Applying Machine Learning and Artificial Intelligence to Business Data

Introduction to Machine Learning and Artificial Intelligence
• Basic concepts and terminology
• Types of ML and AI algorithms
• Applications of ML and AI in business

Data Preparation for ML and AI
• Data collection and preprocessing
• Feature engineering and selection
• Handling missing data and outliers

Supervised Learning Techniques
• Linear and logistic regression
• Decision trees and random forests
• Support vector machines
• Neural networks and deep learning

Unsupervised Learning Techniques
• Clustering algorithms (k-means, hierarchical, DBSCAN)
• Dimensionality reduction (PCA, t-SNE)
• Association rule mining

Model Evaluation and Selection
• Train-test split and cross-validation
• Performance metrics (accuracy, precision, recall, F1-score)
• Hyperparameter tuning and model selection

Practical Applications and Case Studies
• Customer segmentation and personalization
• Fraud detection and risk management
• Predictive maintenance and supply chain optimization
• Sentiment analysis and customer experience improvement

Ethical Considerations and Responsible AI
• Bias and fairness in ML and AI models
• Privacy and data protection
• Explainability and interpretability of AI systems

Hands-on Workshops and Projects
• Implementing ML and AI models using Python libraries (e.g., scikit-learn, TensorFlow, PyTorch)
• Working on real-world business datasets and case studies
• Presenting and discussing project results

Who Can Attend:
• Data analysts, data scientists, and business analysts
• Software developers and engineers working on ML and AI projects
• Business leaders and decision-makers interested in leveraging ML and AI for their organizations
• Researchers and academics in the field of ML and AI

1ST BATCH: Tuesday, January 20, 2026 — Friday, January 23, 2026.

2ND BATCH: Tuesday, May 12, 2026 — Friday, May 15, 2026.

3RD BATCH: Tuesday, September 8, 2026 — Friday, September 11, 2026.

The training methodology integrates lectures, interactive discussions, collaborative group exercises, and
illustrative examples. Participants will acquire a blend of theoretical insights and hands-on practical
experience, emphasizing the application of learned techniques. This approach ensures that attendees return
to their professional environments equipped with both the competence and self-assurance to effectively
implement the acquired skills in their responsibilities.