Python Predictive Modeling Masterclass: Hands-On Guide
Learn predictive modeling techniques in Python from data preprocessing to advanced algorithms.
4.52 (21 reviews)
5,264
students
9.5 hours
content
Mar 2024
last update
$19.99
regular price
What you will learn
Data Preprocessing: Techniques for cleaning, formatting, and organizing data effectively.
Linear Regression: Understanding and implementing linear regression models for predictive analysis.
Logistic Regression: Applying logistic regression for classification tasks and understanding its nuances.
Multiple Linear Regression: Extending regression analysis to multiple predictors for more complex modeling.
Advanced Algorithms: Exploring advanced predictive modeling algorithms such as decision trees, random forests, and gradient boosting.
Model Evaluation: Techniques for evaluating model performance and selecting the most suitable algorithms for specific tasks.
Practical Projects: Hands-on projects and real-world examples to reinforce learning and develop practical skills.
Python Libraries: Utilizing popular Python libraries such as scikit-learn, pandas, and statsmodels for efficient predictive modeling.
Interpretation and Visualization: Interpreting model results and visualizing data insights to communicate findings effectively.
Best Practices: Understanding best practices in predictive modeling, including feature selection, cross-validation, and hyperparameter tuning.
5626864
udemy ID
10/25/2023
course created date
10/30/2023
course indexed date
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