Description

Introduction:
This course focuses on designing and preparing data models specifically for Machine Learning (ML) and AI-driven analytics. Participants will learn how to structure, clean, and transform data to optimize ML model performance, ensure data quality, and enable effective feature engineering. The training blends theoretical concepts with practical exercises using real-world datasets.

Prerequisites:

• Basic knowledge of Machine Learning and AI concepts

• Familiarity with Python or R for data analysis

• Understanding of databases and SQL

• Knowledge of statistics and data preprocessing is helpful

Table of Contents:

1. Introduction to Data Modeling for ML & AI
   1.1 Overview of Data Modeling in ML/AI Context
   1.2 Differences Between Analytics Data Modeling and ML Data Modeling
   1.3 Role of Data in AI: Features, Labels, and Target Variables
   1.4 Common Challenges in ML Data Modeling

2. Data Collection and Understanding
   2.1 Identifying Relevant Data Sources for ML Projects
   2.2 Data Profiling and Exploratory Data Analysis (EDA)
   2.3 Handling Missing Values, Outliers, and Anomalies
   2.4 Understanding Data Distributions and Relationships

3. Feature Engineering and Data Transformation
   3.1 Feature Selection Techniques – Filter, Wrapper, Embedded Methods
   3.2 Feature Extraction and Dimensionality Reduction (PCA, t-SNE)
   3.3 Encoding Categorical Variables – One-Hot, Label Encoding, Embeddings
   3.4 Normalization, Scaling, and Data Standardization
   3.5 Creating New Features from Raw Data

4. Data Modeling for Supervised and Unsupervised Learning
   4.1 Structuring Data for Regression, Classification, and Clustering
   4.2 Handling Imbalanced Datasets – Sampling Techniques
   4.3 Time-Series Data Modeling and Sequence Data Preparation
   4.4 Data Splitting: Training, Validation, and Test Sets

5. Advanced Data Modeling Techniques
   5.1 Dealing with High-Dimensional Data
   5.2 Modeling Sparse Data and Text Data for NLP Applications
   5.3 Graph Data Modeling for AI and Network Analytics
   5.4 Storing and Querying ML-ready Data in Databases and Data Lakes

6. Practical Tools and Frameworks
   6.1 Python Libraries for Data Preparation: Pandas, NumPy, Scikit-learn
   6.2 Data Pipelines for ML: Airflow, Prefect, or MLFlow
   6.3 Integration with Cloud Platforms: AWS S3, GCP BigQuery, Azure Data Lake
   6.4 Automating Feature Engineering and Data Validation

7. Case Studies and Hands-On Exercises
   7.1 Building a Predictive Model for Retail or Finance Dataset
   7.2 Feature Engineering for Image and Text Data
   7.3 Optimizing Data for Model Accuracy and Performance
   7.4 End-to-End ML Pipeline: From Raw Data to Model Training

Participants will gain the ability to structure, clean, and engineer datasets optimized for ML and AI projects. They will understand how to handle complex data types, design robust features, and prepare data pipelines that feed directly into predictive and AI models. By the end, learners will be equipped to contribute effectively to real-world AI and ML analytics initiatives.