There are mainly three types of algorithms namely: Supervised, Unsupervised and Reinforcement.
Supervised Machine Learning means that the algorithm is first trained on the labelled dataset (train data) to form a model. After this, the machine is provided with a new set of data (test data) to the acquired knowledge to predict the outcomes.
Unsupervised learning is a type of machine learning algorithm used to draw inferences from datasets consisting of input data without labeled responses. The most common unsupervised learning method is cluster analysis, which is used for exploratory data analysis to find hidden patterns or grouping in data.
During my internships and online courses , I learned various ML algorithms and implemented them on well-known datasets. These algorithms include both supervised and unsupervised algorithms.
The following are the links to the code of those algorithms :
1. Linear Regression on Bitcoin Data:
2. Using Decision Trees with Iris Dataset:
3. K-Means Clustering with Iris Dataset:
4. Convolutional Neural Network using Fashion MNIST:
5. Text Classification on IMDb Dataset:
6. Back-Propagation from Scratch using XOR dataset:
7. Convolutional Neural Network using MNIST:
8. Neural Network using Breast Cancer Data:
9. Neural Network using Iris Data: