Difference between revisions of "Python for Machine Learning"
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Revision as of 16:52, 18 July 2025
Machine Learning is considered as a transformative field of artificial intelligence that enables systems to learn from data, identify patterns, and make decisions with minimal human intervention.
Python's simplicity, flexibility, and extensive ecosystem make it the language of choice for machine learning, enabling developers and data scientists to build intelligent systems with ease, speed, and accuracy. Python offers a complete toolbox for every stage of the machine learning pipeline. Beyond model development, Python empowers you to visualize data insights with libraries like Matplotlib and Seaborn, assess algorithm performance through confusion matrices and ROC curves, and apply deep learning techniques using powerful frameworks such as TensorFlow and PyTorch.
In this Python for Machine Learning series, we will explore core concepts, practical techniques, and essential libraries that form the foundation of modern machine learning workflows. If you're a beginner stepping into AI or an experienced developer looking to deepen your skills, this series will guide you through hands-on examples and best practices to unlock the full potential of Python in machine learning.
The contributors who helped to create the outline, transcribe, code, and record the tutorials are Anvita Thadavoose Manjummel and Harini Theiveegan under the guidance of Dr. T. Subbulakshmi and Dr. R. Bhargavi, Professor, School of Computer Science and Engineering, Vellore Institute of Technology Chennai. The Spoken Tutorial Effort for Python for Machine Learning is being contributed by Ms. Nirmala Venkat and Ms.Madhuri Ganapathi from the Spoken Tutorial project, Indian Institute of Technology Bombay.
