فهرست مطالب

• Contributor
• About the author
• About the reviewer
• Preface
• Chapter 1: Data Science Terminology
• What is data science?
• The data science Venn diagram
• Some more terminology
• Data science case studies
• Summary
• Chapter 2: Types of Data
• Structured versus unstructured data
• The four levels of data
• Summary
• Questions and answers
• Chapter 3: The Five Steps of Data Science
• Introduction to data science
• Exploring the data
• Summary
• Chapter 4: Basic Mathematics
• Basic symbols and terminology
• Linear algebra
• Summary
• Chapter 5: Impossible or Improbable – A Gentle Introduction to Probability
• Basic definitions
• Bayesian versus frequentist
• How to utilize the rules of probability
• Introduction to binary classifiers
• Summary
• Chapter 6: Advanced Probability
• Bayesian ideas revisited
• Random variables
• Summary
• Chapter 7: What Are the Chances? An Introduction to Statistics
• What are statistics?
• How do we obtain and sample data?
• How do we measure statistics?
• The empirical rule
• Summary
• Chapter 8: Advanced Statistics
• Understanding point estimates
• Sampling distributions
• Confidence intervals
• Hypothesis tests
• Summary
• Chapter 9: Communicating Data
• Why does communication matter?
• Identifying effective visualizations
• When graphs and statistics lie
• Verbal communication
• Summary
• Chapter 10: How to Tell if Your Toaster is Learning – Machine Learning Essentials
• Introducing ML
• Types of ML
• Predicting continuous variables with linear regression
• Summary
• Chapter 11: Predictions Don’t Grow on Trees, or Do They?
• Performing naïve Bayes classification
• Understanding decision trees
• Diving deep into UL
• Feature extraction and PCA
• Summary
• Chapter 12: Introduction to Transfer Learning and Pre-Trained Models
• Understanding pre-trained models
• Different types of TL
• TL with BERT and GPT
• Summary
• Chapter 13: Mitigating Algorithmic Bias and Tackling Model and Data Drift
• Understanding algorithmic bias
• Sources of algorithmic bias
• Measuring bias
• Consequences of unaddressed bias and the importance of fairness
• Mitigating algorithmic bias
• Bias in LLMs
• Emerging techniques in bias and fairness in ML
• Understanding model drift and decay
• Mitigating drift
• Summary
• Chapter 14: AI Governance
• Mastering data governance
• Navigating the intricacy and the anatomy of ML governance
• A guide to architectural governance
• Summary
• Chapter 15: Navigating Real-World Data Science Case Studies in Action
• Introduction to the COMPAS dataset case study
• Text embeddings using pretrained models and OpenAI
• Summary
• Index
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