E-Books →Hands-on NumPy for Numerical Analysis Unlock NumPy with Google Colab for High-Performance Numerical Computing and Optimizing

Key Features
● Master NumPy concepts with hands-on examples and real-world use cases.
● Learn efficient numerical data analysis and performance optimization.
● Explore advanced NumPy functions for data science and ML workflows.
Book Description
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!
What you will learn
● Master NumPy array operations for high-performance numerical computing.
● Optimize data analysis workflows with efficient NumPy techniques.
● Perform advanced linear algebra and matrix operations using NumPy.
● Conduct statistical and exploratory data analysis with NumPy tools.
● Build end-to-end data processing pipelines with NumPy.
● Leverage NumPy for predictive modeling and machine learning tasks.
Who is this book for?
This book is tailored for data scientists, analysts, engineers, and researchers looking to master NumPy for efficient numerical computing. A basic understanding of Python is recommended, but no prior expertise in numerical analysis is required.
Table of Contents
1. Getting Started with NumPy
2. Understanding NumPy Array
3. Data Type (dtype) in NumPy Array
4. Indexing and Slicing in NumPy Array
5. NumPy Array Operations
6. NumPy Array I/O
7. Linear Algebra with NumPy
8. Advanced Numerical Computing
9. Exploratory Data Analysis
10. Performance Optimization
11. Implementing a Machine Learning Algorithm
Index