Divided into three parts, the book ensures a smooth progression from the 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 such as 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 readers to the world of machine learning, covering essential topics like data preprocessing, supervised and unsupervised learning, and algorithm implementation.

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 learning and practicing Python programming.

• Chapter-wise example codes/programs with explanations and output discussions to present each topic in an easy-to-understand way.

• Includes data visualization using Plotly and Matplotlib.

• File handling covers the creation, reading/viewing, and modification of multiple file types, including Excel, CSV, images, PDFs, and more.

• Covers regular expressions and their functions, Lambda functions, and related topics.

• Explains data preprocessing steps, including data cleaning, data transformation, feature engineering, and data splitting.

• Provides detailed coverage of supervised and unsupervised learning, supported with example code and explanations.

TARGET AUDIENCE

• B.Tech Computer Science & Engineering

• B.Sc. Computer Science

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

• BCA/MCA