Explore the exciting world of machine learning with the fastest growing technology in the world Key FeaturesUnderstand various machine learning concepts with real-world examplesImplement a supervised machine learning pipeline from data ingestion to validationGain insights into how you can use machine learning in everyday lifeBook Description Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support. With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data. By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own! What you will learnUnderstand the concept of supervised learning and its applicationsImplement common supervised learning algorithms using machine learning Python librariesValidate models using the k-fold techniqueBuild your models with decision trees to get results effortlesslyUse ensemble modeling techniques to improve the performance of your modelApply a variety of metrics to compare machine learning modelsWho this book is for Applied Supervised Learning with Python is for you if you want to gain a solid understanding of machine learning using Python. It'll help if you to have some experience in any functional or object-oriented language and a basic understanding of Python libraries and expressions, such as arrays and dictionaries.
Cut through the noise and get real results with a step-by-step approach to understanding supervised learning algorithms Key FeaturesIdeal for those getting started with machine learning for the first timeA step-by-step machine learning tutorial with exercises and activities that help build key skillsStructured to let you progress at your own pace, on your own termsUse your physical print copy to redeem free access to the online interactive editionBook Description You already know you want to understand supervised learning, and a smarter way to do that is to learn by doing. The Supervised Learning Workshop focuses on building up your practical skills so that you can deploy and build solutions that leverage key supervised learning algorithms. You'll learn from real examples that lead to real results. Throughout The Supervised Learning Workshop, you'll take an engaging step-by-step approach to understand supervised learning. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend learning how to predict future values with auto regressors. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding. Every physical print copy of The Supervised Learning Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your book. Fast-paced and direct, The Supervised Learning Workshop is the ideal companion for those with some Python background who are getting started with machine learning. You'll learn how to apply key algorithms like a data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead. What you will learnGet to grips with the fundamental of supervised learning algorithmsDiscover how to use Python libraries for supervised learningLearn how to load a dataset in pandas for testingUse different types of plots to visually represent the dataDistinguish between regression and classification problemsLearn how to perform classification using K-NN and decision treesWho this book is for Our goal at Packt is to help you be successful, in whatever it is you choose to do. The Supervised Learning Workshop is ideal for those with a Python background, who are just starting out with machine learning. Pick up a Workshop today, and let Packt help you develop skills that stick with you for life.
Explore the exciting world of machine learning with the fastest growing technology in the world Key FeaturesUnderstand various machine learning concepts with real-world examplesImplement a supervised machine learning pipeline from data ingestion to validationGain insights into how you can use machine learning in everyday lifeBook Description Machine learning—the ability of a machine to give right answers based on input data—has revolutionized the way we do business. Applied Supervised Learning with Python provides a rich understanding of how you can apply machine learning techniques in your data science projects using Python. You'll explore Jupyter Notebooks, the technology used commonly in academic and commercial circles with in-line code running support. With the help of fun examples, you'll gain experience working on the Python machine learning toolkit—from performing basic data cleaning and processing to working with a range of regression and classification algorithms. Once you’ve grasped the basics, you'll learn how to build and train your own models using advanced techniques such as decision trees, ensemble modeling, validation, and error metrics. You'll also learn data visualization techniques using powerful Python libraries such as Matplotlib and Seaborn. This book also covers ensemble modeling and random forest classifiers along with other methods for combining results from multiple models, and concludes by delving into cross-validation to test your algorithm and check how well the model works on unseen data. By the end of this book, you'll be equipped to not only work with machine learning algorithms, but also be able to create some of your own! What you will learnUnderstand the concept of supervised learning and its applicationsImplement common supervised learning algorithms using machine learning Python librariesValidate models using the k-fold techniqueBuild your models with decision trees to get results effortlesslyUse ensemble modeling techniques to improve the performance of your modelApply a variety of metrics to compare machine learning modelsWho this book is for Applied Supervised Learning with Python is for you if you want to gain a solid understanding of machine learning using Python. It'll help if you to have some experience in any functional or object-oriented language and a basic understanding of Python libraries and expressions, such as arrays and dictionaries.
Cut through the noise and get real results with a step-by-step approach to understanding supervised learning algorithms Key FeaturesIdeal for those getting started with machine learning for the first timeA step-by-step machine learning tutorial with exercises and activities that help build key skillsStructured to let you progress at your own pace, on your own termsUse your physical print copy to redeem free access to the online interactive editionBook Description You already know you want to understand supervised learning, and a smarter way to do that is to learn by doing. The Supervised Learning Workshop focuses on building up your practical skills so that you can deploy and build solutions that leverage key supervised learning algorithms. You'll learn from real examples that lead to real results. Throughout The Supervised Learning Workshop, you'll take an engaging step-by-step approach to understand supervised learning. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend learning how to predict future values with auto regressors. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding. Every physical print copy of The Supervised Learning Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your book. Fast-paced and direct, The Supervised Learning Workshop is the ideal companion for those with some Python background who are getting started with machine learning. You'll learn how to apply key algorithms like a data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead. What you will learnGet to grips with the fundamental of supervised learning algorithmsDiscover how to use Python libraries for supervised learningLearn how to load a dataset in pandas for testingUse different types of plots to visually represent the dataDistinguish between regression and classification problemsLearn how to perform classification using K-NN and decision treesWho this book is for Our goal at Packt is to help you be successful, in whatever it is you choose to do. The Supervised Learning Workshop is ideal for those with a Python background, who are just starting out with machine learning. Pick up a Workshop today, and let Packt help you develop skills that stick with you for life.
This book provides an interpretive and comprehensive account of the history of India between the eighteenth and twentieth centuries, a crucial epoch characterized by colonialism, nationalism and the emergence of the independent Indian Union. It explores significant historiographical debates concerning the period while highlighting important new issues, especially those of gender, ecology, caste, and labour. The work combines an analysis of colonial and independent India in order to underscore ideologies, policies, and processes that shaped the colonial state and continue to mould the Indian nation.
This book examines the politics behind, and the socio-economic and ecological repercussions of, the making of a new township, variously called New Town, Megacity or Jyoti Basu Nagar, in Rajarhat near Kolkata. Conceived by the West Bengal state government in the mid-1990s, in pandering to the vision of urban planners of creating a hi-tech town beyond an unruly, crowded Kolkata, and feeding the hunger of realtors and developers, the city is built on the foundations of coercive, even violent, land acquisition, state largesse and corruption — and at the cost of erasing a self-sufficient subsistence economy and despoiling a fragile environment. Yet, after its completion and departure of construction labour, the new town appears as a necropolis, a ghost city, that belies its promised image of an urban utopia, even as the displaced locals lead a precarious, mobile existence as ‘transit labour’, engaged in odd and informal jobs. Written on the basis of intensive fieldwork, government documents, court records, and chronicles of public protests, this book broadly analyses the politics and economics of urbanisation in the age of post-colonial capitalism, particularly the paradoxical combination of neoliberal and primitive modes of capital accumulation upon which the global emergence of ‘new towns’ is based. Departing from the dominant styles of urban studies that focus on cultural or spatial analysis of cities, the authors show the links between changes in space, technology, political economy, class composition, and forms of urban politics which give concrete shape to a city. It will immensely interest those in sociology, political science, economics, development studies, urban studies, policy and governance studies, and history.
Ishita Pande's innovative study provides a dual biography of India's path-breaking Child Marriage Restraint Act (1929) and of 'age' itself as a key category of identity for upholding the rule of law, and for governing intimate life in late colonial India. Through a reading of legislative assembly debates, legal cases, government reports, propaganda literature, Hindi novels and sexological tracts, Pande tells a wide-ranging story about the importance of debates over child protection to India's coming of age. By tracing the history of age in colonial India she illuminates the role of law in sculpting modern subjects, demonstrating how seemingly natural age-based exclusions and understandings of legal minority became the alibi for other political exclusions and the minoritization of entire communities in colonial India. In doing so, Pande highlights how childhood as a political category was fundamental not just to ideas of sexual norms and domestic life, but also to the conceptualisation of citizenship and India as a nation in this formative period.
Thank you for visiting our website. Would you like to provide feedback on how we could improve your experience?
This site does not use any third party cookies with one exception — it uses cookies from Google to deliver its services and to analyze traffic.Learn More.