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What are the most important data science books for applied machine learning?

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Applied machine learning is the practice of using data science techniques to create, train, and deploy models that can perform tasks such as classification, regression, clustering, or recommendation. It is a crucial skill for anyone who wants to pursue a career in artificial intelligence (AI), as it enables you to solve real-world problems with data and algorithms. However, learning applied machine learning can be challenging, as it requires a solid foundation in mathematics, statistics, programming, and domain knowledge. Fortunately, there are many books that can help you master the concepts and skills of applied machine learning, from the basics to the advanced topics. In this article, we will recommend some of the most important data science books for applied machine learning, based on their relevance, popularity, and quality.

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Md Khalid Siddiqui

We don't rise to the level of our expectations, we fall to the level of our training - Archilochus

1 Python for Data Analysis

Python is one of the most widely used programming languages for data science and machine learning, as it offers a rich set of libraries and tools for data manipulation, visualization, and modeling. Python for Data Analysis, written by Wes McKinney, the creator of pandas, is a comprehensive guide to using Python for data analysis, covering topics such as data structures, data wrangling, exploratory data analysis, and data communication. The book also introduces some of the most popular Python packages for machine learning, such as scikit-learn, TensorFlow, and PyTorch. Python for Data Analysis is a must-read for anyone who wants to learn how to use Python effectively for data science and machine learning.

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Kuldeep Patel

IIM-A +IIT, Entrepreneur, Data Scientist, Product Manager, Patent Holder
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Exploratory Data Analysis (EDA) is an analysis approach that is uded to undestand the data and figureout the patterns in the data. These patterns include outliers, missing values and features of the data that might be unexpected. Book "Hands-On Exploratory Data Analysis with Python" by Suresh Kumar Mukhiya, Usman Ahmed is one of the good book on the subject

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2 Introduction to Machine Learning with Python

Introduction to Machine Learning with Python, written by Andreas Müller and Sarah Guido, is a practical and hands-on introduction to the fundamentals of machine learning, using Python and scikit-learn. The book covers the main types of machine learning problems, such as supervised learning, unsupervised learning, and feature engineering, and explains how to apply various algorithms and techniques to real-world datasets. The book also provides tips and best practices for model evaluation, selection, and improvement, as well as how to avoid common pitfalls and challenges. Introduction to Machine Learning with Python is a great book for beginners who want to learn the basics of machine learning with Python and scikit-learn.

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Kuldeep Patel

IIM-A +IIT, Entrepreneur, Data Scientist, Product Manager, Patent Holder
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Frequently used machine the pyhton libraries used in machine learning are: 1. Scikit-Learn 2. Matplotlib 3. Keras Mastering above three can help in machine learning. There are enough material availble on their "API reference" section of these libraries. However,there are various book on these libraries "Python Data Science Handbook" by Jake VanderPlas is good book to refer

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3 Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow

Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow, written by Aurélien Géron, is a comprehensive and in-depth guide to the most popular frameworks and tools for machine learning in Python. The book covers both the theory and the practice of machine learning, from the essential concepts and algorithms to the latest developments and trends. The book also provides many examples and exercises, as well as code snippets and notebooks, that show how to implement and deploy machine learning models using scikit-learn, Keras, and TensorFlow. Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow is a perfect book for intermediate to advanced learners who want to master the art and science of machine learning in Python.

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4 Pattern Recognition and Machine Learning

Pattern Recognition and Machine Learning, written by Christopher Bishop, is a classic and authoritative textbook on the theory and applications of machine learning, from a probabilistic and Bayesian perspective. The book covers a wide range of topics, such as linear models, neural networks, kernel methods, graphical models, latent variable models, and inference techniques. The book also provides many examples and exercises, as well as MATLAB code and solutions, that illustrate the principles and methods of machine learning. Pattern Recognition and Machine Learning is a highly recommended book for advanced learners who want to deepen their understanding of the mathematical and statistical foundations of machine learning.

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5 Deep Learning

Deep learning is a subset of machine learning that uses neural networks to learn from large and complex data, and to perform tasks such as image recognition, natural language processing, speech recognition, and generative modeling. Deep Learning, written by Ian Goodfellow, Yoshua Bengio, and Aaron Courville, is the definitive and comprehensive reference on the theory and practice of deep learning, covering topics such as feedforward networks, convolutional networks, recurrent networks, generative models, optimization methods, regularization techniques, and applications. The book also provides many examples and exercises, as well as Python code and solutions, that demonstrate how to implement and use deep learning models. Deep Learning is an essential book for anyone who wants to learn the state-of-the-art of machine learning and AI.

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6 Machine Learning: A Probabilistic Perspective

Machine Learning: A Probabilistic Perspective, written by Kevin Murphy, is another excellent and comprehensive textbook on the theory and applications of machine learning, from a probabilistic and graphical perspective. The book covers a broad range of topics, such as supervised learning, unsupervised learning, probabilistic modeling, Bayesian inference, Monte Carlo methods, Markov models, latent variable models, and relational models. The book also provides many examples and exercises, as well as MATLAB code and solutions, that illustrate the concepts and techniques of machine learning. Machine Learning: A Probabilistic Perspective is a highly recommended book for advanced learners who want to explore the rich and diverse field of machine learning.

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7 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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Md Khalid Siddiqui

We don't rise to the level of our expectations, we fall to the level of our training - Archilochus
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Deep Learning for Coders with Fastai and PyTorch: AI Applications Without a PhD 1st Edition by Jeremy Howard , Sylvain Gugger To get a taste of what being a deep learning engineer entails, this book is highly recommended. There is a course accompanying the book which helps people in developing confidence in applying the books concepts.

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What are the most important data science books for applied machine learning?

1

2

3

4

5

6

7

1 Python for Data Analysis

2 Introduction to Machine Learning with Python

3 Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow

4 Pattern Recognition and Machine Learning

5 Deep Learning

6 Machine Learning: A Probabilistic Perspective

7 Here’s what else to consider

Artificial Intelligence

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What are the most important data science books for applied machine learning?

1

2

3

4

5

6

7

1 Python for Data Analysis

2 Introduction to Machine Learning with Python

3 Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow

4 Pattern Recognition and Machine Learning

5 Deep Learning

6 Machine Learning: A Probabilistic Perspective

7 Here’s what else to consider

Artificial Intelligence

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感谢您的反馈

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