Curriculum

14 Chapters · 14 Lessons

  1. 1

    Book Preview

    1. Hands-on NumPy for Numerical Analysis Free preview
  2. 2

    Introduction

    1. (Included in full purchase)

      Introduction

  3. 3

    Chapter 1 : Getting Started with NumPy

    1. (Included in full purchase)

      Getting Started with NumPy

  4. 4

    Chapter 2 : Understanding NumPy Array

    1. (Included in full purchase)

      Understanding NumPy Array

  5. 5

    Chapter 3 : Data Type (dtype) in NumPy Array

    1. (Included in full purchase)

      Data Type (dtype) in NumPy Array

  6. 6

    Chapter 4 : Indexing and Slicing in NumPy Array

    1. (Included in full purchase)

      Indexing and Slicing in NumPy Array

  7. 7

    Chapter 5 : NumPy Array Operations

    1. (Included in full purchase)

      NumPy Array Operations

  8. 8

    Chapter 6 : NumPy Array I/O

    1. (Included in full purchase)

      NumPy Array I/O

  9. 9

    Chapter 7 : Linear Algebra with NumPy

    1. (Included in full purchase)

      Linear Algebra with NumPy

  10. 10

    Chapter 8 : Advanced Numerical Computing

    1. (Included in full purchase)

      Advanced Numerical Computing

  11. 11

    Chapter 9 : Exploratory Data Analysis

    1. (Included in full purchase)

      Exploratory Data Analysis

  12. 12

    Chapter 10 : Performance Optimization

    1. (Included in full purchase)

      Performance Optimization

  13. 13

    Chapter 11 : Implementing a Machine Learning Algorithm

    1. (Included in full purchase)

      Implementing a Machine Learning Algorithm

  14. 14

    INDEX

    1. (Included in full purchase)

      INDEX

About the course

NumPy is the backbone of numerical computing in Python, powering everything from scientific research to machine learning and AI applications. Mastering NumPy is essential for anyone working with data, enabling faster computations, efficient data structures, and seamless integration with advanced analytical tools. Hands-on NumPy for Numerical Analysis is a comprehensive guide that takes you from the fundamentals of NumPy to its advanced applications. Through hands-on examples and real-world scenarios, this book equips data scientists, analysts, and machine learning engineers with the practical skills needed to manipulate large datasets and optimize performance. Key topics include array operations, linear algebra, signal processing, and machine learning implementations, all covered with detailed explanations and step-by-step guidance. Whether you're building your foundation in numerical computing or looking to enhance your data analysis workflows, this book will give you a competitive edge. Don't get left behind—harness the full power of NumPy to supercharge your data science and machine learning projects today!

About the Author

Rituraj Dixit brings over a decade of extensive experience in data engineering and analytics, specializing in enterprise-scale data solutions. As a Technical Manager at Cognizant Technology Solutions, Singapore, he leads complex data transformation initiatives, leveraging his expertise in ETL processes, data warehousing, big data architectures, and cloud platforms.