Rohan Chopra, Aniruddha M. Godbole, Nipun Sadvilkar, Muzaffar Bashir Shah, Sohom Ghosh, Dwight Gunning - książki
Tytuły autora: dostępne w księgarni Ebookpoint
-
Learn Power BI. A comprehensive, step-by-step guide for beginners to learn real-world business intelligence - Second Edition
-
TinyML. Wykorzystanie TensorFlow Lite do uczenia maszynowego na Arduino i innych mikrokontrolerach
-
Matematyka dyskretna dla praktyków. Algorytmy i uczenie maszynowe w Pythonie
-
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
-
Extreme DAX. Take your Power BI and Microsoft data analytics skills to the next level
-
Digital Transformation and Modernization with IBM API Connect. A practical guide to developing, deploying, and managing high-performance and secure hybrid-cloud APIs
-
Intelligent Workloads at the Edge. Deliver cyber-physical outcomes with data and machine learning using AWS IoT Greengrass
-
Data Engineering with AWS. Learn how to design and build cloud-based data transformation pipelines using AWS
-
Agile Machine Learning with DataRobot. Automate each step of the machine learning life cycle, from understanding problems to delivering value
-
The TensorFlow Workshop. A hands-on guide to building deep learning models from scratch using real-world datasets
-
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
-
Machine Learning Engineering with Python. Manage the production life cycle of machine learning models using MLOps with practical examples
-
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 for Time-Series with Python. Forecast, predict, and detect anomalies with state-of-the-art machine learning methods
-
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
-
Power Query Cookbook. Use effective and powerful queries in Power BI Desktop and Dataflows to prepare and transform your data
-
Reliable Machine Learning
-
LaTeX Beginner's Guide. Create visually appealing texts, articles, and books for business and science using LaTeX - Second Edition
-
Microsoft Power BI Cookbook. Gain expertise in Power BI with over 90 hands-on recipes, tips, and use cases - Second Edition
-
Practical Weak Supervision
-
Up and Running with Affinity Designer. A practical, easy-to-follow guide to get up to speed with the powerful features of Affinity Designer 1.10
-
Building Data-Driven Applications with Danfo.js. A practical guide to data analysis and machine learning using JavaScript
-
Data Science for Marketing Analytics. A practical guide to forming a killer marketing strategy through data analysis with Python - Second Edition
-
Data Processing with Optimus. Supercharge big data preparation tasks for analytics and machine learning with Optimus using Dask and PySpark
-
Data Analytics Made Easy. Analyze and present data to make informed decisions without writing any code
-
Exploring GPT-3. An unofficial first look at the general-purpose language processing API from OpenAI
-
Machine Learning Engineering with MLflow. Manage the end-to-end machine learning life cycle with MLflow
-
3D Graphics Rendering Cookbook. A comprehensive guide to exploring rendering algorithms in modern OpenGL and Vulkan
-
Getting Started with Streamlit for Data Science. Create and deploy Streamlit web applications from scratch in Python
-
Empowering Organizations with Power Virtual Agents. A practical guide to building intelligent chatbots with Microsoft Power Platform
-
AI and Machine Learning for On-Device Development
-
Sztuczna inteligencja i uczenie maszynowe dla programistów. Praktyczny przewodnik po sztucznej inteligencji
-
Software Architecture Patterns for Serverless Systems. Architecting for innovation with events, autonomous services, and micro frontends
-
Tableau Strategies
-
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
-
Graph Machine Learning. Take graph data to the next level by applying machine learning techniques and algorithms
-
Deep learning dla programistów. Budowanie aplikacji AI za pomocą fastai i PyTorch
-
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
-
97 Things Every Data Engineer Should Know
-
Expert Data Modeling with Power BI. Get the best out of Power BI by building optimized data models for reporting and business needs
-
Machine Learning with BigQuery ML. Create, execute, and improve machine learning models in BigQuery using standard SQL queries
-
Dane grafowe w praktyce. Jak technologie grafowe ułatwiają rozwiązywanie złożonych problemów
-
Machine Learning with the Elastic Stack. Gain valuable insights from your data with Elastic Stack's machine learning features - Second Edition
-
Mastering Tableau 2021. Implement advanced business intelligence techniques and analytics with Tableau - Third Edition
-
Machine learning, Python i data science. Wprowadzenie
-
Distributed Data Systems with Azure Databricks. Create, deploy, and manage enterprise data pipelines
-
Automated Machine Learning with AutoKeras. Deep learning made accessible for everyone with just few lines of coding
-
Interactive Dashboards and Data Apps with Plotly and Dash. Harness the power of a fully fledged frontend web framework in Python – no JavaScript required
-
PyTorch Pocket Reference
-
Machine Learning Automation with TPOT. Build, validate, and deploy fully automated machine learning models with Python
-
Quantum Computing with Silq Programming. Get up and running with quantum computing with the simplicity of this new high-level programming language
-
Hands-On Data Analysis with Pandas. A Python data science handbook for data collection, wrangling, analysis, and visualization - Second Edition
-
Automated Machine Learning with Microsoft Azure. Build highly accurate and scalable end-to-end AI solutions with Azure AutoML
-
Engineering MLOps. Rapidly build, test, and manage production-ready machine learning life cycles at scale
-
Big Data. Krótkie Wprowadzenie 30
-
Skazany na sukces. Kariera w Data Science
-
Cleaning Data for Effective Data Science. Doing the other 80% of the work with Python, R, and command-line tools
-
Scalable Data Streaming with Amazon Kinesis. Design and secure highly available, cost-effective data streaming applications with Amazon Kinesis
-
Effortless App Development with Oracle Visual Builder. Boost productivity by building web and mobile applications efficiently using the drag-and-drop approach
-
Interpretable Machine Learning with Python. Learn to build interpretable high-performance models with hands-on real-world examples
-
Umiejętności analityczne w pracy z danymi i sztuczną inteligencją. Wykorzystywanie najnowszych technologii w rozwijaniu przedsiębiorstwa
-
AWS Certified Machine Learning Specialty: MLS-C01 Certification Guide. The definitive guide to passing the MLS-C01 exam on the very first attempt
-
Deep learning i modelowanie generatywne. Jak nauczyć komputer malowania, pisania, komponowania i grania
-
Hands-On Data Visualization
-
Przetwarzanie języka naturalnego w akcji
-
Automated Machine Learning. Hyperparameter optimization, neural architecture search, and algorithm selection with cloud platforms
-
Managing Microsoft Teams: MS-700 Exam Guide. Configure and manage Microsoft Teams workloads and achieve Microsoft 365 certification with ease
-
Practical Threat Intelligence and Data-Driven Threat Hunting. A hands-on guide to threat hunting with the ATT&CK™ Framework and open source tools
-
Data Pipelines Pocket Reference
-
Python. Machine learning i deep learning. Biblioteki scikit-learn i TensorFlow 2. Wydanie III
-
Machine Learning Using TensorFlow Cookbook. Create powerful machine learning algorithms with TensorFlow
-
Hurtownie danych. Od przetwarzania analitycznego do raportowania. Wydanie II
-
Kluczowe kompetencje specjalisty danych
-
Wykorzystanie sztucznych sieci neuronowych
-
Język R i analiza danych w praktyce. Wydanie II
-
Uczenie maszynowe w aplikacjach. Projektowanie, budowa i wdrażanie
-
Python Data Cleaning Cookbook. Modern techniques and Python tools to detect and remove dirty data and extract key insights
-
Analiza danych w zarządzaniu projektami
-
Kubeflow Operations Guide
-
Practical Fairness
-
Introducing MLOps
-
The Economics of Data, Analytics, and Digital Transformation. The theorems, laws, and empowerments to guide your organization’s digital transformation
-
Codeless Deep Learning with KNIME. Build, train, and deploy various deep neural network architectures using KNIME Analytics Platform
-
Corporate Learning with Moodle Workplace. Explore concepts, implementation, and strategies for adopting Moodle Workplace in your organization
-
Python dla DevOps. Naucz się bezlitośnie skutecznej automatyzacji
-
Quantum Computing in Practice with Qiskit(R) and IBM Quantum Experience(R). Practical recipes for quantum computer coding at the gate and algorithm level with Python
-
Microsoft Excel 2010 Analiza i modelowanie danych biznesowych
-
Microsoft Excel 2013 Budowanie modeli danych przy użyciu PowerPivot
-
Microsoft SQL Server 2012 Analysis Services: Model tabelaryczny BISM
-
Opowieści ukryte w danych. Wyjaśnij dane i wywołaj działania za pomocą narracji
-
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
-
Python Machine Learning By Example. Build intelligent systems using Python, TensorFlow 2, PyTorch, and scikit-learn - Third Edition
-
Machine Learning Design Patterns
-
Artificial Intelligence in Finance
-
Kubeflow for Machine Learning
-
AI and Machine Learning for Coders
-
Learn Quantum Computing with Python and IBM Quantum Experience. A hands-on introduction to quantum computing and writing your own quantum programs with Python
-
Praktyczne uczenie nienadzorowane przy użyciu języka Python
-
Szeregi czasowe. Praktyczna analiza i predykcja z wykorzystaniem statystyki i uczenia maszynowego
-
Wprowadzenie do uczenia maszynowego według Esposito
-
Uczenie maszynowe w Pythonie dla każdego
-
The Self-Service Data Roadmap
-
The Data Science Workshop. Learn how you can build machine learning models and create your own real-world data science projects - Second Edition
-
Myślenie systemowe. Wprowadzenie
-
Power BI i Power Pivot dla Excela. Analiza danych
-
Semantic Modeling for Data
-
The Natural Language Processing Workshop. Confidently design and build your own NLP projects with this easy-to-understand practical guide
-
Analiza statystyczna z IBM SPSS Statistics
-
Technologie informatyczne a prawo. Prolegomena