HANDS-ON TUTORIAL
36 Regression Model
(Sklearn & XGBoost)
for
Energy Efficiency Prediction
End-to-End Data Science Project
Incl. AI Explainability with SHAP Values & FULL Python Code
Presented by Dr. Maryam Miradi
Online | Pre-recorded | One-hour
Hosted by:
Dr. Maryam Miradi
About Dr. Maryam Miradi
Maryam Miradi is the CEO and Chief AI Scientist of Profound Analytics.
She has over 20 years of experience in AI model development, holds a PhD in AI, has published 24 articles, authored 2 books, received 5 awards, and has experience coaching over 200 data scientists across 12 industries.
She has been awarded as the Best European Researcher in Future Vision.
📈 End to End Data Science Project
- Data Science Portfolio: Perfect for adding to your resume.
- Machine Learning Projects: Great examples of practical applications.
- End-to-End Data Science Project: From data preprocessing to model evaluation.
- Scikit-Learn: Implementation of various models using sklearn.
- Pipeline and Cross Validation: Ensuring robust model performance.
🛠️ 36 Regression SK Learn & XGBoost Models
- Linear Regression
- Ridge, Lasso, ElasticNet
- LARS, LassoLARS, OMP
- Bayesian Ridge, ARD Regression
- SGD, Passive Aggressive Regressor
- Huber, Quantile Regressor
- SVR, NuSVR, LinearSVR
- KNeighbors Regressor, RadiusNeighbors Regressor
- Decision Tree, Random Forest, Extra Trees Regressors
- Gradient Boosting, AdaBoost, Bagging Regressors
- Voting, Stacking Regressors
- XGBoost Regressor
- Gaussian Process Regressor
- Naive Bayes (Gaussian, Multinomial, Bernoulli, Complement, Categorical)
- Discriminant Analysis (Linear, Quadratic)
- MLP Regressor
💡 AI Explainability with SHAP
Next to Models from Sklearn and XGBooost you will learn about AI Explainability with SHAP Python Library.
🔑 Key Techniques:
- Pipeline Scikit-Learn: Streamline your workflow.
- Ensemble Learning Methods: Boost model performance using ensemble algorithms.
- Generalized Linear Models: Covering various regression techniques.
- Support Vector Machine: Understanding SVM in supervised learning.
- K-Nearest Neighbors: Implementing KNN for regression tasks.
- Discriminant Analysis: Leveraging LDA and QDA.
- Neural Networks
- AI Explainability with SHAP values
Sign Up For Hands-On Tutorial Today
If you're looking for a unique end-to-end data science project to enhance your portfolio and want to have All Machine Learning Models explained, this Tutorial is perfect for you. Join me as I guide you step-by-step through 36 Regression models for Energy Efficiency, providing you with practical, hands-on experience in a real-world application.
