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Kai Murakami Morales

Kai Murakami Morales

Data Science Student

Non-Fiction Book Recommendations
  • Book 1

    Grokking Machine Learning

    Luis G. Serrano

    "Grokking Machine Learning" is a fantastic resource that demystifies complex machine learning concepts through clear, intuitive explanations and practical examples that require minimal math background. The book's conversational tone and focus on building understanding rather than memorizing formulas makes it an ideal entry point for beginners who might otherwise be intimidated by the technical barriers of the field.

  • Book 2

    A Mind for Numebrs

    Barbara Oakley Ph.D

    "A Mind for Numbers" brilliantly transforms how we approach learning difficult subjects through Barbara Oakley's accessible explanations of neuroscience-backed study techniques and memory strategies. The book's genius lies in making complex concepts approachable for anyone, regardless of their background, while providing practical tools that readers can immediately apply to overcome learning challenges in mathematics, science, or any field requiring deep concentration.

  • Book 3

    So Good They Can't Ignore You

    Cal Newport Ph.D

    "So Good They Can't Ignore You" captivated me with Cal Newport's compelling argument against following your passion, showing instead how developing rare and valuable skills leads to meaningful work. As one of my favorite authors, Newport consistently delivers practical wisdom across his books—from "Deep Work" to "Digital Minimalism"—always challenging conventional career advice with research-backed insights that have fundamentally shifted how I approach professional development and finding purpose in my work.

Fiction