Executive Development Programme in Advanced Techniques for Data Preprocessing in ML

This course is designed for experienced data scientists, managers, and executives seeking to enhance their skills in advanced data preprocessing techniques for machine learning. Participants will gain deep insights into state-of-the-art data cleaning, transformation, and feature engineering methods, enabling them to build more accurate and robust predictive models.

Upon completion, attendees will be equipped with practical knowledge to handle complex data challenges, optimize model performance, and make informed decisions based on thorough data preparation.

1. 1. Introduction to Data Preprocessing in Machine Learning: Learners will understand the importance of data preprocessing and explore foundational concepts such as data cleaning, normalization, and the impact of preprocessing on model performance. Practical skills include identifying and correcting common data issues.
2. 2. Data Cleaning Techniques: This module covers the identification and handling of missing values, outliers, and duplicate data to ensure data integrity. Learners will gain hands-on experience in cleaning datasets using Python libraries like Pandas and Scikit-learn.
3. 3. Feature Selection Methods: Learners will study various feature selection techniques, including filter, wrapper, and embedded methods, to improve model accuracy and efficiency. Practical skills include implementing feature selection algorithms and evaluating their impact on model performance.
4. 4. Data Transformation and Normalization: This module focuses on transforming data to meet the assumptions of ML algorithms. Topics include log transformations, polynomial transformations, and normalization techniques. Practical exercises will involve applying these transformations to real datasets.
5. 5. Dimensionality Reduction: Learners will delve into dimensionality reduction techniques like Principal Component Analysis (PCA) and t-SNE to manage high-dimensional data. Practical skills include reducing dataset dimensions while preserving as much variance as possible.
6. 6. Data Imputation Techniques: This module covers advanced imputation methods for missing data, including mean imputation, K-nearest neighbors imputation, and multiple imputation. Practical exercises will involve implementing these techniques in Python.
7. 7. Advanced Feature Engineering: Learners will explore advanced feature engineering techniques, such as creating interaction terms, polynomial features, and using domain knowledge to engineer new features. Practical skills include developing custom feature engineering pipelines.
8. 8. Handling Imbalanced Datasets: This module addresses the challenges of working with imbalanced datasets and introduces techniques like resampling, cost-sensitive learning, and anomaly detection. Practical skills include balancing datasets and evaluating the impact on model performance.
9. 9. Time Series Data Preprocessing: Learners will learn specific preprocessing techniques for time series data, including handling seasonal patterns, trends, and missing values. Practical exercises will involve preprocessing time series datasets for forecasting models.
10. 10. Real-World Case Studies: This module applies the learned techniques to real-world datasets and projects. Learners will work on end-to-end data preprocessing pipelines and present findings, enhancing their ability to solve complex data preprocessing challenges.