Statistics for Machine Learning

  1. Statistics

    The science of collecting, analyzing, interpreting, presenting, and organizing data.

  2. Probability Distributions

    Mathematical functions describing the likelihood of different outcomes in random experiments.

  3. Descriptive Statistics

    Techniques for summarizing datasets using measures such as mean, median, variance, and standard deviation.

  4. Central Limit Theorem (CLT)

    A fundamental theorem stating that sample means tend to follow a normal distribution as sample size increases.

  5. Inferential Statistics

    Methods used to draw conclusions about populations based on sample data.

  6. Hypothesis Testing

    A formal framework to test assumptions about population parameters using statistical evidence.

  7. t-Test

    A statistical test used to compare the means of two groups.

  8. ANOVA Test

    Analysis of Variance used to compare means across three or more groups.

  9. Non-Parametric Tests

    Statistical tests that do not assume a specific data distribution.

  10. Statistical Modeling

    Building mathematical models to represent and analyze real-world data.

Machine Learning

  1. Fundamentals of ML

    Core principles of machine learning including learning paradigms, feature selection, model evaluation, and overfitting.

  2. Supervised Algorithms

    Algorithms trained on labeled data such as linear regression, decision trees, and support vector machines.

  3. Unsupervised Algorithms

    Techniques that discover hidden patterns in unlabeled data, such as clustering and dimensionality reduction.

  4. Reinforcement Learning

    Learning framework where an agent interacts with an environment to maximize cumulative reward.

  5. Support Vector Machine

    A powerful algorithm for classification and regression that finds the optimal hyperplane maximizing the margin between classes.

  6. Gradient Descent Method

    An optimization technique used to minimize the loss function by iteratively adjusting model parameters in the direction of the steepest descent.

  7. MLE & MAP

    MLE (Maximum Likelihood Estimation) estimates model parameters by maximizing the likelihood function, while MAP (Maximum A Posteriori) incorporates prior distributions.

  8. Polynomial Regression

    An extension of linear regression that models relationships as an nth-degree polynomial.

  9. Ridge, Lasso & Elastic Net

    Regularization techniques used to prevent overfitting and improve model performance.

  10. Principal Component Analysis (PCA)

    A dimensionality-reduction technique that transforms data into orthogonal components.

  11. Classification & Regression

    Methods for classifying data into categories and predicting continuous values.

  12. Naive Bayes

    A probabilistic classifier based on Bayes' theorem with an assumption of feature independence.

  13. K-Nearest Neighbors (KNN)

    A simple, non-parametric algorithm used for classification and regression based on proximity.

  14. Decision Tree

    A tree-based model that represents decisions and their possible consequences for classification and regression.

  15. Support Vector Machine

    Support Vector Machines find the optimal hyperplane that maximizes the margin between classes for classification and regression.

  16. Logistic Regression

    A classification algorithm used for binary outcomes, predicting probabilities based on the logistic function.

Deep Learning

  1. Optimization Methods

    Techniques used to find optimal solutions by minimizing or maximizing objective functions, widely used in deep learning.

  2. Deep Learning

    Neural networks with many layers that learn hierarchical representations from large datasets.

  3. Activation Functions

    Functions that control neuron activation and enable neural networks to model complex non-linear relationships.

  4. Convolutional Neural Networks (CNNs)

    Neural networks designed for visual data that learn spatial hierarchies using convolutional layers.

  5. Ensemble Learning

    Techniques that combine predictions from multiple models to improve robustness and overall performance.

Natural Language Processing (NLP) & Artificial Intelligence (AI)

  1. Natural Language Processing (NLP)

    Techniques for analyzing and understanding human language data, enabling meaningful interaction between humans and machines.

Applications

  1. Data Mining

    The process of extracting meaningful patterns and insights from large datasets using statistical and machine learning techniques.

References

Repositories

Books

  • Machine Learning Using Python — Manarajan Pradhan, U. Dinesh Kumar
  • Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow — Aurélien Géron
  • Statistics — Murray R. Spiegel, Larry J. Stephens

Tutorials & Learning Paths

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