Reading this book felt like "Love at first sight."
I started reading the book: "Introduction to Machine Learning with Python - A Guide for Data Scientists" by the amazing authors Andreas C. Müller & Sarah Guido. and I completely fell in love with it.
Link to the book: Introduction to Machine Learning with Python [Book] (oreilly.com)
It is honestly one of the most intuitive and well written books and content sources regarding data science I have encountered in the 6 months I have been learning about the field of Machine Learning and Data Science.
I wish I had this book when I was just getting started things would have been so much clearer and easier to grasp from the beginning. But better late than never, I am glad I came across this great source of information to help me in the journey.
It is a comprehensive introductory guide to the concepts of Machine Learning and Data Science Concepts, the authors start by introducing the basic and fundamental concepts of machine learning, including the types of problems that can be solved with machine learning, the different algorithms available, and all the steps involved in building a machine learning model. It covers all the topics that we can possibly require to get started and explains them briefly with intuitive explanations and examples while maintaining all the fine details for each step of the process.
I was genuinely surprised when I started reading the second chapter of the book named "Supervised Learning" - It covers all the algorithms that can be used with Supervised Learning and explains each one of them in great detail using useful examples and easy to understand code snippets for using them effectively.
It covers all the essential algorithms of supervised learning in great detail, including linear regression, logistic regression, decision trees, random forests, gradient boosted regression and support vector machines.
The authors provide a step-by-step explanation of how each algorithm works and the strengths and weaknesses of each and compares different approaches for achieving the best results and they provide practical tips and best practices for building machine learning models, including feature selection, model evaluation, and hyperparameter tuning.
This makes it easy for readers to understand the different algorithms and choose the one that is best suited for the different problems.
The Book provides great visual representations of different models and examples for better understanding of otherwise complex concepts.
The author uses real-world examples and case studies to demonstrate how machine learning can be applied to a variety of problems, from classification to regression and beyond.
The authors include a case study that walks readers through the entire process of building a machine learning model, from collecting and preprocessing data to evaluating the model's performance.
In conclusion, the "Introduction to Machine Learning with Python - A Guide for Data Scientists" has been an excellent resource for me while getting started with machine learning.
With its intuitive examples and visuals, this book is sure to be a valuable resource for anyone interested in this exciting field.
I would like to thank the amazing authors: Andreas C. Müller & Sarah Guido for this super helpful masterpiece. I would always be grateful for this.
Attribution:
“An Introduction to Machine Learning with Python by Andreas C. Müller and Sarah Guido (O’Reilly). Copyright 2017 Sarah Guido and Andreas Müller, 978-1-449-36941-5.”
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