Introduction :

Some of the most significant technologies we employ, such as driverless vehicles,translation applications,recommendation systems, medical imaging equipment and many more are powered by machine learning (ML). 

ML offers a fresh approach to resolving issues and responding to difficult queries.

In its simplest form, machine learning (ML) is the act of teaching a computer program, known as a model, to make useful predictions from data.

The mathematical relationship between the data pieces that an ML system utilizes to create predictions is represented by an ML model.

 

 

 

 

 

---- Summary ----

As of now you know all basics of Protein Structures.

• Central Dogma of life.

• Amino acids and it's types.

• Peptide bonds and torsion angles.

• Ramachandran Plots and protein secondary structures.

• etc..

Content

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Introduction

Linear regression

Gradient Descent 

Scaling

Imputation

Imbalancing

PCA

Multicollinearity

Cross Validation

GridSearchCV

Pipeline Building

SVM

Logistic Regression

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Decision Trees

Ensemble Methods

Ramdom Forest

GradientBoost

Loss Function

A Demonstration