Welcome to machine learning!

Machine learning (ML) is the study of computer algorithms that improve automatically through experience. It is seen as a subset of artificial intelligence. Machine learning algorithms build a mathematical model based on sample data, known as "training data", in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as email filtering and computer vision, where it is difficult or infeasible to develop conventional algorithms to perform the needed tasks.

Machine learning is closely related to computational statistics, which focuses on making predictions using computers. The study of mathematical optimization delivers methods, theory and application domains to the field of machine learning. Data mining is a related field of study, focusing on exploratory data analysis through unsupervised learning. In its application across business problems, machine learning is also referred to as predictive analytics.

Topics

1. Data preprocessing

2. Regression

Simple Linear Regression

Multiple Linear Regression

Polynomial Regression

Support Vector for Regression (SVR)

Decision Tree Regression

Random Forest Regression

3. Classification

Logistic Regression

K-Nearest Neighbors (K-NN)

Support Vector Machine (SVM)

Kernel SVM

Naive Bayes

Decision Tree Classification

Random Forest Classification

4. Clustering

Hierarchical Clustering

K-means Clustering

5. Association Rule Learning

Apriori

Eclat

6. Reinforcement Learning

Upper Confidence Bound

Thompson Sampling

7. Natural Language Processing

8. Deep Learning

Artificial-Neural-Networks-(ANN)

Convolutional-Neural-Networks-(CNN)

9. Dimensionality-Reduction

Kernel-PCA

Linear-Discriminant-Analysis

Principal-Component-Analysis

10. Model-Selection-and-Boosting

Model Selection

XGBoost