The most important thing while learning the machine learning concepts is its mathematical intuition .We come across many algorithms while exploring the field of machine learning.

There are 2 types of machine learning techniques by which we try to predict the output of the given dataset.

1. Supervised Machine learning

2. Unsupervised Machine Learning

   

Supervised Machine Learning

In simple terms, supervised machine learning is like teaching a computer to learn from examples. You give it a bunch of data where you already know the right answers, and the computer figures out the patterns or rules in that data to make predictions or decisions when it sees new data it hasn't seen before. It's like training a pet - you show it what to do and what not to do, and it learns from those examples.

Regression and Classification are the Two types into which supervised learning is divided.

The Algorithms in Supervised Learning are :

1. Linear Regression

2. Ridge and Lasso

3. Logistic Regression

4. Decision Tree

5. Adaboost

6. Random Forest

7. Gradient Boosting

8. XgBoost

9. Naive Baye's

10. Support Vector Machines

11. K-Nearest Neighbour

UnSupervised Machine Learning

Unsupervised machine learning is like exploring a new city without a map or guide. You're just wandering around and noticing similarities or differences between things you see.

In unsupervised learning, the computer is given a bunch of data without any specific instructions on what to do with it. It has to figure out any patterns or structures on its own. It's like giving the computer a pile of puzzle pieces without showing it the picture on the box. It has to sort them out and find connections by itself.

Unsupervised learning models makes use of Clustering techniques to build the model.

The Clustering Algorithms in UnSupervised Learnings are:

1. K-Mean

2. DBSCAN

3. Hierarchical

4. K-Neigherest Neighbour

5. PCA [Principal component analysis]

6. LDA [Linear discriminant analysis]