Data Science Fusion: Integrating Maths, Python, and Machine Learning

Table of contents :
Title Page
Copyright Page
Data Science Fusion: Integrating Maths, Python, and Machine Learning
Chapter 1: Understanding Data Science
Chapter 2: The Data Science Workflow
Chapter 3: Tools and Technologies in Data Science
Chapter 4: Foundations of Mathematics for Data Science
Chapter 5: Linear Algebra for Data Scientists
Chapter 6: Multivariable Calculus: A Data Science Perspective
Chapter 7: Probability and Statistics for Data Analysis
Chapter 8: Python Fundamentals
Chapter 9: Essential Python Libraries for Data Science
Chapter 10: Data Wrangling and Preprocessing with Python
Chapter 11: Data Visualization Techniques with Matplotlib and Seaborn
Chapter 12: Introduction to Machine Learning
Chapter 13: Supervised Learning: Regression and Classification
Chapter 14: Unsupervised Learning: Clustering and Dimensionality Reduction
Chapter 15: Evaluation Metrics for Machine Learning Models
Chapter 16: Ensembles and Boosting Algorithms
Chapter 17: Deep Learning Fundamentals
Chapter 18: Convolutional Neural Networks (CNNs) for Image Analysis
Chapter 19: Recurrent Neural Networks (RNNs) for Sequence Data
Chapter 20: Natural Language Processing (NLP) with Machine Learning
Chapter 1: Understanding Data Science
Chapter 2: The Data Science Workflow
Chapter 3: Tools and Technologies in Data Science
Chapter 4: Foundations of Mathematics for Data Science
Chapter 5: Linear Algebra for Data Scientists
Chapter 6: Multivariable Calculus: A Data Science Perspective
Chapter 7: Probability and Statistics for Data Analysis
Chapter 8: Python Fundamentals
Chapter 9: Essential Python Libraries for Data Science
Chapter 10: Data Wrangling and Preprocessing with Python
Chapter 11: Data Visualization Techniques with Matplotlib and Seaborn
Chapter 12: Introduction to Machine Learning
Chapter 13: Supervised Learning: Regression and Classification
Chapter 14: Unsupervised Learning: Clustering and Dimensionality Reduction
Chapter 15: Evaluation Metrics for Machine Learning Models
Chapter 16: Ensembles and Boosting Algorithms
Chapter 17: Deep Learning Fundamentals
Chapter 18: Convolutional Neural Networks (CNNs) for Image Analysis
Chapter 19: Recurrent Neural Networks (RNNs) for Sequence Data
Chapter 20: Natural Language Processing (NLP) with Machine Learning
Appendix

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