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

✍ Scribed by NIBEDITA Sahu

• Publisher: NIBEDITA Sahu
• Year: 2023
• Tongue: English
• Leaves: 286
• Category: Library

✦ Synopsis

In this book, we will explore in the world of Data Science and inside you will gain informative insights in depth. You wiill access Maths needed for Data Science in detail with the formulase, examples and simple explanations. Then you will go through Python needed for Data Science, where you will get everything in Python from basics to advanced level, code examples and explanations. And the main thing is Machine Learning, here Machine Learning Basics to advanced techniques, everything is explained well. Access everything in detail and go deep inside each concept, understand them well and gain informative insights.

Unlock the full potential of data science with "Data Science Fusion: Integrating Maths, Python, and Machine Learning." This comprehensive guide empowers you to master the essential components of data science, equipping you with the knowledge and skills to tackle real-world challenges.

Begin your journey by understanding the core principles of data science and its vast applications. Embrace Python, the preferred language in the field, and discover the power of essential libraries for data manipulation, visualization, and analysis. Delve into the mathematical foundations that underpin data analysis and machine learning, including linear algebra, calculus, and statistics.

With a solid grasp of both mathematics and Python, dive into the exciting realm of machine learning. Learn about supervised and unsupervised learning, and explore the cutting-edge techniques of deep learning and natural language processing.

What sets this book apart is its emphasis on the fusion of mathematical theory with practical Python implementation. Each concept is accompanied by hands-on projects and real-world examples, bridging the gap between theory and application.

Whether you're an absolute beginner or an experienced practitioner, with insights into model deployment, evaluation, and ethical considerations, this book prepares you to make informed decisions in the data-driven world. Unleash the true potential of data science and revolutionize your understanding of mathematics, Python, and machine learning in the data-driven era.

✦ 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