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Get to grips with building robust XGBoost models using Python and scikit-learn for deployment

An example-rich guide for beginners to start their reinforcement and deep reinforcement learning journey with state-of-the-art distinct algorithms

Cut through the noise and get real results with a step-by-step approach to mastering Kubernetes

Implementing supervised, unsupervised, and generative deep learning (DL) models using Keras, TensorFlow, and PyTorch

Leverage real-world use cases to deploy and test Linux-based Docker containers

Uncover Angular’s potential for creating enterprise web applications—from setting up the environment to deployment—with the help of expert guidance, step-by-step explanations, and hands-on exercises

Implement investment strategies using real market data to perform effective financial and data analysis using Python

Quickly build and deploy machine learning models without managing infrastructure, and improve productivity using Amazon SageMaker’s capabilities such as Amazon SageMaker Studio, Autopilot, Experiments, Debugger, and Model Monitor

With examples and activities that help you achieve real results, applying calculus and statistical methods relevant to advanced data science has never been so easy

Complete recipes spread across 15 chapters to help you overcome commonly faced issues by Python for everybody across the globe. Each recipe takes a problem-solution approach to resolve for effective Python.

Leverage machine learning to design and back-test automated trading strategies for real-world markets using pandas, TA-Lib, scikit-learn, LightGBM, SpaCy, Gensim, TensorFlow 2, Zipline, backtrader, Alphalens, and pyfolio.

Learn proven patterns, techniques, and tricks to take full advantage of the Node.js platform. Master well-known design principles to create applications that are readable, extensible, and that can grow big.

Go beyond simply learning Kubernetes fundamentals and its deployment, and explore more advanced concepts, including serverless computing and service meshes with the latest updates

A comprehensive guide to getting well-versed with the mathematical techniques for building modern deep learning architectures

Learn algorithms for solving classic computer science problems with this concise guide covering everything from fundamental algorithms, such as sorting and searching, to modern algorithms used in machine learning and cryptography