Gnana Lakshmi T C - Hands-on Supervised Learning with Python: Learn How to Solve Machine Learning Problems with Supervised Learning Algorithms Using Python

My resilient, strong and loving mother
Mrs. Uma Chandrasekara Barathi

And my patient, supportive and loving father
Mr. T.A. Chandrasekara Barathi

Gnana Lakshmi T C (Gyan) is an emerging technology enthusiast and has given several technical talks across the globe, both in the space of Artificial Intelligence as well as Blockchain. She has taught several courses in the Emerging technology space and is actively looking to explore further at the intersection of Artificial Intelligence, Blockchain and Quantum Computing.

She is a passionate writer and has written several medium articles on various technologies. She co-chaired the Data Science and Artificial Intelligence track for Grace Hoppers Celebration India 2019 conducted by AnitaB.org, which saw an attendance of 6000+ women in technology. She continues her passion for teaching by conducting webinars over the weekend for various communities and is also the core member of Women in AI Lean In Bangalore community.

She was responsible for creating and leading the Blockchain community for Women Who Code (WWCode) across the globe. With over 8+ years of experience as a Software Developer in the corporate industry, Gyan has been extremely passionate about women in technology and has been a part of the Bangalore network of WWCode Women Who Code for 5+ years. She has conducted several meetups and has given several tech talks on various technology topics like Blockchain and Machine Learning. She is also a hands-on coder and loves exploring new technologies.

Madeleine Shang is a Recommender Systems Team Lead @OpenMined. She started the Data Science and Machine Learning community at WomenWhoCode which is now successfully running with 2147 members. She is an expert in AI and Blockchain Research. She has been involved in many startups as a Founder. She is an Adventurer and Futurist at heart.

Khushi has a total +6 years of experience in the field of web, automation, and AI technologies. A Machine Learning enthusiast and a strong believer in life long learning. Currently working in IBM as Security Specialist. Few areas of expertise are Web Development, Automation, Python, Chatbots, and NLP.

Jamuna Vignesh is a research scholar with 5+ years of research experience in interpreting and analyzing technical reports, research papers, data in order to understand the technology standpoint and its implementation to successfully arrive at conclusions. Proficient knowledge in statistics and analytics. Excellent understanding of research problems and analytics tools for effective analyses of data and interpretation.

Kavitha Yogaraj is a technical lead in IBM with key skills in learning new technologies while being delivery focused. She has spent a major part of carriers in the startup company working closely with stakeholders. She has built the data crawling infrastructure, designing end-to-end architecture with implementation. She has also delivered machine learning projects for a new classification and has built a data pipeline making it operationally work. She has recently developed an interest in quantum Qiskit libraries for the role of quantum solutions engineer at IBM. Scikit libraries for machine learning, java and microservices, event-driven systems Kafka and Dockers Open Shift platform.

No task is a single mans effort. Cooperation and coordination of various people at different levels go into the successful implementation of this book.

There is always a sense of gratitude that everyone expresses to others for the help they render during difficult phases of life and to achieve the goal already set. It is impossible to thank individually, so I am, hereby, making a humble effort to thank and acknowledge some of them.

My gratitude to the entire BPB Publications for the opportunity to work with them on this project.

I would also like to thank my family members (special mentions: my sister Sri Lakshmi and my husband Sudheendra R), my supportive friends (special mention: Soundarya and Sharanya), and my readers for providing all the encouragement and motivation.

Finally, I want to thank everyone who has directly or indirectly contributed to complete this authentic piece of work.

It is said, To err is human, to forgive is divine. In this light, I wish that the shortcomings of the book will be forgiven. At the same time, I am open to all constructive criticisms, feedback, corrections, and suggestions for further improvement. All intelligent suggestions are welcome, and I will try my best to incorporate all the valuable suggestions in the subsequent editions of this book.