Introduction

Data mining is a powerful process that uncovers patterns, correlations, and insights from large datasets, enabling businesses and researchers to make informed decisions and predictions. At the heart of data mining are various algorithms, each tailored to solve specific types of problems—ranging from predicting outcomes to discovering hidden structures within data.

1. Regression & Classification

Learn the foundational algorithms like Linear and Logistic Regression, along with advanced concepts such as Vectorization and Stochastic Gradient Descent.

  • Linear
  • Multivariate Linear
  • Logistic
  • Softmax
  • Vectorization
  • Gradient Calculation
  • Stochastic Gradient Descent (SGD)
  • Optimizers and Objectives

2. Regularization

Explore techniques like Ridge Regression that help prevent overfitting in models.

  • Ridge regression

3. Clustering

Discover how algorithms like k-Means and EM Algorithms group similar data points together.

  • k – Means
  • EM Algorithms

4. Unsupervised Learning

Dive into methods such as Autoencoders and PCA Whitening that find structure in unlabeled data.

  • Autoencoders
  • PCA Whitening
  • Sparse coding

5. Neural Network

Understand the basics of Perceptrons and Backpropagation, as well as more complex models like Restricted Boltzmann Machines.

  • Perceptrons
  • Backpropagation
  • Restricted Boltzmann Machines
  • Learning Vector Quantization

6. Deep Learning

Gain insights into advanced techniques like Convolutional Neural Networks and Deep Belief Networks, which are at the forefront of AI research.

  • Stacked Autoencoders
  • Convolution Neural Networks (Feature Extraction, Pooling)
  • Deep Boltzmann Machines
  • Deep Belief Networks

7. Decision Trees

Learn how tree-based models like ID3, C4.5, and Random Forests make decisions based on data.

  • ID3
  • C4.5
  • CART (Classification and regression tree)
  • Random Forests

8. Bayesian

Explore probabilistic models such as Naïve Bayes and Bayesian Networks, which incorporate uncertainty into predictions.

  • Naïve Bayes
  • Gaussian Naïve Bayes
  • Bayesian Networks
  • Conditional Random Fields
  • Hidden Markov Models

9. Others

Get acquainted with Support Vector Machines, Reinforcement Learning, and other pivotal algorithms that don’t fit neatly into the previous categories.

  • Support Vector Machines
  • Evolutionary Methods
  • Reinforcement Learning
  • Conditional Random Fields

10. Dimensionality Reduction

Understand PCA and other techniques that reduce the complexity of data without losing important information.

  • PCA

11. Ensemble Methods

Learn how Boosting, Bagging, and Adaboost combine multiple models to improve prediction accuracy.

  • Boosting
  • Bagging
  • Adaboost