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  Ultimate Machine Learning with...

  Parag Saxena

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  Master the Art of Data Munging and Predictive Modeling for Machine Learning with Scikit-Learn
  Book Description
  “Ultimate Machine Learning with Scikit-Learn” is a definitive resource that offers an in-depth exploration of data preparation, modeling techniques, and the theoretical foundations behind powerful machine learning algorithms using Python and Scikit-Learn.
  Beginning with foundational techniques, you'll dive into essential skills for effective data preprocessing, setting the stage for robust analysis. Next, logistic regression and decision trees equip you with the tools to delve deeper into predictive modeling, ensuring a solid understanding of fundamental methodologies. You will master time series data analysis, followed by effective strategies for handling unstructured data using techniques like Naive Bayes.
  Transitioning into real-time data streams, you'll discover dynamic approaches with K-nearest neighbors for high-dimensional data analysis with Support Vector Machines(SVMs). Alongside, you will learn to safeguard your analyses against anomalies with isolation forests and harness the predictive power of ensemble methods, in the domain of stock market data analysis.
  By the end of the book you will master the art of data engineering and ML pipelines, ensuring you're equipped to tackle even the most complex analytics tasks with confidence.
  Table of Contents
  1. Data Preprocessing with Linear Regression
  2. Structured Data and Logistic Regression
  3. Time-Series Data and Decision Trees
  4. Unstructured Data Handling and Naive Bayes
  5. Real-time Data Streams and K-Nearest Neighbors
  6. Sparse Distributed Data and Support Vector Machines
  7. Anomaly Detection and Isolation Forests
  8. Stock Market Data and Ensemble Methods
  9. Data Engineering and ML Pipelines for Advanced Analytics
  Index

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  Megalodon The: The History and...

  Editors Charles River

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  As scary as the shark in Jaws is, the extinct megalodon had teeth three times the length of those found within the jaws of a great white shark, so a human would only have been a small snack. In fact, the megalodon was capable of swallowing a small car. Known to scientists as Otodus megalodon or Carcharocles megalodon (otodus meaning “tooth shaped like an ear”; carcharocles meaning “well known for jaggedness”; and megalodon meaning “big tooth”), this predator lived approximately 23–3.6 million years ago), from the early Miocene Epoch to the early Pliocene Epoch. The genus name Otodus and the family name Otodontidae are now the most commonly used, but scientific literature remains full of the older designation, and many of the shark experts regard the new family designation as flawed.  
  	For many years, megalodon was thought to be more closely related to the great white shark (Carcharodon carcharias), and though both are still part of the order Lamniformes, the two species now reside in different families: Lamnidae and Otodontidae (megalodon). Either way, while great white sharks can reach sizes of 20 feet long and nearly 2.5 tons in weight, the average megalodon was twice as big, and some of them may have been about three times as big. The sheer size allowed it to target prey as big as whales, and the massive jaw allowed it to simply tear through any kind of body with no regard for the prey’s most vulnerable spots.  
  	While all of that seems to be beyond dispute, almost everything else about the history of the megalodon is up for debate, from its origins to its extinction.  The Megalodon: The History and Legacy of the World’s Largest Prehistoric Predator covers that expansive history in detail, from what is known to what remains most mysterious.

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  A Little Cocaine to Loosen My...

  Sigmund Freud

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  In the 1880s, Freud discovered cocaine and became enthusiastic about what was not yet considered a party drug. Favoring the use of cocaine in the treatment of neurasthenia, as an energizer, or as an anesthetic, Freud made a creative and fashionable use of it, heralding practices that our society knows well. Several surprising texts are gathered here on cocaine. Freud recounts the use of coca in history, particularly in South America, from where it was imported. In Cocaine Addiction and Cocaine Phobia, the inventor of psychoanalysis defended coca against its accusers as a dangerous product. Finally, in the letters to Martha Bernays, his future wife, we discover an intimate and loving Freud, telling how he escaped boring dinners thanks to his dear magic substance.

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  Unplagued

  Santiago Machain

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  Unplagued explores the profound transformation of human civilization in an era largely free from the devastating plagues that once shaped history. This comprehensive volume examines how science, technology, medicine, politics, economics, society, culture, and the environment have evolved in the absence of widespread infectious disease. Through a global lens, the book delves into the triumphs and tensions of the modern world, highlighting both the opportunities and the new challenges that have emerged. It offers a nuanced portrait of a world where humanity’s ingenuity has conquered old threats, only to face new complexities—climate change, inequality, conflict, and the quest for meaning and connection. Drawing on lessons from the past and present, the book invites readers to reflect on the future and the responsibilities that come with progress.

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  Summary of Brian Christian’s The...

  Falcon Press

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  Buy now to get the main key ideas from Brian Christian’s The Alignment Problem 
    
  As machine-learning systems grow not only more prevalent, but also more powerful, humans want to ensure that they understand us and do what we want, eliminating the possibility of catastrophic divergence. In the field of computer science, this question is known as the alignment problem. 
  In The Alignment Problem (2020), Brian Christian raises questions of safety and ethics in a world where humans are turning into machines and machines are turning into humans. He discusses tools that, through imitation, curiosity, inference, and shaping, exhibit human skills without being programmed to do so. The future of machine learning holds risks, but also great promise.

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  Dogopolis - How Dogs and Humans...

  Chris Pearson

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  Dogopolis presents a surprising source for urban innovation in the history of three major cities: human-canine relationships. 
    
  Stroll through any American or European city today and you probably won’t get far before seeing a dog being taken for a walk. It’s expected that these domesticated animals can easily navigate sidewalks, streets, and other foundational elements of our built environment. But what if our cities were actually shaped in response to dogs more than we ever realized? 
   
  Chris Pearson’s Dogopolis boldly and convincingly asserts that human-canine relations were a crucial factor in the formation of modern urban living. Focusing on New York, London, and Paris from the early nineteenth century into the 1930s, Pearson shows that human reactions to dogs significantly remolded them and other contemporary western cities. It’s an unalterable fact that dogs—often filthy, bellicose, and sometimes off-putting—run away, spread rabies, defecate, and breed wherever they like, so as dogs became a more and more common in nineteenth-century middle-class life, cities had to respond to people’s fear of them and revulsion at their least desirable traits. The gradual integration of dogs into city life centered on disgust at dirt, fear of crime and vagrancy, and the promotion of humanitarian sentiments. On the other hand, dogs are some people’s most beloved animal companions, and human compassion and affection for pets and strays were equally powerful forces in shaping urban modernity. Dogopolis details the complex interrelations among emotions, sentiment, and the ways we manifest our feelings toward what we love—showing that together they can actually reshape society.

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