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  • Book Overview & Buying Expert C++
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Expert C++

Expert C++ - Second Edition

By : Marcelo Guerra Hahn, Araks Tigranyan, John Asatryan, Grigoryan, Wu
3.9 (7)
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Expert C++

Expert C++

3.9 (7)
By: Marcelo Guerra Hahn, Araks Tigranyan, John Asatryan, Grigoryan, Wu

Overview of this book

Are you an experienced C++ developer eager to take your skills to the next level? This updated edition of Expert C++ is tailored to propel you toward your goals. This book takes you on a journey of building C++ applications while exploring advanced techniques beyond object-oriented programming. Along the way, you'll get to grips with designing templates, including template metaprogramming, and delve into memory management and smart pointers. Once you have a solid grasp of these foundational concepts, you'll advance to more advanced topics such as data structures with STL containers and explore advanced data structures with C++. Additionally, the book covers essential aspects like functional programming, concurrency, and multithreading, and designing concurrent data structures. It also offers insights into designing world-ready applications, incorporating design patterns, and addressing networking and security concerns. Finally, it adds to your knowledge of debugging and testing and large-scale application design. With Expert C++ as your guide, you'll be empowered to push the boundaries of your C++ expertise and unlock new possibilities in software development.
Table of Contents (24 chapters)
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1
Part 1:Under the Hood of C++ Programming
7
Part 2: Designing Robust and Efficient Applications
18
Part 3:C++ in the AI World

Data cleansing and processing

Data cleaning and processing is a key step in the data science industry, where unstructured data is processed and used to improve its quality, integrity, and usability. These processes play a key role in ensuring that the data used for assessment and decision-making is accurate, precise, and dependable. This section will explore the importance of data cleansing and processing and discuss these processes’ basic concepts and techniques.

Data cleaning, also known as data cleaning or data scrubbing, refers to the process of identifying, correcting, or removing errors, inconsistencies, and anomalies from a data structure. Raw data often contain missing values, anomalies, records duplicates, inconsistent characters, or other abnormalities that are biased if not dealt with or may produce inaccurate results. Data cleansing aims to address these issues and improve data collection.

However, using the information to make the data relevant to analysis...

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Expert C++
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