Machine Learning

Phase of an Machine Learning model build;

1. Gather data from a data source (e.g databases,csv file)
2. Apply EDA on the data and preprocess if needed .
3. Create an ML model according to needs and problem.
4. Feed data into model.
5. Turne the hyperparamiters in need.

ML Terminology

1. Future:is the information that used for prediction by our model.
2. Predictied Value: prediction made by our model.
3. Target Or Label: Real value of an example

How ML work?

DATA — — -MODEL — — PREDICT — — A)Succesful Prediction B) Failed Prediction

WHERE CAN YOU USE ML?

• Detecting spam e-mails
• Personalized ADS
• Disease detection & Diagnose
• Voice Asistans
• Autonomous Vehicles

ML PROBLEM TYPES

1. Regression;

Simple Linear Regression: y=b0 + b1*x1

y=Predicted value

X1=Features

Multiple Linear Regression:y=bo +b1.x1+b2.x2+……..bx.xn

y=dependent value(target variable)

x1,x2….Xn =İndependent variable,features

REAL LIFE EXAMPLE:Predict hause Price value

y=wx +b

y=label,target(price)

w=weight of x

x=feature(squarmeter)

b=bias

Non-Linear Polynomial Regression:

y=a.(x**3) +b

Evaluation of A Linear Regression Model:

Mean Squared Error (MSE)

Mean Absolute Error: (MAE):

Underfitting / Overfitting & Good Fit,robust

Loss Reduction Method (train-validation split)

Early Stopping:

Gradient Descent Algorithm

Repeat Until Convergence:

Classification:

Logistics Regression:

Conflusion Matrix:

Accuracy,precision,recall

Classification Threshold:

ROC Curve:

Splitting Gini:

Decision Trees: