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Credits Author Project Coordinator Andrea Isoni Ritika Manoj Reviewers Proofreader Chetan Khatri Safis Editing Pavan Kumar Kolluru Dipanjan Sarkar Indexer Mariammal Chettiyar Commissioning Editor Akram Hussain Graphics Disha Haria Acquisition Editor Kirk D'Penha Sonali Vernekar Abhinash Sahu Content Development Editor Production Coordinator Arun Nadar Arvindkumar Gupta Technical Editor Cover Work Sushant S Nadkar Arvindkumar Gupta Copy Editor Vikrant Phadkay.
Foreword What is machine learning? In the past year, whether it was during a conference, a seminar or an interview, a lot of people have asked me to define machine learning. There is a lot of curiosity around what it is, as human nature requires us to define something before we begin to understand what its potential impact on our lives may be, what this new thing may mean for us in the future.
The implications are easy to grasp and will have a deep impact on our society. The best way I can think of to describe what will likely happen in the next 5 to 10 years with machine learning is recalling what happened during the industrial revolution. Before the advent of the steam engine, lots of people were performing highly repetitive physical tasks, often risking their lives or their health for minimum wages; thanks to the industrial revolution, society evolved and machines took over the relevant parts of manufacturing processes, leading to improved yields, more predictable and stable outputs, improved quality of the products and new kinds of jobs, controlling the machines that were replacing physical labor. This was the first time in the history of mankind where man had delegated the responsibility for the creation of something else to a thing we had designed and invented. In the same way, machine learning will change the way data operations are performed, reducing the need of human intervention and leaving optimization to machines and algorithms. Operators will no longer have a direct control over data, but they will control algorithms that, in turn, will control data. This will allow faster execution of operations, larger datasets will be manageable by fewer people, errors will be reduced, and more stable and predictable outcomes will be guaranteed. As many things that have a deep impact on our society, it is easy to love it as it is to hate it. Lovers will praise the benefits that machine learning will drive to their lives, haters will be criticizing the fact that, in order to be effective, machine learning needs lots of iterations, hence, lots of data. Usually, the data we feed algorithms with is our own personal information.
It is intriguing and challenging at the same time, but there is no doubt that the winners of the next decade will be those companies or individuals who can understand unstructured data and make decisions based on them in a scalable way: I see no other way than machine learning to achieve such a feat.
About the Reviewers Chetan Khatri is a data science researcher who has a total of 4.6 years of experience in research and development. He works as a principal engineer, data and machine learning, at Nazara Technologies Pvt. Ltd, where he leads data science practice in the gaming business and the subscription telecom business. He has worked with a leading data company and a Big 4 company, where he managed the Data Science Practice Platform and one of the Big 4 company's resources team. Previously, he was worked with R & D Lab and Eccella Corporation. He completed his master's degree in computer science and minor data science at KSKV Kachchh University as a gold medalist.