ML Algorithm from Scratch

Machine Learning is a branch of Artificial intelligence that focuses on the development of algorithms and statistical models that can learn from and make predictions on data.

• Supervised Machine Learning Algorithms is a type of machine learning where the algorithm learns from labelled data. Labeled data means the dataset whose respective target value is already known. Supervised learning has two types:

  1. Classification: It predicts the class of the dataset based on the independent input variable. Class is the categorical or discrete values. like the image of an animal is a cat or dog?
  2. Regression: It predicts the continuous output variables based on the independent input variable. like the prediction of house prices based on different parameters like house age, distance from the main road, location, area, etc.

• Unsupervised machine learning is a type of machine learning where the algorithm is tasked with discovering patterns, relationships, or structures within a dataset that is not labeled. Unlike supervised learning, unsupervised learning works with unlabeled data, meaning there are no predefined target values for the algorithm to learn from. Unsupervised learning can be broadly categorized into two main types:

  1. Clustering: Groups similar data points to identify natural patterns, applied in customer segmentation, document clustering, and image segmentation.
  2. Dimensionality Reduction: Reduces features while preserving information, utilizing techniques like PCA and t-SNE for visualization, feature extraction, and enhanced computational efficiency.

---

Regression:

• Linear Regression
• Decision Trees
• Random Forest
• Support Vector Machine

---

Classification:

• Logistic Regression
• Decision Trees
• KNN (K-Nearest Neighbors)
• Naive Bayes
• Perceptron
• Random Forest
• Support Vector Machine

---

Clustering:

• KMeans

---

Dimensionality Reduction:

• Principal Component Analysis (PCA)

---