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Data Science Projects with Python
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Learn data science with Python by exploring datasets, building, deploying, and monitoring models alongside mastering logistic regression, decision trees, gradient boosting, and SHAP values.

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Overview

As businesses gather vast amounts of data, machine learning is becoming an increasingly valuable tool for utilizing data to deliver cutting-edge predictive models that support informed decision-making. In this course, you will work on a data science project with a realistic dataset to create actionable insights for a business. You’ll begin by exploring the dataset and cleaning it using pandas. Next, you will learn to build and evaluate logistic regression classification models using scikit-learn. You will...Show More

As businesses gather vast amounts of data, machine learning is becoming an increasingly valuable tool for utilizing data to deli...Show More

WHAT YOU'LL LEARN

Hands-on experience in data exploration, data processing, data modeling and data visualization using pandas, scikit-learn, and Matplotlib

The ability to evaluate model performance and interpret model predictions

Working knowledge of how predictive models can support business decision-making

An understanding of the mathematical foundations of machine learning models

Hands-on experience in data exploration, data processing, data modeling and data visualization using pandas, scikit-learn, and Matplotlib

Content

98 Lessons7 Projects7 Quizzes

Introduction

2 Lessons

Get familiar with machine learning's role in data science and essential Python libraries.

Overview

Course Structure

Data Exploration and Cleaning

16 Lessons

Discover the logic behind data exploration and cleaning for effective data science projects.

Introduction: Python for Data Science

Exercise: Getting Familiar with Python

Different Types of Data Science Problems

Introduction to Jupyter and pandas

Exercise: Loading the Case Study Data in a Jupyter Notebook

Getting Familiar with Data and Performing Data Cleaning

Exercise: Verifying Basic Data Integrity

Boolean Masks

Exercise: Continuing Verification of Data Integrity

Exercise: Exploring and Cleaning the Data

Exercise: Exploring the Credit Limit and Demographic Feature

Deep Dive: Categorical Features

Exercise: Implementing OHE for a Categorical Feature

Exploring the Financial History Features in the Dataset

Summary: Data Exploration and Cleaning

Quiz: Data Exploration and Cleaning

(Challenge) Exploring Remaining Financial Features in Dataset

Project

Introduction to scikit-learn and Model Evaluation

14 Lessons

Examine scikit-learn tools for model training, evaluation metrics, and generating synthetic data.

Introduction: scikit-learn and Model Evaluation

The Response Variable and Concluding the Initial Exploration

scikit-learn

Generating Synthetic Data

Exercise: Linear Regression in scikit-learn

Model Performance Metrics for Binary Classification

Splitting the Data: Training and Test Sets

Classification Accuracy

Exercise: Calculating True and False Rates and Confusion Matrix

Exercise: Obtaining Probabilities from Logistic Regression Model

The Receiver Operating Characteristic (ROC) Curve

Precision

Summary: Introduction to scikit-learn and Model Evaluation

Quiz: Introduction to scikit-learn and Model Evaluation

Fake News Detection Using Scikit-learn

Project

(Challenge) Logistic Regression and Precision-Recall Curve

Project

Details of Logistic Regression and Feature Extraction

16 Lessons

Break down complex ideas in logistic regression, feature extraction, and their practical applications.

Introduction: Logistic Regression and Feature Extraction

Examining Relationships Between Features and Response Variable

Pearson Correlation

Mathematics of Linear Correlation

Exercise: F-test and Univariate Feature Selection

Finer Points of the F-test

Exercise: Visualizing the Feature and Response Variable Relationship

Univariate Feature Selection

Exercise: Plotting the Sigmoid Function

Scope of Functions

Why Is Logistic Regression Considered a Linear Model?

Exercise: Finding Appropriate Features for Logistic Regression

Logistic Regression Predictions Using Sigmoid

Exercise: Linear Decision Boundary of Logistic Regression

Summary: Details of Logistic Regression and Feature Extractor

Quiz: Details of Logistic Regression and Feature Extraction

(Challenge) Logistic Regression Model and Coefficients

Project

The Bias-Variance Trade-Off

14 Lessons

Map out the steps for regularization, cross-validation, and gradient descent in logistic regression.

Introduction: The Bias-Variance Trade-Off

Estimating the Coefficients and Intercepts of Logistic Regression

Gradient Descent to Find Optimal Parameter Values

Exercise: Using Gradient Descent to Minimize a Cost Function

Assumptions of Logistic Regression

The Motivation for Regularization

Exercise: Generating and Modeling Synthetic Classification Data

Lasso (L1) and Ridge (L2) Regularization

Cross-Validation

Exercise: The Synthetic Data Classification Problem

Options for Logistic Regression in scikit-learn

Scaling Data, Pipelines, and Interaction Features in Scikit-Learn

Summary: The Bias-Variance Trade-Off

Quiz: The Bias-Variance Trade-Off

(Challenge) Cross-Validation and Feature Engineering

Project

Decision Trees and Random Forests

13 Lessons

Tackle decision trees and random forests to enhance predictive modeling and handle non-linear data.

Introduction: Decision Trees and Random Forests

Decision Trees

Exercise: A Decision Tree in scikit-learn

Training Decision Trees: Node Impurity

Training Decision Trees: Greedy Algorithm and Stopping Criteria

Using Decision Trees: Advantages and Predicted Probabilities

A More Convenient Approach to Cross-Validation

Exercise: Find Optimal Hyperparameters for a Decision Tree

Random Forests: Predictions and Ensembles of Decision Trees

Exercise: Fitting a Random Forest

Checkerboard Graph

Summary: Decision Trees and Random Forests

Quiz: Decision Trees and Random Forests

(Challenge) Cross-Validation Grid Search with Random Forest

Project

Gradient Boosting, XGBoost, and SHAP Values

12 Lessons

Master advanced techniques in gradient boosting, XGBoost, and SHAP values for model performance and interpretation.

Introduction: XGBoost

Gradient Boosting and XGBoost

XGBoost Hyperparameters: Early Stopping

XGBoost Hyperparameters: Tuning the Learning Rate

Other Important Hyperparameters in XGBoost

Exercise: Randomized Grid Search to Tune XGBoost Hyperparameters

Another Way of Growing Trees: XGBoost's grow_policy

Explaining Model Predictions with SHAP Values

Exercise: SHAP Visualization and Analysis

Missing Data

Summary: Gradient Boosting, XGBoosting, and SHAP Values

Quiz: Gradient Boosting, XGBoosting, and SHAP Values

(Challenge) XGBoost and SHAP Explanation for Case Study Data

Project

Predict Frog Toxicity with Python and XGBoost

Project

Test Set Analysis, Financial Insights, and Delivery to the Client

10 Lessons

Learn how to use test set analysis for model evaluation, financial insights, and client delivery.

Introduction: Test Set Analysis

Review of Modeling Results

Model Performance on the Test Set

Distribution of Predicted Probability and Decile Chart

Exercise: Equal-Interval Chart

Calibration of Predicted Probabilities

Financial Analysis

Exercise: Characterizing Costs and Savings

Summary: Analysis, Financial Insights, and Delivery to the Client

Quiz: Test Set Analysis and Financial Insights

(Challenge) Deriving Financial Insights

Project

Appendix

1 Lessons

Create a Jupyter Notebook locally with recommended hardware, software, and Anaconda.

How to Create Jupyter Notebook Locally

Certificate of Completion

Showcase your accomplishment by sharing your certificate of completion.

Course Author:

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