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Data Science Projects with Python - AI-Powered Course
5.0
Beginner
24h
Updated 4 weeks 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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Overview
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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
98 Lessons7 Projects7 Quizzes
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.
Introduction: Python for Data ScienceExercise: Getting Familiar with PythonDifferent Types of Data Science ProblemsIntroduction to Jupyter and pandasExercise: Loading the Case Study Data in a Jupyter NotebookGetting Familiar with Data and Performing Data CleaningExercise: Verifying Basic Data IntegrityBoolean MasksExercise: Continuing Verification of Data IntegrityExercise: Exploring and Cleaning the DataExercise: Exploring the Credit Limit and Demographic FeatureDeep Dive: Categorical FeaturesExercise: Implementing OHE for a Categorical FeatureExploring the Financial History Features in the DatasetSummary: Data Exploration and CleaningQuiz: Data Exploration and Cleaning
3.
Introduction to scikit-learn and Model Evaluation
14 Lessons
Examine scikit-learn tools for model training, evaluation metrics, and generating synthetic data.
4.
Details of Logistic Regression and Feature Extraction
16 Lessons
Break down complex ideas in logistic regression, feature extraction, and their practical applications.
5.
The Bias-Variance Trade-Off
14 Lessons
Map out the steps for regularization, cross-validation, and gradient descent in logistic regression.
6.
Decision Trees and Random Forests
13 Lessons
Tackle decision trees and random forests to enhance predictive modeling and handle non-linear data.
7.
Gradient Boosting, XGBoost, and SHAP Values
12 Lessons
Master advanced techniques in gradient boosting, XGBoost, and SHAP values for model performance and interpretation.
8.
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.
9.
Appendix
1 Lessons
Create a Jupyter Notebook locally with recommended hardware, software, and Anaconda.
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