1.Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Deep learning has significantly boosted machine learning, enabling even novice programmers to develop intelligent systems using simple tools. This bestselling book, updated third edition, provides an intuitive understanding of these concepts using Python frameworks like Scikit-Learn, Keras, and TensorFlow. Author Aurélien Géron explores various techniques, from linear regression to deep neural networks, using examples and exercises to help readers apply their knowledge. The book covers various models, unsupervised learning techniques, neural net architectures, and uses TensorFlow and Keras for building and training neural nets for various applications, including computer vision, natural language processing, and deep reinforcement learning.
Rated 4.7 on Amazon.
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2.Machine Learning for Absolute Beginners: A Plain English Introduction (Third Edition) (Machine Learning with Python for Beginners Book Series)
Machine Learning for Absolute Beginners Third Edition is a concise and clear introduction to machine learning, designed for absolute beginners. It provides a high-level introduction to machine learning, free downloadable code exercises, and video demonstrations. The book is written in plain-English with no coding experience required, and features clear explanations and visual examples for easy follow-up at home. The updated edition includes extended chapters with quizzes, free supplementary online video tutorials for coding models in Python, and downloadable resources not included in the Second Edition. The book covers topics such as downloading free datasets, identifying necessary tools and libraries, data scrubbing techniques, preparing data for analysis, regression analysis, k-means clustering, neural network basics, bias/variance, decision trees, and building a machine learning model to predict house values using Python. It is a great resource for those who have passed the beginner stage in their study of machine learning and are ready to tackle coding and deep learning.
Rated 4.4 on Amazon.
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3.Introduction to Machine Learning with Python: A Guide for Data Scientists
This book provides practical guidance on building machine learning solutions using Python and the scikit-learn library. Authors Andreas Müller and Sarah Guido emphasize the practical aspects of using machine learning algorithms, while familiarity with NumPy and matplotlib libraries is beneficial. The book covers fundamental concepts and applications of machine learning, advantages and shortcomings of widely used algorithms, data representation, advanced methods for model evaluation and parameter tuning, pipelines for chaining models, text data processing techniques, and suggestions for improving machine learning and data science skills.
Rated 4.6 on Amazon.
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4.The Art of Machine Learning: A Hands-On Guide to Machine Learning with R
The Art of Machine Learning is a practical guide that teaches how to apply various machine learning methods to real data. It covers various techniques, such as k-NN, random forests, gradient boosting, SVMs, and neural networks. The book also covers regression models, decision trees, and parametric methods. It provides expert tips for handling “dirty” or unbalanced data and troubleshooting pitfalls. The guide also covers dealing with large datasets, the Bias-Variance Trade-off, linear relationships, and real-world image and text classification. The book requires a basic understanding of graphs and charts and familiarity with the R programming language. It is a valuable resource for those looking to improve their machine learning skills.
Rated 5 on Amazon.
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5.Machine Learning with PyTorch and Scikit-Learn: Develop machine learning and deep learning models with Python
This book, part of the Python Machine Learning series, is a comprehensive guide to machine learning and deep learning using PyTorch’s simple-to-code framework. It provides clear explanations, visualizations, and examples, covering essential machine learning techniques in depth. PyTorch is the Pythonic way to learn machine learning, making it easier to learn and code with. The book also covers popular libraries like PyTorch Lightning and PyTorch Geometric, generative adversarial networks (GANs), and the latest trends in deep learning, including graph neural networks and large-scale transformers used for natural language processing (NLP). The book is a companion for Python developers new to machine learning or those looking to deepen their knowledge of the latest developments. Key features include exploring frameworks, models, and techniques for machines to learn from data, using scikit-learn for machine learning and PyTorch for deep learning, training machine learning classifiers, building and training neural networks, transformers, and boosting algorithms, discovering best practices for evaluating and tuning models, and predicting continuous target outcomes using regression analysis.
Rated 4.3 on Goodreads.
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6.Programming Machine Learning: From Coding to Deep Learning
Mastering machine learning is not a difficult task, even for software developers. By writing code one line at a time, from simple learning programs to deep learning systems, you can tackle hard topics and build confidence. Start by breaking down complex topics into manageable parts and writing Python code without any libraries. Iterate on your design and add layers of complexity as you go. Examples include building an image recognition application with supervised learning, predicting the future with linear regression, diving into gradient descent, creating perceptrons, building neural networks for complex data sets, training and refining them with backpropagation and batching, layering neural networks, eliminating overfitting, and adding convolution to transform them into a deep learning system. The book provides examples written in Python, but you only need a computer and a code-adept brain.
Rated 4.4 on Amazon.
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7.Ultimate Step by Step Guide to Machine Learning Using Python: Predictive modelling concepts explained in simple terms for beginners
This book teaches readers how to become a data scientist in just 7 days. It covers Python basics, including data structures like Pandas and foundational libraries like Numpy, Seaborn, and Scikit-Learn. The book also teaches how to build predictive machine learning models using techniques like regression analysis, decision tree analysis, training, and testing. The goal is to code in Python confidently, build new models from scratch, clean and prepare data for analytics, and speak confidently about statistical analysis techniques. The book stands out for its step-by-step code examples, visual explanations of complex concepts, real-world applicability of machine learning models, and bonus free code samples. The goal is to equip readers with a step-by-step action plan to master data science and machine learning, leading to a lucrative career.
Rated 3.9 on Goodreads.
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