Machine Learning Engineering Review Course

Module 1: Introduction to Data Preprocessing

1.1. Importance of Data Preprocessing

• Objective: Understand why data preprocessing is crucial for machine learning and data analysis.
• Reading: Data Preprocessing in Machine Learning
• Interactive Tool: Use the Google Colab Notebook to experiment with different preprocessing techniques on sample datasets.
• Podcast: Listen to this episode on Data Skeptic Podcast: The Importance of Data Preprocessing

1.2. Data Preprocessing Workflow

• Objective: Learn the typical data preprocessing workflow, including data loading, cleaning, transformation, and normalization.
• Video: Data Preprocessing Workflow
• Interactive Tutorial: Complete this interactive tutorial on DataCamp: Data Preprocessing
• Case Study: Review this case study on Kaggle: Data Preprocessing for Titanic Dataset
• Tool: Explore the OpenRefine tool for data cleaning and preprocessing.

1.3. Handling Missing Data

• Objective: Learn techniques to handle missing data, including imputation methods and dealing with missing values.
• Reading: Handling Missing Data in Python
• Video: Handling Missing Data with Pandas
• Interactive Notebook: Use this Kaggle Notebook to practice handling missing data on the Housing Prices dataset.

1.4. Data Cleaning and Transformation