Learn how to: Use Python code and libraries like SymPy, NumPy, and scikit-learn to explore essential mathematical concepts like calculus, linear algebra, statistics, and machine learning; Understand techniques like linear regression, logistic regression, and neural networks in plain English, with minimal mathematical notation and jargon; Perform descriptive statistics and hypothesis testing on a dataset to interpret p-values and statistical significance; Manipulate vectors and matrices and perform matrix decomposition; Integrate and build upon incremental knowledge of calculus, probability, statistics, and linear algebra, and apply it to regression models including neural networks; Navigate practically through a data science career and avoid common pitfalls, assumptions, and biases while tuning your skill set to stand out in the job market.

## Book Details | |

Publisher: | O'Reilly Media |

By: | Thomas Nield |

ISBN-13: | 9781098102937 |

ISBN-10: | 1098102932 |

Year: | 2022 |

Pages: | 347 |

Language: | English |

## Book Preview | |

Online | Essential Math for Data Science |

## Paper Book | |

Buy: | Essential Math for Data Science |

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