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Python Machine Learning by Example: Build Intelligent Systems Using Python, Tensorflow 2, Pytorch, and Scikit-Learn, 3rd Edition
Yuxi (Hayden) Liu (Author)
·
Packt Publishing
· Paperback
Python Machine Learning by Example: Build Intelligent Systems Using Python, Tensorflow 2, Pytorch, and Scikit-Learn, 3rd Edition - Yuxi (Hayden) Liu
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Synopsis "Python Machine Learning by Example: Build Intelligent Systems Using Python, Tensorflow 2, Pytorch, and Scikit-Learn, 3rd Edition"
A comprehensive guide to get you up to speed with the latest developments of practical machine learning with Python and upgrade your understanding of machine learning (ML) algorithms and techniquesKey FeaturesDive into machine learning algorithms to solve the complex challenges faced by data scientists todayExplore cutting edge content reflecting deep learning and reinforcement learning developmentsUse updated Python libraries such as TensorFlow, PyTorch, and scikit-learn to track machine learning projects end-to-endBook DescriptionPython Machine Learning By Example, Third Edition serves as a comprehensive gateway into the world of machine learning (ML).With six new chapters, on topics including movie recommendation engine development with Naive Bayes, recognizing faces with support vector machine, predicting stock prices with artificial neural networks, categorizing images of clothing with convolutional neural networks, predicting with sequences using recurring neural networks, and leveraging reinforcement learning for making decisions, the book has been considerably updated for the latest enterprise requirements.At the same time, this book provides actionable insights on the key fundamentals of ML with Python programming. Hayden applies his expertise to demonstrate implementations of algorithms in Python, both from scratch and with libraries.Each chapter walks through an industry-adopted application. With the help of realistic examples, you will gain an understanding of the mechanics of ML techniques in areas such as exploratory data analysis, feature engineering, classification, regression, clustering, and NLP.By the end of this ML Python book, you will have gained a broad picture of the ML ecosystem and will be well-versed in the best practices of applying ML techniques to solve problems.What you will learnUnderstand the important concepts in ML and data scienceUse Python to explore the world of data mining and analyticsScale up model training using varied data complexities with Apache SparkDelve deep into text analysis and NLP using Python libraries such NLTK and GensimSelect and build an ML model and evaluate and optimize its performanceImplement ML algorithms from scratch in Python, TensorFlow 2, PyTorch, and scikit-learnWho this book is forIf you're a machine learning enthusiast, data analyst, or data engineer highly passionate about machine learning and want to begin working on machine learning assignments, this book is for you.Prior knowledge of Python coding is assumed and basic familiarity with statistical concepts will be beneficial, although this is not necessary.Table of ContentsGetting Started with Machine Learning and PythonBuilding a Movie Recommendation Engine with Naive BayesRecognizing Faces with Support Vector MachinePredicting Online Ad Click-Through with Tree-Based AlgorithmsPredicting Online Ad Click-Through with Logistic RegressionScaling Up Prediction to Terabyte Click LogsPredicting Stock Prices with Regression AlgorithmsPredicting Stock Prices with Artificial Neural NetworksMining the 20 Newsgroups Dataset with Text Analysis TechniquesDiscovering Underlying Topics in the Newsgroups Dataset with Clustering and Topic ModelingMachine Learning Best PracticesCategorizing Images of Clothing with Convolutional Neural NetworksMaking Predictions with Sequences Using Recurrent Neural NetworksMaking Decisions in Complex Environments with Reinforcement Learning
