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