Devin Knight, Mitchell Pearson, Bradley Schacht, Erin Ostrowsky - książki
Tytuły autora: dostępne w księgarni Ebookpoint
-
De-Mystifying Math and Stats for Machine Learning. Mastering the Fundamentals of Mathematics and Statistics for Machine Learning
-
Data Management Strategy at Microsoft. Best practices from a tech giant's decade-long data transformation journey
-
Data Engineering with Databricks Cookbook. Build effective data and AI solutions using Apache Spark, Databricks, and Delta Lake
-
Azure Data Engineer Associate Certification Guide. Ace the DP-203 exam with advanced data engineering skills - Second Edition
-
Zarządzanie danymi w zbiorach o dużej skali. Nowoczesna architektura z siatką danych i technologią Data Fabric. Wydanie II
-
Databricks ML in Action. Learn how Databricks supports the entire ML lifecycle end to end from data ingestion to the model deployment
-
Dylemat sztucznej inteligencji. 7 zasad odpowiedzialnego tworzenia technologii
-
Accelerate Model Training with PyTorch 2.X. Build more accurate models by boosting the model training process
-
The Machine Learning Solutions Architect Handbook. Practical strategies and best practices on the ML lifecycle, system design, MLOps, and generative AI - Second Edition
-
Fundamentals of Analytics Engineering. An introduction to building end-to-end analytics solutions
-
The Definitive Guide to Data Integration. Unlock the power of data integration to efficiently manage, transform, and analyze data
-
Artificial Intelligence with Microsoft Power BI
-
Data Stewardship in Action. A roadmap to data value realization and measurable business outcomes
-
Bayesian Analysis with Python. A practical guide to probabilistic modeling - Third Edition
-
Data Engineering with Scala and Spark. Build streaming and batch pipelines that process massive amounts of data using Scala
-
MATLAB for Machine Learning. Unlock the power of deep learning for swift and enhanced results - Second Edition
-
Automating Data Quality Monitoring
-
Data Observability for Data Engineering. Proactive strategies for ensuring data accuracy and addressing broken data pipelines
-
Deep Learning with MXNet Cookbook. Discover an extensive collection of recipes for creating and implementing AI models on MXNet
-
Developing Kaggle Notebooks. Pave your way to becoming a Kaggle Notebooks Grandmaster
-
Power BI i sztuczna inteligencja. Jak w pełni wykorzystać funkcje AI dostępne w Power BI
-
Learn PostgreSQL. Use, manage, and build secure and scalable databases with PostgreSQL 16 - Second Edition
-
The Statistics and Machine Learning with R Workshop. Unlock the power of efficient data science modeling with this hands-on guide
-
Architecting Data and Machine Learning Platforms
-
Microsoft Power BI dla zaawansowanych. Eksperckie techniki tworzenia interaktywnych analiz w świecie biznesu. Wydanie II
-
Amazon Redshift: The Definitive Guide
-
Building ETL Pipelines with Python. Create and deploy enterprise-ready ETL pipelines by employing modern methods
-
Modern Data Architectures with Python. A practical guide to building and deploying data pipelines, data warehouses, and data lakes with Python
-
Debugging Machine Learning Models with Python. Develop high-performance, low-bias, and explainable machine learning and deep learning models
-
Learning Data Science
-
Machine Learning Engineering with Python. Manage the lifecycle of machine learning models using MLOps with practical examples - Second Edition
-
Microsoft Power BI. Jak modelować i wizualizować dane oraz budować narracje cyfrowe. Wydanie III
-
Przetwarzanie języka naturalnego w praktyce. Przewodnik po budowie rzeczywistych systemów NLP
-
Fundamentals of Data Observability
-
Probabilistic Machine Learning for Finance and Investing
-
Data Wrangling on AWS. Clean and organize complex data for analysis
-
Data Wrangling with SQL. A hands-on guide to manipulating, wrangling, and engineering data using SQL
-
Graph-Powered Analytics and Machine Learning with TigerGraph
-
Zaufanie do systemów sztucznej inteligencji
-
Data Ingestion with Python Cookbook. A practical guide to ingesting, monitoring, and identifying errors in the data ingestion process
-
Streaming Data Mesh
-
Sztuczna inteligencja od podstaw
-
Machine Learning for High-Risk Applications
-
Data Management at Scale. 2nd Edition
-
Scaling Machine Learning with Spark
-
Jak projektować systemy uczenia maszynowego. Iteracyjne tworzenie aplikacji gotowych do pracy
-
Learn Azure Synapse Data Explorer. A guide to building real-time analytics solutions to unlock log and telemetry data
-
Sztuczna inteligencja. Nowe spojrzenie. Wydanie IV. Tom 1
-
Sztuczna inteligencja. Nowe spojrzenie. Wydanie IV. Tom 2
-
Practicing Trustworthy Machine Learning
-
Graph Data Processing with Cypher. A practical guide to building graph traversal queries using the Cypher syntax on Neo4j
-
The Art of Data-Driven Business. Transform your organization into a data-driven one with the power of Python machine learning
-
Microsoft Power BI Quick Start Guide. The ultimate beginner's guide to data modeling, visualization, digital storytelling, and more - Third Edition
-
Modern Time Series Forecasting with Python. Explore industry-ready time series forecasting using modern machine learning and deep learning
-
Applied Machine Learning and AI for Engineers
-
Quantum Machine Learning and Optimisation in Finance. On the Road to Quantum Advantage
-
Deep Learning with TensorFlow and Keras. Build and deploy supervised, unsupervised, deep, and reinforcement learning models - Third Edition
-
Praktyczne uczenie maszynowe w języku R
-
Matematyka w uczeniu maszynowym
-
Serverless ETL and Analytics with AWS Glue. Your comprehensive reference guide to learning about AWS Glue and its features
-
SQL for Data Analytics. Harness the power of SQL to extract insights from data - Third Edition
-
Głębokie uczenie. Wprowadzenie
-
Natural Language Processing with TensorFlow. The definitive NLP book to implement the most sought-after machine learning models and tasks - Second Edition
-
Simplifying Data Engineering and Analytics with Delta. Create analytics-ready data that fuels artificial intelligence and business intelligence
-
Simplifying Android Development with Coroutines and Flows. Learn how to use Kotlin coroutines and the flow API to handle data streams asynchronously in your Android app
-
Głębokie uczenie przez wzmacnianie. Praca z chatbotami oraz robotyka, optymalizacja dyskretna i automatyzacja sieciowa w praktyce. Wydanie II
-
Mastering Microsoft Power BI. Expert techniques to create interactive insights for effective data analytics and business intelligence - Second Edition
-
Sztuczna inteligencja w finansach. Używaj języka Python do projektowania i wdrażania algorytmów AI
-
In-Memory Analytics with Apache Arrow. Perform fast and efficient data analytics on both flat and hierarchical structured data
-
The Pandas Workshop. A comprehensive guide to using Python for data analysis with real-world case studies
-
Projektowanie głosowych interfejsów użytkownika. Zasady doświadczeń konwersacyjnych
-
Practical Simulations for Machine Learning
-
Quantum Chemistry and Computing for the Curious. Illustrated with Python and Qiskit® code
-
Microsoft Power BI. Jak modelować i wizualizować dane oraz budować narracje cyfrowe. Wydanie II
-
Designing Machine Learning Systems
-
Fundamentals of Deep Learning. 2nd Edition
-
Mastering Azure Machine Learning. Execute large-scale end-to-end machine learning with Azure - Second Edition
-
Artificial Intelligence with Power BI. Take your data analytics skills to the next level by leveraging the AI capabilities in Power BI
-
Democratizing Artificial Intelligence with UiPath. Expand automation in your organization to achieve operational efficiency and high performance
-
The Tableau Workshop. A practical guide to the art of data visualization with Tableau
-
Microsoft Power BI Performance Best Practices. A comprehensive guide to building consistently fast Power BI solutions
-
Automated Machine Learning on AWS. Fast-track the development of your production-ready machine learning applications the AWS way
-
Data Algorithms with Spark
-
Data Lakehouse in Action. Architecting a modern and scalable data analytics platform
-
TinyML. Wykorzystanie TensorFlow Lite do uczenia maszynowego na Arduino i innych mikrokontrolerach
-
AI-Powered Commerce. Building the products and services of the future with Commerce.AI
-
Agile Machine Learning with DataRobot. Automate each step of the machine learning life cycle, from understanding problems to delivering value
-
IBM Cloud Pak for Data. An enterprise platform to operationalize data, analytics, and AI
-
Essential PySpark for Scalable Data Analytics. A beginner's guide to harnessing the power and ease of PySpark 3
-
Reliable Machine Learning
-
Microsoft Power BI Cookbook. Gain expertise in Power BI with over 90 hands-on recipes, tips, and use cases - Second Edition
-
Practical Weak Supervision
-
Building Data-Driven Applications with Danfo.js. A practical guide to data analysis and machine learning using JavaScript
-
3D Graphics Rendering Cookbook. A comprehensive guide to exploring rendering algorithms in modern OpenGL and Vulkan
-
Software Architecture Patterns for Serverless Systems. Architecting for innovation with events, autonomous services, and micro frontends
-
Amazon Redshift Cookbook. Recipes for building modern data warehousing solutions
-
Practical Machine Learning for Computer Vision
-
Wzorce projektowe uczenia maszynowego. Rozwiązania typowych problemów dotyczących przygotowania danych, konstruowania modeli i MLOps
-
Sztuczna inteligencja. Błyskawiczne wprowadzenie do uczenia maszynowego, uczenia ze wzmocnieniem i uczenia głębokiego
-
Limitless Analytics with Azure Synapse. An end-to-end analytics service for data processing, management, and ingestion for BI and ML
-
Statystyka praktyczna w data science. 50 kluczowych zagadnień w językach R i Python. Wydanie II
-
Distributed Data Systems with Azure Databricks. Create, deploy, and manage enterprise data pipelines
-
PyTorch Pocket Reference
-
Hands-On Financial Trading with Python. A practical guide to using Zipline and other Python libraries for backtesting trading strategies
-
Przetwarzanie języka naturalnego w akcji
-
Managing Microsoft Teams: MS-700 Exam Guide. Configure and manage Microsoft Teams workloads and achieve Microsoft 365 certification with ease
-
Mastering PyTorch. Build powerful neural network architectures using advanced PyTorch 1.x features
-
Hurtownie danych. Od przetwarzania analitycznego do raportowania. Wydanie II
-
Analiza danych w zarządzaniu projektami
-
Kubeflow Operations Guide
-
Microsoft Power Platform Functional Consultant: PL-200 Exam Guide. Learn how to customize and configure Microsoft Power Platform and prepare for the PL-200 exam
-
Codeless Deep Learning with KNIME. Build, train, and deploy various deep neural network architectures using KNIME Analytics Platform
-
Essential Statistics for Non-STEM Data Analysts. Get to grips with the statistics and math knowledge needed to enter the world of data science with Python
-
Microsoft Power BI Quick Start Guide. Bring your data to life through data modeling, visualization, digital storytelling, and more - Second Edition
-
Becoming a Data Analyst. A beginner's guide to kickstarting your data analysis journey
-
Modern Time Series Forecasting with Python. Industry-ready machine learning and deep learning time series analysis with PyTorch and pandas - Second Edition
-
Python for Algorithmic Trading Cookbook. Recipes for designing, building, and deploying algorithmic trading strategies with Python
-
Microsoft Power BI Cookbook. Turn data into strategic assets with updated techniques, features, use cases and best practices - Third Edition
-
Google Machine Learning and Generative AI for Solutions Architects. Build efficient and scalable AI/ML solutions on Google Cloud
-
Hands-On Image Processing with Python. Advanced Methods for Analyzing, Transforming, and Interpreting Digital Images with Expertise - Second Edition