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PYTHON PROGRAMMING: BASICS TO MACHINE LEARNING


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PYTHON PROGRAMMING: BASICS TO MACHINE LEARNING

Pages : 261

Print Book ISBN : 9789354437670
Binding : Paperback
Print Book Status : Pre-order
Print Book Price : 595.00  476
You Save : (119)

eBook ISBN : 9789354439711
Ebook Status : Pre-order
Ebook Price : 595.00  476
You Save : (119)

Description:


This comprehensive text on Python programming is designed for undergraduate and postgraduate students in Computer Science and Information Technology. Whether you are a beginner or have limited programming knowledge, this book offers a structured learning experience, starting from foundational concepts and advancing to complex topics like machine learning.

Divided into three parts, the book ensures a smooth progression from Basics and Core concepts of Python to Machine Learning with Python. It covers fundamental topics such as data types, variables, operators, and interactive input-output, enabling readers to write simple yet effective Python programs. Subsequently, the text explores advanced concepts like control flow, functions, file handling, object-oriented programming, modules, and data visualization through graph plotting empowering readers to develop robust and complex Python applications. Finally, the book introduces its readers to the world of machine learning, covering essential topics like data preprocessing, supervised and unsupervised learning, and implementing algorithms.

The book equips students to excel in Python programming and seamlessly transition into machine learning, enabling them to design and implement customized algorithms for their datasets.

KEY FEATURES

• A practical approach to learn and practice python programming.

• Chapter-wise example code/program with explanation and output discussion to explain each topic in easy way.

• Includes data visualization through Plotly and Matplotlib.

• File handling covers creation, read/view, modification of multiple file types—excel, csv, image, pdf, etc.

• Includes Regular expression and Regular Expression Function, Lambda Function, and so on.

• Explains data preprocessing steps—Data cleaning, Data transformation, Feature engineering, and Data splitting.

• Covers, in detail, the supervised learning and unsupervised learning supported with example code and explanation.

TARGET AUDIENCE

• B.Tech Computer Science & Engineering

• B.Sc. Computer Science

• B.Tech Computer Science & Engineering with specialisation in Machine Learning

• BCA/MCA

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