Zero to Agile Data Science

[LeeAndro]

Genre: eLearning | MP4 | Video: h264, 1280x720 | Audio: aac, 44100 Hz
Language: English | VTT | Size: 3.75 GB | Duration: 3h 49m

You will learn how to apply Agile Data Science techniques to Classification problems through 3 projects - Predicting Credit Card Fraud, Predicting Customer Churn and Predicting Financial Distress.

What you'll learn

Machine Learning

Agile Data Science

Data Science

Imbalanced Data

Credit Card Fraud Prediction

Customer Churn Prediction

Financial Distress Prediction

Feature Eeering

Hyperparameter Tuning

Ensemble Models

Binary Classification

XGBoost

Requirements

Students need to have taken introductory Data Science courses to be familiar with running Jupyter notebooks in Python

Familiarity with sklearn packages

Student should be able to setup their own Jupyter environment either in the laptops or on the cloud

Description

Each project will have 5 iterations labelled 'Day 1' to 'Day 5' that will gently take you from a simple Random Forest Classifier to a tuned ensemble of 5 classifiers (XGBoost, LightGBM, Gradient Boosted Decision Trees, Extra Trees and Random Forest) evaluated on upsampled data.

This course is ideal for intermediate Data Scientists looking to expand their skills with the following:

Automated detection of bad columns in our raw data (Day 1)

Creating your own metric for imbalanced datasets (Day 1)

Four Data Resampling techniques (Day 2)

Handling Nulls (Day 2)

Two Feature Eeering techniques (Day 3)

Four Feature Reduction techniques (Day 3)

Memory footprint reduction (Day 3)

Setting a custom scoring function inside the GridSearchCV (Day 4)

Chag the default scoring metric for XGBoost (Day 5)

Building meta-model (Day 5)

Complete Jupyter notebooks with the source code and a library of reusable functions is given to the students to use in their own projects as needed!

Who this course is for:

Intermediate Data Scientists looking to acquire Advanced Data Science skills



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