Master the Classification and Regression

Easily learn an End to End data science process and steps in two days!

📚Contents:
✅Exploratory data analysis and visualization
Numeric descriptive statistics
Interpreting descriptive statistics
Understanding the distribution
Histograms
Boxplots and IQR
Correlation
heatmaps for co-relation
Analyzing the target variable
✅Pre-processing data
Dealing with missing values
Treatment of categorical values
Normalize the data
Split the data
✅ #MachineLearning algorithm
Defining/instantiating the model
Fitting the model
Define the evaluation metric
Predict scores against our test set and assess
✅Evaluation metrics for classification
Improving a model – from baseline models to final models
Understanding cross-validation
Feature engineering
#Regularization to prevent overfitting
#Ensembles – typically for classification
Test alternative models
#Hyperparameter tuning
✅Regression #Code
✅Classification #Code

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Bayesian Optimization with Binary Auxiliary Information
https://deepai.org/publication/bayesian-optimization-with-binary-auxiliary-information … by Yehong Zhang et al.
#ReinforcementLearning #Hyperparameter

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