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# System prepended metadata

title: Security Labs
tags: [Intela]

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tags: Intela
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# Security Labs

## Level 1: Preliminary data analysis of dataset

The purpose of this lab is to make the preliminary data analysis in security area based on credit card clients dataset with the helping frameworks & libraries.

### Learning Objectives

After completing this lab you will

1. Be able to explore the credit card clients dataset and to calculate the main statistical indicators.
2. Be able to build different dependencies among existed attributes of dataset.
3. Be able to visualize the data analysis results with various plot types.

## Level 2: Classification & Evaluation of the credit card clients

The purpose of this lab is to master the some techniques of classification and evaluation of the credit card clients based on baseline machine learning models.

### Learning Objectives

After completing this lab you will

1. Be able to visualize main parameters of the credit card clients dataset with well-known visualization libraries.
2. Be able to manipulate the preprocessing stages.
3. Be able to build some machine learning models (based on baseline algorithms).

## Level 3: Advanced machine learning for classification tasks 

The purpose of this lab is to build the ensemble models and its further optimization (tuning) for the the credit card clients dataset.

### Learning Objectives

After completing this lab you will

1. Be able to build ensemble (set) of baseline models.
2. Be able to apply boosting techniques for built machine learning models.
3. Be able to tune various machine learning models with ensembles and compare its.

## Level 4: Predicting non-fulfillment of credit payment based on Deep Learning model

The purpose of this lab is to build the Deep Learning model for credit card clients dataset that to predict and avoid non-fulfillment of credit payment.

### Learning Objectives

After completing this lab you will

1. Be able to select target metrics for Deep Learning model.
2. Be able to build Deep Learning model with hyperparameter tuning.
3. Be able to train and evaluate the built model.
