Certificate in Data Preprocessing and Feature Engineering in Scikit-Learn

This course is designed for data scientists, machine learning engineers, and analytics professionals looking to enhance their skills in data preprocessing and feature engineering using Scikit-Learn. You will gain proficiency in handling missing values, encoding categorical data, scaling features, and creating meaningful features to improve model performance.

Learn essential techniques for data cleaning, transformation, and selection to prepare your datasets for accurate and robust machine learning models. By the end, you'll be equipped with practical skills to preprocess and engineer features effectively using Scikit-Learn.

1. 1. Introduction to Data Preprocessing: Learners will understand the importance of data preprocessing and the common issues encountered with raw data. They will gain skills in cleaning, handling missing values, and removing duplicates.
2. 2. Data Cleaning Techniques: This module covers techniques for correcting errors in data, dealing with outliers, and transforming data into a more usable format. Learners will practice identifying and fixing data quality issues.
3. 3. Feature Engineering Fundamentals: Learners will explore the basics of feature engineering, including feature selection, creation, and transformation. They will learn how to improve model performance by transforming raw data into meaningful features.
4. 4. Numerical Feature Manipulation: This module focuses on techniques for working with numerical data, including scaling, normalization, and aggregation. Learners will apply these techniques to standardize feature values.
5. 5. Categorical Feature Encoding: Learners will study different methods for encoding categorical data, such as one-hot encoding, label encoding, and ordinal encoding. They will practice implementing these methods in Scikit-Learn.
6. 6. Text and Categorical Data Preprocessing: This module teaches how to preprocess text data and categorical features effectively. Learners will gain skills in tokenization, stemming, and lemmatization, as well as encoding categorical variables.
7. 7. Feature Selection Techniques: Learners will learn various feature selection methods, including filter methods, wrapper methods, and embedded methods. They will practice selecting the most relevant features for a dataset using Scikit-Learn.
8. 8. Dimensionality Reduction: This module covers techniques for reducing the number of random variables under consideration, such as PCA, t-SNE, and LDA. Learners will apply these techniques to simplify datasets and improve model performance.
9. 9. Advanced Feature Engineering Strategies: Learners will delve into more advanced feature engineering strategies, including interaction terms, polynomial features, and domain-specific transformations. They will practice applying these techniques to enhance model accuracy.
10. 10. Handling Imbalanced Datasets: This module focuses on strategies for dealing with imbalanced datasets, including oversampling, undersampling, and anomaly detection. Learners will learn how to preprocess and balance datasets to improve model performance.