HomeCoursesData Science Projects with Python
5.0

Beginner

24h

Updated 2 months ago

Data Science Projects with Python

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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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 explore the bias-variance trade-off by examining how the logistic regression model can be extended to address the overfitting problem. Then, you will train and visualize decision tree models. You'll learn about gradient boosting and understand how SHAP values can be used to explain model predictions. Finally, you’ll learn to deliver a model to the client and monitor it after deployment. By the end of the course, you will have a deep understanding of how data science can deliver real value to businesses.
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

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Content

1.

Introduction

2 Lessons

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

2.

Data Exploration and Cleaning

16 Lessons

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

4.

Details of Logistic Regression and Feature Extraction

16 Lessons

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

9.

Appendix

1 Lessons

Create a Jupyter Notebook locally with recommended hardware, software, and Anaconda.
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Developed by MAANG Engineers
Every Educative lesson is designed by our in-house team of ex-MAANG software engineers and PhD computer science educators, and developed in consultation with developers and data scientists working at Meta, Google, and more. Our mission is to get you hands-on with the necessary skills to stay ahead in a constantly changing industry. No video, no fluff. Just interactive, project-based learning with personalized feedback that adapts to your goals and experience.

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