Stefan Kottwitz - książki
Tytuły autora: dostępne w księgarni Ebookpoint
-
Learning Data Science
-
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
-
Podręcznik architekta rozwiązań. Poznaj reguły oraz strategie projektu architektury i rozpocznij niezwykłą karierę. Wydanie II
-
Azure Data and AI Architect Handbook. Adopt a structured approach to designing data and AI solutions at scale on Microsoft Azure
-
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
-
AI & Data Literacy. Empowering Citizens of Data Science
-
Graph-Powered Analytics and Machine Learning with TigerGraph
-
Data Curious
-
Cost-Effective Data Pipelines
-
Marketing i analityka biznesowa dla początkujących. Poznaj najważniejsze narzędzia i wykorzystaj ich możliwości
-
Poznaj Tableau 2022. Wizualizacja danych, interaktywna analiza danych i umiejętność data storytellingu. Wydanie V
-
Python w analizie danych. Przetwarzanie danych za pomocą pakietów pandas i NumPy oraz środowiska Jupyter. Wydanie III
-
Driving Data Quality with Data Contracts. A comprehensive guide to building reliable, trusted, and effective data platforms
-
Enhancing Deep Learning with Bayesian Inference. Create more powerful, robust deep learning systems with Bayesian deep learning in Python
-
Geospatial Data Analytics on AWS. Discover how to manage and analyze geospatial data in the cloud
-
Graph Data Modeling in Python. A practical guide to curating, analyzing, and modeling data with graphs
-
Inżynieria danych w praktyce. Kluczowe koncepcje i najlepsze technologie
-
Zaufanie do systemów sztucznej inteligencji
-
Data Ingestion with Python Cookbook. A practical guide to ingesting, monitoring, and identifying errors in the data ingestion process
-
Data Modeling with Snowflake. A practical guide to accelerating Snowflake development using universal data modeling techniques
-
Potoki danych. Leksykon kieszonkowy. Przenoszenie i przetwarzanie danych na potrzeby ich analizy
-
Streaming Data Mesh
-
Sztuczna inteligencja od podstaw
-
Machine Learning for High-Risk Applications
-
Data Management at Scale. 2nd Edition
-
Wizualizacja danych. Pulpity nawigacyjne i raporty w Excelu
-
Analityka biznesowa wspomagana sztuczną inteligencją. Ulepszanie prognoz i podejmowania decyzji za pomocą uczenia maszynowego
-
Zaawansowana analiza danych w PySpark. Metody przetwarzania informacji na szeroką skalę z wykorzystaniem Pythona i systemu Spark
-
Applied Geospatial Data Science with Python. Leverage geospatial data analysis and modeling to find unique solutions to environmental problems
-
The Kaggle Workbook. Self-learning exercises and valuable insights for Kaggle data science competitions
-
Data Wrangling with R. Load, explore, transform and visualize data for modeling with tidyverse libraries
-
Learn Azure Synapse Data Explorer. A guide to building real-time analytics solutions to unlock log and telemetry data
-
The Enterprise Data Catalog
-
Spark. Błyskawiczna analiza danych. Wydanie II
-
Uczenie maszynowe. Elementy matematyki w analizie danych
-
Data Analytics Using Splunk 9.x. A practical guide to implementing Splunk’s features for performing data analysis at scale
-
Tomographic imaging in environmental, industrial and medical applications
-
Modelowanie danych z Power BI dla ekspertów analityki. Jak w pełni wykorzystać możliwości Power BI
-
Sztuczna inteligencja. Nowe spojrzenie. Wydanie IV. Tom 1
-
Sztuczna inteligencja. Nowe spojrzenie. Wydanie IV. Tom 2
-
Practicing Trustworthy Machine Learning
-
Machine Learning Model Serving Patterns and Best Practices. A definitive guide to deploying, monitoring, and providing accessibility to ML models in production
-
Dodaj mocy Power BI! Jak za pomocą kodu w Pythonie i R pobierać, przekształcać i wizualizować dane
-
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
-
Machine Learning Techniques for Text. Apply modern techniques with Python for text processing, dimensionality reduction, classification, and evaluation
-
Python Feature Engineering Cookbook. Over 70 recipes for creating, engineering, and transforming features to build machine learning models - Second Edition
-
Quantum Machine Learning and Optimisation in Finance. On the Road to Quantum Advantage
-
Neural Search - From Prototype to Production with Jina. Build deep learning–powered search systems that you can deploy and manage with ease
-
Deep Learning with TensorFlow and Keras. Build and deploy supervised, unsupervised, deep, and reinforcement learning models - Third Edition
-
Deep Learning. Praktyczne wprowadzenie z zastosowaniem środowiska Pythona
-
Scalable Data Architecture with Java. Build efficient enterprise-grade data architecting solutions using Java
-
Dziennikarstwo danych i data storytelling
-
Matematyka w uczeniu maszynowym
-
Data Quality Fundamentals
-
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
-
Inżynieria danych na platformie AWS. Jak tworzyć kompletne potoki uczenia maszynowego
-
Głębokie uczenie. Wprowadzenie
-
Machine Learning at Scale with H2O. A practical guide to building and deploying machine learning models on enterprise systems
-
Natural Language Processing with TensorFlow. The definitive NLP book to implement the most sought-after machine learning models and tasks - Second Edition
-
Quantum Computing Experimentation with Amazon Braket. Explore Amazon Braket quantum computing to solve combinatorial optimization problems
-
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
-
Tidy Modeling with R
-
Sztuczna inteligencja w finansach. Używaj języka Python do projektowania i wdrażania algorytmów AI
-
Generative Deep Learning. 2nd Edition
-
In-Memory Analytics with Apache Arrow. Perform fast and efficient data analytics on both flat and hierarchical structured data
-
Machine Learning on Kubernetes. A practical handbook for building and using a complete open source machine learning platform on Kubernetes
-
Designing Autonomous AI
-
Fundamentals of Data Engineering
-
Data Democratization with Domo. Bring together every component of your business to make better data-driven decisions using Domo
-
The Pandas Workshop. A comprehensive guide to using Python for data analysis with real-world case studies
-
Excel 2021 i Microsoft 365. Analiza i modelowanie danych biznesowych
-
AI-Powered Business Intelligence
-
Practical Simulations for Machine Learning
-
Building Data Science Solutions with Anaconda. A comprehensive starter guide to building robust and complete models
-
Natural Language Processing with Transformers, Revised Edition
-
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
-
Mastering Azure Machine Learning. Execute large-scale end-to-end machine learning with Azure - Second Edition
-
Deep Learning with PyTorch Lightning. Swiftly build high-performance Artificial Intelligence (AI) models using Python
-
The Tableau Workshop. A practical guide to the art of data visualization with Tableau
-
Essential Mathematics for Quantum Computing. A beginner's guide to just the math you need without needless complexities
-
Microsoft Power BI Performance Best Practices. A comprehensive guide to building consistently fast Power BI solutions
-
The Kaggle Book. Data analysis and machine learning for competitive data science
-
Google Analytics w biznesie. Poradnik dla zaawansowanych. Wydanie II
-
Data Algorithms with Spark
-
Getting Started with Amazon SageMaker Studio. Learn to build end-to-end machine learning projects in the SageMaker machine learning IDE
-
Unity Artificial Intelligence Programming. Add powerful, believable, and fun AI entities in your game with the power of Unity - Fifth Edition
-
Simplify Big Data Analytics with Amazon EMR. A beginner’s guide to learning and implementing Amazon EMR for building data analytics solutions
-
Transformers for Natural Language Processing. Build, train, and fine-tune deep neural network architectures for NLP with Python, Hugging Face, and OpenAI's GPT-3, ChatGPT, and GPT-4 - Second Edition
-
Getting Started with Elastic Stack 8.0. Run powerful and scalable data platforms to search, observe, and secure your organization
-
Reproducible Data Science with Pachyderm. Learn how to build version-controlled, end-to-end data pipelines using Pachyderm 2.0
-
Analiza danych behawioralnych przy użyciu języków R i Python
-
Scalable Data Analytics with Azure Data Explorer. Modern ways to query, analyze, and perform real-time data analysis on large volumes of data
-
Machine Learning with PyTorch and Scikit-Learn. Develop machine learning and deep learning models with Python
-
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
-
Machine Learning in Biotechnology and Life Sciences. Build machine learning models using Python and deploy them on the cloud
-
Digital Transformation and Modernization with IBM API Connect. A practical guide to developing, deploying, and managing high-performance and secure hybrid-cloud APIs
-
Google Analytics dla marketingowców. Wydanie III
-
Agile Machine Learning with DataRobot. Automate each step of the machine learning life cycle, from understanding problems to delivering value
-
Optimizing Databricks Workloads. Harness the power of Apache Spark in Azure and maximize the performance of modern big data workloads
-
The TensorFlow Workshop. A hands-on guide to building deep learning models from scratch using real-world datasets
-
Machine Learning for Financial Risk Management with Python
-
Azure Data Scientist Associate Certification Guide. A hands-on guide to machine learning in Azure and passing the Microsoft Certified DP-100 exam
-
Extending Power BI with Python and R. Ingest, transform, enrich, and visualize data using the power of analytical languages
-
Learn Amazon SageMaker. A guide to building, training, and deploying machine learning models for developers and data scientists - Second Edition
-
IBM Cloud Pak for Data. An enterprise platform to operationalize data, analytics, and AI
-
Uczenie głębokie i sztuczna inteligencja. Interaktywny przewodnik ilustrowany
-
Digital Transformation with Dataverse for Teams. Become a citizen developer and lead the digital transformation wave with Microsoft Teams and Power Platform
-
Essential PySpark for Scalable Data Analytics. A beginner's guide to harnessing the power and ease of PySpark 3
-
Machine Learning with Amazon SageMaker Cookbook. 80 proven recipes for data scientists and developers to perform machine learning experiments and deployments
-
Data Engineering with Apache Spark, Delta Lake, and Lakehouse. Create scalable pipelines that ingest, curate, and aggregate complex data in a timely and secure way
-
Reliable Machine Learning
-
Building Data Science Applications with FastAPI. Develop, manage, and deploy efficient machine learning applications with Python
-
LaTeX Beginner's Guide. Create visually appealing texts, articles, and books for business and science using LaTeX - Second Edition