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Course 3 : Understand sense of DATA and pre-processing

About Course

  • Introduction to Data Processing for Machine Learning
    • Data processing is vital for preparing data for machine learning tasks, transforming raw data into an analyzable format suitable for training models.
  • Objectives of Data Processing
    • Primary goals include creating high-quality, well-structured datasets optimized for analysis and model training.
  • Adaptability of Data Processing Techniques
    • Methods vary based on the problem, data characteristics, and chosen machine learning algorithms.
  • Importance of a Meticulous Data Processing Pipeline
    • A well-crafted pipeline is foundational for successful machine learning, highlighting the crucial role of this preparatory phase.
  • Module Focus: Numeric/Data Column Processing
    • The module zeroes in on handling numeric or columnar data, delving into specialized techniques for processing such datasets.
  • Specialized Lens for Numeric Data
    • Understanding the intricacies of numeric datasets, covering tailored techniques and methodologies for effective processing.
  • Equipping Learners with Essential Tools
    • The module aims to furnish learners with the necessary skills and insights for proficiently managing and processing numeric data in machine learning contexts.



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Course Content

Data Insight and Refinement: Grasping its Core and Preprocessing Tactics
Understanding the essence of data and the importance of preprocessing is fundamental in any data-driven endeavor. The essence of data lies in its potential to reveal insights and drive informed decision-making. However, raw data often requires preprocessing to ensure its quality, consistency, and suitability for analysis. Preprocessing involves various techniques such as cleaning, normalization, transformation, and feature engineering, aimed at enhancing data quality and facilitating downstream tasks like modeling and analysis. By deciphering the essence of data and employing effective preprocessing strategies, organizations can unlock the full potential of their data assets, leading to more accurate insights, better decision-making, and enhanced performance across diverse domains and applications.

  • Making sense of DATA for ML/DL modeling
  • Numeric Insights: Basic sanity check for Data Analysis
  • Beyond NaN: Understanding and Tackling Missing Data in Python – part 1
  • Missing values (Part 2) : Nearest Neighbor-Based Interpolation with scikit-learn
  • Outlier & Cardinality assessment : Python Code Demos and Strategies
  • A Hands-On Exploration of Data Encoding Methods
  • Practical Guide to Implementing Data Scaling Techniques in Python
  • Overcoming Data Imbalance: The Role of SMOTE in Machine Learning
  • Enhancing ML Model Generalization: Best Practices in Data Splitting

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