This contains basic template for machine learning topics, properly structured and updated. So stay tuned always . Thank you for checking me out. God Bless you :)

Me.... I'm Swasthika Present Intrests - Blockchain Technology, IOT, ML, Web or Mobile App development, . Open to any new tech and learning........... ""SO MAIL OR PING ME ON INSTA" Other Intrests - Team Building, Self development Books, Sing and Dance, Life Skills. Happy Learning Darling I'm a coding nightmare dressed like day dream!

A Shark 🦈. Mail me at - [email protected] or [email protected]

Mail me if you need any help or want me to explain any part of my code.

Don't worry we are here to learn and its my pleasure.

God bless you.

Templates for topics like : (Topics keep on adding)

In the file template.py and its ipynb we have:

Data Preprocessing Template

Importing the libraries

Importing the dataset

Taking Care Of Missing Data

Encoding Categorial data

Splitting the dataset into the Training set and Test set

Feature Scaling

Regression

Simple Linear Regression model

Multiple Linear Regression

Polynomial Regression

SUPPORT VECTOR REGRESSION (SVR)

Decision Tree Regression

Random Forest Regression

R Squared Intuition and Adjusted R^2

Which Regression Model to use tool on any given data set

CLASSIFICATION

Logistic Regression Model

Check on udemy for free course named - Logistic Regression Practical Case Study :)

KNN ALGORITHM

Support Vector Machine (SVM)

Kernel SVM MODEL

Naive Bayes

Decision Tree

Random Forest Classification

Best Model / Max Efficiency

Clustering

K- Means Clustering

Hierarchial Clustering

Association Rule Learning

Apriori

Eclat

Reinforcement

Upper Confidence Bound

Thompson Sampling

Natural Language Processing

Deep Learning

Artificial Neural Networks

Convolutional Neural Networks

Dimensionality Reduction

Principal Component Analysis

Linear Discriminant Analysis

Kernal PCA

Model Selection AND Boosting

K-Fold Cross Validation

Grid Search

XGBoost