This comprehensive course, "Data for Machine Learning," is designed to provide learners with the fundamental knowledge and skills necessary to harness the power of data in the context of applied machine learning. Through a series of engaging modules, participants will delve into the critical elements of data in the learning, training, and operational phases of machine learning models. The course covers an extensive range of topics, including understanding biases and data sources, implementing techniques to improve model generality, addressing overfitting and exploring feature engineering for enhanced model accuracy.
Participants will gain proficiency in aligning data with the needs of learning algorithms, preparing data for machine learning success, and conducting feature engineering to optimize model performance. The course also delves into critical considerations such as imbalanced data, bias in data sources, outliers, and skewed distributions, providing learners with a comprehensive understanding of potential challenges and strategies to mitigate them.
Essential prerequisites include a beginner-level background in Python programming, a basic understanding of linear algebra and statistics, and familiarity with vector notation and probability distributions. This course is the third installment of the Applied Machine Learning Specialization and is offered by the Alberta Machine Intelligence Institute.
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Data for Machine Learning comprises four modules, each focusing on key aspects of data in machine learning, from understanding good data and preparing it for success to exploring feature engineering and addressing challenges such as imbalanced data and bias.
This module introduces learners to the critical elements of good data, including business understanding, problem discovery, and data acquisition. It covers essential topics such as metadata, multimodal data, features and transformations, and matching data to the needs of learning algorithms. The module also includes a quiz and review sessions to reinforce learning.
This module focuses on preparing data for machine learning success, delving into data warehousing, converting data to useful forms, data quality, data quantity requirements, and the transformation of data. Participants will gain essential insights into aligning similar data, imputing missing values, and data cleaning, reinforced by a module quiz and review sessions.
Feature Engineering for MORE Fun & Profit explores the intricacies of feature engineering, including the simplest features to try, useful/useless features, unsupervised learning, feature selection, and extraction. It also covers transfer learning, text features, word embeddings, and the process of building good features, culminating in a practical data preparation exercise.
This module sheds light on the challenges posed by bad data, including imbalanced data, bias in data sources, the tradeoff between bias and variance, outliers, skewed distributions, and live data dangers. It also includes a quiz and review sessions to consolidate understanding.
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