Model Building for Artificial Intelligence
web-development.
Course Modules:
Module 1: Introduction to AI Model Building
What is a model in machine learning?
Overview of the model lifecycle
Understanding training vs. inference
Module 2: Data Splitting and Preparation
Train-test-validation splitting strategies
Feature selection and transformation
Handling imbalance and missing values
Module 3: Supervised Learning Algorithms
Linear Regression and Logistic Regression
Decision Trees and Random Forest
k-Nearest Neighbors and Naive Bayes
Module 4: Model Evaluation and Metrics
Confusion matrix, precision, recall, F1-score
ROC-AUC, mean squared error (MSE), R²
When to use each metric based on model type
Module 5: Hyperparameter Tuning & Optimization
Grid search vs. random search
Cross-validation and k-fold strategies
Avoiding overfitting and underfitting
Module 6: Unsupervised Model Building
Clustering (k-Means, DBSCAN)
Dimensionality reduction (PCA)
Visualizing unsupervised outputs
Module 7: Ensemble Modeling Techniques
Boosting (XGBoost, LightGBM)
Bagging and voting classifiers
Model stacking for better performance
Module 8: Capstone Project
Choose one:
Build a classification model for customer churn
Develop a regression model for price prediction
Implement a clustering model for customer segmentation
Submit model notebook, visualizations, and evaluation report
Tools & Technologies Used:
Python
Scikit-learn
Pandas, NumPy, Matplotlib
Jupyter Notebook / Google Colab
Target Audience:
Beginners learning AI and ML
Data analysts and aspiring data scientists
Developers transitioning to machine learning
Anyone looking to improve decision-making with models
Global Learning Benefits:
Build AI models that solve real problems
Master practical evaluation and tuning techniques
Understand the end-to-end modeling workflow
Prepare for advanced topics in deep learning and deployment
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