Learning - Mean-Shift Clustering
- Machine Learning - Hierarchical Clustering
- Machine Learning - Density-Based Clustering
- Machine Learning - DBSCAN Clustering
- Machine Learning - OPTICS Clustering
- Machine Learning - HDBSCAN Clustering
- Machine Learning - BIRCH Clustering
- Machine Learning - Affinity Propagation
- Machine Learning - Distribution-Based Clustering
- Machine Learning - Agglomerative Clustering
- Dimensionality Reduction In ML
- Machine Learning - Dimensionality Reduction
- Machine Learning - Feature Selection
- Machine Learning - Feature Extraction
- Machine Learning - Backward Elimination
- Machine Learning - Forward Feature Construction
- Machine Learning - High Correlation Filter
- Machine Learning - Low Variance Filter
- Machine Learning - Missing Values Ratio
- Machine Learning - Principal Component Analysis
- Machine Learning Miscellaneous
- Machine Learning - Performance Metrics
- Machine Learning - Automatic Workflows
- Machine Learning - Boost Model Performance
- Machine Learning - Gradient Boosting
- Machine Learning - Bootstrap Aggregation (Bagging)
- Machine Learning - Cross Validation
- Machine Learning - AUC-ROC Curve
- Machine Learning - Grid Search
- Machine Learning - Data Scaling
- Machine Learning - Train and Test
- Machine Learning - Association Rules
- Machine Learning - Apriori Algorithm
- Machine Learning - Gaussian Discriminant Analysis
- Machine Learning - Cost Function
- Machine Learning - Bayes Theorem
- Machine Learning - Precision and Recall
- Machine Learning - Adversarial
- Machine Learning - Stacking
- Machine Learning - Epoch
- Machine Learning - Perceptron
- Machine Learning - Regularization
- Machine Learning - Overfitting
- Machine Learning - P-value
- Machine Learning - Entropy
- Machine Learning - MLOps
- Machine Learning - Data Leakage
- Machine Learning - Resources
- Machine Learning - Quick Guide
- Machine Learning - Useful Resources
- Machine Learning - Discussion
Machine Learning - Mathematics
Machine learning is an interdisciplinary field that involves computer science, statistics, and mathematics. In particular, mathematics plays a critical role in developing and understanding machine learning algorithms. In this article, we will discuss the mathematical concepts that are essential for machine learning, including linear algebra, calculus, probability, and statistics.
Linear Algebra
Linear algebra is the branch of mathematics that deals with linear equations and their representation in vector spaces. In machine learning, linear algebra is used to represent and manipulate data. In particular, vectors and matrices are used to represent and manipulate data points, features, and weights in machine learning models.
A vector is an ordered list of numbers, while a matrix is a rectangular array of numbers. For example, a vector can represent a single data point, and a matrix can represent a dataset. Linear algebra operations, such as matrix multiplication and inversion, can be used to transform and analyze data.
Calculus
Calculus is the branch of mathematics that deals with rates of change and accumulation. In machine learning, calculus is used to optimize models by finding the minimum or maximum of a function. In particular, gradient descent, a widely used optimization algorithm, is based on calculus.
Gradient descent is an iterative optimization algorithm that updates the weights of a model based on the gradient of the loss function. The gradient is the vector of partial derivatives of the loss function with respect to each weight. By iteratively updating the weights in the direction of the negative gradient, gradient descent tries to minimize the loss function.
Probability
Probability is the branch of mathematics that deals with uncertainty and randomness. In machine learning, probability is used to model and analyze data that are uncertain or variable. In particular, probability distributions, such as Gaussian and Poisson distributions, are used to model the probability of data points or events.
Bayesian inference, a probabilistic modeling technique, is also widely used in machine learning. Bayesian inference is based on Bayes' theorem, which states that the probability of a hypothesis given the data is proportional to the probability of the data given the hypothesis multiplied by the prior probability of the hypothesis. By updating the prior probability based on the observed data, Bayesian inference can make probabilistic predictions or classifications.
Statistics
Statistics is the branch of mathematics that deals with the collection, analysis, interpretation, and presentation of data. In machine learning, statistics is used to evaluate and compare models, estimate model parameters, and test hypotheses.
For example, cross-validation is a statistical technique that is used to evaluate the performance of a model on new, unseen data. In cross-validation, the dataset is split into multiple subsets, and the model is trained and evaluated on each subset. This allows us to estimate the performance of the model on new data and compare different models.