100 Days Of Machine Learning

ML Project(Toy Dataset)

• End-To-End Toy ML Project
• Web App Code

Data Gathering

• Getting Data From CSV Files
• Getting Data From Database and JSON
• Getting Data From API

Understanding Your Data

• Understanding Your Data using EDA(univariate Bi-Variate and Multi-Variate Analysis)

Feature Engnering

• Handling Missing Values

  • Handling Missing Data

• Handling Outliers

  • z-Score Method
  • IQR Method
  • Winserization Method

• Feature Scaling

  • Standard Scaling)
  • Custom Scaler Class)
  • Normilization(MinMaxScaling)
  • Custom MinMaxScaler)
  • Roboust Scaling)

• Encoding Categorical Data

  • Ordinal Encoder)
  • One Hot Encoder)
  • Lanbel Encoder It is commonly used for encoding the target col if target col is categorical.

• Feature Construction

  • In Feature Construction we can construct new feature manually based on domain knowledge.
  • Feature Construction

• Feature Extraction

  • Principal Component Analysis (PCA)

• Feature Selection

  • pass

Transformations

• Column Transformer
  • Column Transformer)
• Function Transformation
  • Function Transformation

ML-Pipeline Project

• End-To-End-Project using Pipeline and without pipeline)
• Web App)

Handling Imbalance Data

• UnderSamoling

Matricss

• Classification Matrics
  • Accuracy ,confusion matrics,Precession,Recall and f1-score )
• Regression Matrics
  • MAE,MSE,RMSE,R2-Score,Adjusted-R2-Score

Algorithmns

• Simple Linear Regression

  • Simple Linear Rregression
  • Custom Simple Linear Rregression

• Logistic Regression

  • Perceptron Trick(step_fun)
  • Perceptron Trick(sigmoid fun)
  • LogisticRegression(GradientDecent)
  • SoftMax Regression
  • SoftMax Regression On MNIST

• Logistic Regression HyperParameter

  • LogisticRegression Hyperparameter

• Voting-Ensumble

  • Voting-Classifier
  • Voting-Regressor

• Bagging-Ensumble

  • Bagging-Classifier
  • Bagging-Regressor

• RandomForest

  • RandomForestClassifier
  • FeatureImportance

• AdaBoost

  • AdaBoostRegreessor

• Clustring

  • K_mean Clustring

Regulerization

• Ridge Regression
  • Ridge(L2)
• Lasso Regression
  • Lasso(L1)
• Elestic-Net
  • Elestic-Net