Master Python Data Engineering With Virtual Ai Tutoring
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Master Python Data Engineering With Virtual Ai Tutoring
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Author : Diego Rodrigues
language : en
Publisher: Diego Rodrigues
Release Date : 2024-11-19
Master Python Data Engineering With Virtual Ai Tutoring written by Diego Rodrigues and has been published by Diego Rodrigues this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-19 with Business & Economics categories.
Imagine acquiring a book and, as a bonus, gaining access to a 24/7 AI-assisted Virtual Tutoring to personalize your learning journey, reinforce knowledge, and receive mentorship for developing and implementing real projects... ...Welcome to the Revolution of Personalized Learning with AI-Assisted Virtual Tutoring! Discover " MASTER PYTHON DATA ENGINEERING: From Fundamentals to Advanced Applications with Virtual AI Tutoring," the essential guide for professionals and enthusiasts who want to master data engineering with Python. This innovative manual, written by Diego Rodrigues, an author with over 140 titles published in six languages, combines high-quality content with the advanced technology of IAGO, a virtual tutor developed and hosted on the OpenAI platform. Innovative Features: Personalized Learning: IAGO adapts the content to your knowledge level, offering detailed explanations and personalized exercises. Immediate Feedback: Receive corrections and suggestions in real time, speeding up your learning process. Interactivity and Engagement: Interact with the tutor via text or voice, making learning more dynamic and motivating. Project Development Mentorship: Get practical guidance to develop and implement real projects, applying the knowledge gained. Total Flexibility: Access the tutor anywhere, anytime, whether on a desktop, notebook, or smartphone with web access. Take advantage of the Limited-Time Launch Promotional Price! Don't miss the opportunity to transform your learning journey with an innovative and effective method. This book has been carefully structured to meet your needs and exceed your expectations, ensuring you are prepared to face challenges and seize opportunities in the field of data engineering. Open the book sample and discover how to access the select club of cutting-edge technology professionals. Take advantage of this unique opportunity and achieve your goals! TAGS: data engineering automation science big Pandas NumPy Dask SQLAlchemy web scraping BeautifulSoup Scrapy APIs ETL DataOps Data Lakes Data Warehouses AWS Google Cloud Microsoft Azure Hadoop Spark machine learning artificial intelligence data pipelines data visualization Matplotlib Seaborn data analysis relational databases NoSQL MongoDB Apache Airflow Kafka real-time data governance data security compliance mentorship Diego Rodrigues Tableau Power BI Snowflake Informatica Alation Talend Apache Flink Jupyter Notebooks DevOps Databricks Cloudera Hortonworks Teradata IBM Cloud Oracle Cloud Salesforce SAP HANA ElasticSearch Redis Kubernetes Docker Jenkins GitHub GitLab Continuous Integration Continuous Deployment CI/CD digital transformation predictive analysis business intelligence IoT Internet of Things smart cities connected health Industry 4.0 fintechs retail education marketing competitive intelligence data science automated testing custom reports operational efficiency Python Java Linux Kali Linux HTML ASP.NET Ada Assembly Language BASIC Borland Delphi C C# C++ CSS Cobol Compilers DHTML Fortran General HTML Java JavaScript LISP PHP Pascal Perl Prolog RPG Ruby SQL Swift UML Elixir Haskell VBScript Visual Basic XHTML XML XSL Django Flask Ruby on Rails Angular React Vue.js Node.js Laravel Spring Hibernate .NET Core Express.js TensorFlow PyTorch Jupyter Notebook Keras Bootstrap Foundation jQuery SASS LESS Scala Groovy MATLAB R Objective-C Rust Go Kotlin TypeScript Elixir Dart SwiftUI Xamarin React Native NumPy Pandas SciPy Matplotlib Seaborn D3.js OpenCV NLTK PySpark BeautifulSoup Scikit-learn XGBoost CatBoost LightGBM FastAPI Celery Tornado Redis RabbitMQ Kubernetes Docker Jenkins Terraform Ansible Vagrant GitHub GitLab CircleCI Travis CI Linear Regression Logistic Regression Decision Trees Random Forests FastAPI AI ML K-Means Clustering Support Vector Tornado Machines Gradient Boosting Neural Networks LSTMs CNNs GANs ANDROID IOS MACOS WINDOWS Nmap Metasploit Framework Wireshark Aircrack-ng John the Ripper Burp Suite SQLmap Maltego Autopsy Volatility IDA Pro OllyDbg YARA Snort ClamAV iOS Netcat Tcpdump Foremost Cuckoo Sandbox Fierce HTTrack Kismet Hydra Nikto OpenVAS Nessus ZAP Radare2 Binwalk GDB OWASP Amass Dnsenum Dirbuster Wpscan Responder Setoolkit Searchsploit Recon-ng BeEF aws google cloud ibm azure databricks nvidia meta x Power BI IoT CI/CD Hadoop Spark Pandas NumPy Dask SQLAlchemy web scraping mysql big data science openai chatgpt Handler RunOnUiThread()Qiskit Q# Cassandra Bigtable VIRUS MALWARE docker kubernetes Kali Linux Nmap Metasploit Wireshark information security pen test cybersecurity Linux distributions ethical hacking vulnerability analysis system exploration wireless attacks web application security malware analysis social engineering Android iOS Social Engineering Toolkit SET computer science IT professionals cybersecurity careers cybersecurity expertise cybersecurity library cybersecurity training Linux operating systems cybersecurity tools ethical hacking tools security testing penetration test cycle security concepts mobile security cybersecurity fundamentals cybersecurity techniques skills cybersecurity industry global cybersecurity trends Kali Linux tools education innovation penetration test tools best practices global companies cybersecurity solutions IBM Google Microsoft AWS Cisco Oracle consulting cybersecurity framework network security courses cybersecurity tutorials Linux security challenges landscape cloud security threats compliance research technology React Native Flutter Ionic Xamarin HTML CSS JavaScript Java Kotlin Swift Objective-C Web Views Capacitor APIs REST GraphQL Firebase Redux Provider Angular Vue.js Bitrise GitHub Actions Material Design Cupertino Fastlane Appium Selenium Jest CodePush Firebase Expo Visual Studio C# .NET Azure Google Play App Store CodePush IoT AR VR
Master Python Data Science Wiith Ai Virtual Tutoring
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Author : Diego Rodrigues
language : en
Publisher: Diego Rodrigues
Release Date : 2024-11-19
Master Python Data Science Wiith Ai Virtual Tutoring written by Diego Rodrigues and has been published by Diego Rodrigues this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-19 with Business & Economics categories.
Imagine acquiring a complete book and, as a bonus, receiving access to a 24/7 AI-assisted Virtual Tutoring to personalize your learning journey, knowledge consolidation, and mentorship for the development and implementation of real projects... ... Welcome to the Revolution of Personalized Learning with AI-Assisted Virtual Tutoring! Discover "MASTER PYTHON: DATA SCIENCE From Fundamentals to Advanced Applications with AI Virtual Tutoring" the essential guide for professionals and enthusiasts who wish to master data science with Python. This innovative manual, written by Diego Rodrigues, an author with over 140 titles published in six languages, combines high-quality content with the advanced technology of IAGO, a virtual tutor developed and hosted on the OpenAI platform. The book begins with a comprehensive introduction to data science, highlighting the importance of the field and the crucial role Python plays. Next, it covers the fundamentals of Python, including basic syntax, data structures, and control flow, laying a solid foundation for subsequent chapters. You will learn essential data manipulation and cleaning techniques using libraries like Pandas and NumPy, ensuring your data is ready for analysis. Then, you will explore exploratory data analysis (EDA) with tools like Matplotlib and Seaborn to discover valuable patterns and insights. Data visualization is deepened with the use of Plotly to create interactive charts and Dash to develop dynamic dashboards. The book progresses to machine learning, introducing basic concepts and types of learning, followed by data preparation and model implementation with Scikit-Learn. Linear and polynomial regression techniques are explained in detail, along with model performance evaluation. You will also delve into advanced machine learning with chapters on classification, clustering, and dimensionality reduction. Natural language processing (NLP) techniques are covered, using libraries like NLTK and SpaCy. The deep learning section covers everything from basic neural networks to advanced applications with TensorFlow and Keras, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The book also explores big data, teaching how to work with large volumes of data using Hadoop and Spark with Python. It concludes with a comprehensive guide on conducting a data science project from start to finish and discusses ethics and responsibility in data science, addressing best practices and regulations. Take advantage of the Limited Time Launch Promotional Price! Open the book sample and discover how to join the select club of cutting-edge technology professionals. Take this unique opportunity and achieve your goals! TAGS data science manipulation data analysis visualization Pandas NumPy Matplotlib Seaborn Plotly Dash machine learning deep learning Scikit-Learn TensorFlow Keras big data Hadoop Spark exploratory analysis EDA models regression classification clustering NLP natural language processing convolutional neural networks CNNs recurrent RNNs supervised learning unsupervised learning reinforcement learning digital transformation predictive analysis artificial intelligence Diego Rodrigues applied data science real projects virtual tutoring OpenAI IAGO task automation modeling prediction advanced techniques SQL time series analysis social network analysis interactive data visualization data storytelling Python programming data science ethics data privacy regulations cybersecurity data collection data processing data engineering statistical analysis real-time visualization automated reports data-driven aws google ibm meta azure Python Java Linux Kali Linux HTML ASP.NET Ada Assembly Language BASIC Borland Delphi C C# C++ CSS Cobol Compilers DHTML Fortran General HTML Java JavaScript LISP PHP Pascal Perl Prolog RPG Ruby SQL Swift UML Elixir Haskell VBScript Visual Basic XHTML XML XSL Django Flask Ruby on Rails Angular React Vue.js Node.js Laravel Spring Hibernate .NET Core Express.js TensorFlow PyTorch Jupyter Notebook Keras Bootstrap Foundation jQuery SASS LESS Scala Groovy MATLAB R Objective-C Rust Go Kotlin TypeScript Elixir Dart SwiftUI Xamarin React Native NumPy Pandas SciPy Matplotlib Seaborn D3.js OpenCV NLTK PySpark BeautifulSoup Scikit-learn XGBoost CatBoost LightGBM FastAPI Celery Tornado Redis RabbitMQ Kubernetes Docker Jenkins Terraform Ansible Vagrant GitHub GitLab CircleCI Travis CI Linear Regression Logistic Regression Decision Trees Random Forests FastAPI AI ML K-Means Clustering Support Vector Tornado Machines Gradient Boosting Neural Networks LSTMs CNNs GANs ANDROID IOS MACOS WINDOWS Nmap Metasploit Framework Wireshark Aircrack-ng John the Ripper Burp Suite SQLmap Maltego Autopsy Volatility IDA Pro OllyDbg YARA Snort ClamAV iOS Netcat Tcpdump Foremost Cuckoo Sandbox Fierce HTTrack Kismet Hydra Nikto OpenVAS Nessus ZAP Radare2 Binwalk GDB OWASP Amass Dnsenum Dirbuster Wpscan Responder Setoolkit Searchsploit Recon-ng BeEF aws google cloud ibm azure databricks nvidia meta x Power BI IoT CI/CD Hadoop Spark Pandas NumPy Dask SQLAlchemy web scraping mysql big data science openai chatgpt Handler RunOnUiThread()Qiskit Q# Cassandra Bigtable VIRUS MALWARE docker kubernetes Kali Linux Nmap Metasploit Wireshark information security pen test cybersecurity Linux distributions ethical hacking vulnerability analysis system exploration wireless attacks web application security malware analysis social engineering Android iOS Social Engineering Toolkit SET computer science IT professionals cybersecurity careers cybersecurity expertise cybersecurity library cybersecurity training Linux operating systems cybersecurity tools ethical hacking tools security testing penetration test cycle security concepts mobile security cybersecurity fundamentals cybersecurity techniques skills cybersecurity industry global cybersecurity trends Kali Linux tools education innovation penetration test tools best practices global companies cybersecurity solutions IBM Google Microsoft AWS Cisco Oracle consulting cybersecurity framework network security courses cybersecurity tutorials Linux security challenges landscape cloud security threats compliance research technology React Native Flutter Ionic Xamarin HTML CSS JavaScript Java Kotlin Swift Objective-C Web Views Capacitor APIs REST GraphQL Firebase Redux Provider Angular Vue.js Bitrise GitHub Actions Material Design Cupertino Fastlane Appium Selenium Jest CodePush Firebase Expo Visual Studio C# .NET Azure Google Play App Store CodePush IoT AR VR
Master Python Cybersecurity With Ai Virtual Tutoring
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Author : Diego Rodrigues
language : en
Publisher: Diego Rodrigues
Release Date : 2024-11-19
Master Python Cybersecurity With Ai Virtual Tutoring written by Diego Rodrigues and has been published by Diego Rodrigues this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-19 with Business & Economics categories.
Imagine acquiring a book and, as a bonus, getting access to a 24/7 AI-assisted Virtual Tutoring to personalize your learning journey, consolidate knowledge, and receive mentorship for developing and implementing real projects... ... Welcome to the Revolution of Personalized Learning with AI-Assisted Virtual Tutoring! Discover "MASTER PYTHON CYBERSECURITY: From Fundamentals to Advanced Applications with AI Virtual Tutoring," the essential guide for professionals and enthusiasts aiming to master automation and cybersecurity with Python. This innovative manual, written by Diego Rodrigues, a renowned author with over 140 titles published in six languages, combines high-quality content with advanced technology from IAGO, a virtual tutor developed and hosted on the OpenAI platform. Innovative Features: - Personalized Learning: IAGO adapts the content according to your knowledge level, offering detailed explanations and personalized exercises. - Immediate Feedback: Receive corrections and suggestions in real-time, accelerating your learning process. - Interactivity and Engagement: Interact with the tutor via text or voice, making the study more dynamic and motivating. - Mentorship for Project Development: Get practical guidance to develop and implement real projects, applying the knowledge acquired. - Total Flexibility: Access the tutor anywhere and anytime, whether on desktop, notebook, or smartphone with web access. Take Advantage of the Limited Time Launch Promotional Price! Don't miss the opportunity to transform your learning journey with an innovative and effective method. This book has been carefully structured to meet your needs and exceed your expectations, ensuring you are prepared to face challenges and seize opportunities in the field of automation and cybersecurity. Open the book sample and discover how to join the select club of cutting-edge technology professionals. Take advantage of this unique opportunity and achieve your goals! TAGS hacking automation cybersecurity Scapy Requests BeautifulSoup Nmap Metasploit ethical hacking penetration testing forensic analysis vulnerabilities network security encryption cyber attacks data protection network monitoring security audit advanced techniques cyber defense information security system security invasion protection Diego Rodrigues CyberExtreme malware virus phishing DDoS attacks artificial intelligence machine learning blockchain DevOps DevSecOps SCADA security industry 4.0 connected health smart cities vulnerability analysis web application security SQL Injection XSS CSRF patch management software update password policy multi-factor authentication MFA encryption AES RSA ECC cloud security AWS Microsoft Azure Google Cloud IBM Cloud Palo Alto Networks Cisco Systems Check Point Symantec McAfee Splunk CrowdStrike Fortinet Tenable Nessus OpenVAS Wi-Fi security LTE 5G endpoints APIs osint encryption at rest risk-based risk management log analysis continuous monitoring threat response behavior analysis security tools best practices innovation digital transformation big data hack Python Java Linux Kali Linux HTML ASP.NET Ada Assembly Language BASIC Borland Delphi C C# C++ CSS Cobol Compilers DHTML Fortran General HTML Java JavaScript LISP PHP Pascal Perl Prolog RPG Ruby SQL Swift UML Elixir Haskell VBScript Visual Basic XHTML XML XSL Django Flask Ruby on Rails Angular React Vue.js Node.js Laravel Spring Hibernate .NET Core Express.js TensorFlow PyTorch Jupyter Notebook Keras Bootstrap Foundation jQuery SASS LESS Scala Groovy MATLAB R Objective-C Rust Go Kotlin TypeScript Elixir Dart SwiftUI Xamarin React Native NumPy Pandas SciPy Matplotlib Seaborn D3.js OpenCV NLTK PySpark BeautifulSoup Scikit-learn XGBoost CatBoost LightGBM FastAPI Celery Tornado Redis RabbitMQ Kubernetes Docker Jenkins Terraform Ansible Vagrant GitHub GitLab CircleCI Travis CI Linear Regression Logistic Regression Decision Trees Random Forests FastAPI AI ML K-Means Clustering Support Vector Tornado Machines Gradient Boosting Neural Networks LSTMs CNNs GANs ANDROID IOS MACOS WINDOWS Nmap Metasploit Framework Wireshark Aircrack-ng John the Ripper Burp Suite SQLmap Maltego Autopsy Volatility IDA Pro OllyDbg YARA Snort ClamAV iOS Netcat Tcpdump Foremost Cuckoo Sandbox Fierce HTTrack Kismet Hydra Nikto OpenVAS Nessus ZAP Radare2 Binwalk GDB OWASP Amass Dnsenum Dirbuster Wpscan Responder Setoolkit Searchsploit Recon-ng BeEF aws google cloud ibm azure databricks nvidia meta x Power BI IoT CI/CD Hadoop Spark Pandas NumPy Dask SQLAlchemy web scraping mysql big data science openai chatgpt Handler RunOnUiThread()Qiskit Q# Cassandra Bigtable VIRUS MALWARE docker kubernetes Kali Linux Nmap Metasploit Wireshark information security pen test cybersecurity Linux distributions ethical hacking vulnerability analysis system exploration wireless attacks web application security malware analysis social engineering Android iOS Social Engineering Toolkit SET computer science IT professionals cybersecurity careers cybersecurity expertise cybersecurity library cybersecurity training Linux operating systems cybersecurity tools ethical hacking tools security testing penetration test cycle security concepts mobile security cybersecurity fundamentals cybersecurity techniques skills cybersecurity industry global cybersecurity trends Kali Linux tools education innovation penetration test tools best practices global companies cybersecurity solutions IBM Google Microsoft AWS Cisco Oracle consulting cybersecurity framework network security courses cybersecurity tutorials Linux security challenges landscape cloud security threats compliance research technology React Native Flutter Ionic Xamarin HTML CSS JavaScript Java Kotlin Swift Objective-C Web Views Capacitor APIs REST GraphQL Firebase Redux Provider Angular Vue.js Bitrise GitHub Actions Material Design Cupertino Fastlane Appium Selenium Jest CodePush Firebase Expo Visual Studio C# .NET Azure Google Play App Store CodePush IoT AR VR GITHUB
Learn Django With Ai Virtual Tutoring
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Author : Diego Rodrigues
language : en
Publisher: Diego Rodrigues
Release Date : 2024-11-01
Learn Django With Ai Virtual Tutoring written by Diego Rodrigues and has been published by Diego Rodrigues this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-11-01 with Computers categories.
Imagine purchasing a complete book and, as a bonus, gaining lifetime access to a 24/7 Virtual Tutoring system powered by Artificial Intelligence — capable of personalizing your learning journey, strengthening knowledge retention, and offering technical mentoring for real-world project development… …Welcome to the Revolution of Personalized Learning with AI! A multilingual and interactive innovation, created exclusively for the readers of StudioD21 AI Learning Academy. “Choose Your Language”: Português · English · Español · Français · Deutsch · Italiano · العربية · 中文 · हिंदी · 日本語 · 한국어 · Türkçe · Русский You're not just reading a book. You’re stepping into a hands-on experience guided by an embedded AI Agent — one that turns knowledge into practice, questions into progress, and reading into action. Welcome to "LEARN DJANGO: Build Fast and Secure Web Apps with Python - 2024 Edition," the ultimate guide to transforming the way you develop web applications. Written by Diego Rodrigues, an author with vast experience and over 180 titles published in six languages, this book offers a comprehensive and practical journey to mastering Django, one of the most powerful and flexible frameworks on the market, essential for creating robust and scalable web applications. Whether you are a beginner in web development or an experienced professional, this complete guide covers everything from installing and configuring Django to building secure, high-performance, and production-ready web applications. Through a structured and didactic approach, you will be guided from setting up the development environment to creating robust APIs using the Django Rest Framework. You will learn to master industry best practices such as using the Model-View-Template (MVT) architecture, creating authentication systems, handling static files and media, as well as working with automated testing and optimizing database queries. The book also delves into advanced techniques for integrating with cloud platforms, application security, and efficient deployment. Throughout the 30 chapters, you will be challenged with practical exercises at the end of each section, applying the knowledge gained to build real, scalable solutions for the cloud computing era and distributed systems. With practical examples and clear explanations, this book is an essential resource for those looking to stand out in the Python web development market. If you are ready to take your skills to the next level, "LEARN DJANGO: Build Fast and Secure Web Apps with Python" is the guide you need to create the future of technology with innovative and high-impact solutions. TAGS: Python Java Linux Kali Linux HTML ASP.NET Ada Assembly Language BASIC Borland Delphi C C# C++ CSS Cobol Compilers DHTML Fortran General HTML Java JavaScript LISP PHP Pascal Perl Prolog RPG Ruby SQL Swift UML Elixir Haskell VBScript Visual Basic XHTML XML XSL Django Flask Ruby on Rails Angular React Vue.js Node.js Laravel Spring Hibernate .NET Core Express.js TensorFlow PyTorch Jupyter Notebook Keras Bootstrap Foundation jQuery SASS LESS Scala Groovy MATLAB R Objective-C Rust Go Kotlin TypeScript Elixir Dart SwiftUI Xamarin React Native NumPy Pandas SciPy Matplotlib Seaborn D3.js OpenCV NLTK PySpark BeautifulSoup Scikit-learn XGBoost CatBoost LightGBM FastAPI Celery Tornado Redis RabbitMQ Kubernetes Docker Jenkins Terraform Ansible Vagrant GitHub GitLab CircleCI Travis CI Linear Regression Logistic Regression Decision Trees Random Forests FastAPI AI ML K-Means Clustering Support Vector Tornado Machines Gradient Boosting Neural Networks LSTMs CNNs GANs ANDROID IOS MACOS WINDOWS Nmap Metasploit Framework Wireshark Aircrack-ng John the Ripper Burp Suite SQLmap Maltego Autopsy Volatility IDA Pro OllyDbg YARA Snort ClamAV iOS Netcat Tcpdump Foremost Cuckoo Sandbox Fierce HTTrack Kismet Hydra Nikto OpenVAS Nessus ZAP Radare2 Binwalk GDB OWASP Amass Dnsenum Dirbuster Wpscan Responder Setoolkit Searchsploit Recon-ng BeEF aws google cloud ibm azure databricks nvidia meta x Power BI IoT CI/CD Hadoop Spark Pandas NumPy Dask SQLAlchemy web scraping mysql big data science openai chatgpt Handler RunOnUiThread()Qiskit Q# Cassandra Bigtable VIRUS MALWARE docker kubernetes Kali Linux Nmap Metasploit Wireshark information security pen test cybersecurity Linux distributions ethical hacking vulnerability analysis system exploration wireless attacks web application security malware analysis social engineering Android iOS Social Engineering Toolkit SET computer science IT professionals cybersecurity careers cybersecurity expertise cybersecurity library cybersecurity training Linux operating systems cybersecurity tools ethical hacking tools security testing penetration test cycle security concepts mobile security cybersecurity fundamentals cybersecurity techniques cybersecurity skills cybersecurity industry global cybersecurity trends Kali Linux tools cybersecurity education cybersecurity innovation penetration test tools cybersecurity best practices global cybersecurity companies cybersecurity solutions IBM Google Microsoft AWS Cisco Oracle cybersecurity consulting cybersecurity framework network security cybersecurity courses cybersecurity tutorials Linux security cybersecurity challenges cybersecurity landscape cloud security cybersecurity threats cybersecurity compliance cybersecurity research cybersecurity technology
Hands On Transfer Learning With Python
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Author : Dipanjan Sarkar
language : en
Publisher: Packt Publishing Ltd
Release Date : 2018-08-31
Hands On Transfer Learning With Python written by Dipanjan Sarkar and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-08-31 with Computers categories.
Deep learning simplified by taking supervised, unsupervised, and reinforcement learning to the next level using the Python ecosystem Key Features Build deep learning models with transfer learning principles in Python implement transfer learning to solve real-world research problems Perform complex operations such as image captioning neural style transfer Book Description Transfer learning is a machine learning (ML) technique where knowledge gained during training a set of problems can be used to solve other similar problems. The purpose of this book is two-fold; firstly, we focus on detailed coverage of deep learning (DL) and transfer learning, comparing and contrasting the two with easy-to-follow concepts and examples. The second area of focus is real-world examples and research problems using TensorFlow, Keras, and the Python ecosystem with hands-on examples. The book starts with the key essential concepts of ML and DL, followed by depiction and coverage of important DL architectures such as convolutional neural networks (CNNs), deep neural networks (DNNs), recurrent neural networks (RNNs), long short-term memory (LSTM), and capsule networks. Our focus then shifts to transfer learning concepts, such as model freezing, fine-tuning, pre-trained models including VGG, inception, ResNet, and how these systems perform better than DL models with practical examples. In the concluding chapters, we will focus on a multitude of real-world case studies and problems associated with areas such as computer vision, audio analysis and natural language processing (NLP). By the end of this book, you will be able to implement both DL and transfer learning principles in your own systems. What you will learn Set up your own DL environment with graphics processing unit (GPU) and Cloud support Delve into transfer learning principles with ML and DL models Explore various DL architectures, including CNN, LSTM, and capsule networks Learn about data and network representation and loss functions Get to grips with models and strategies in transfer learning Walk through potential challenges in building complex transfer learning models from scratch Explore real-world research problems related to computer vision and audio analysis Understand how transfer learning can be leveraged in NLP Who this book is for Hands-On Transfer Learning with Python is for data scientists, machine learning engineers, analysts and developers with an interest in data and applying state-of-the-art transfer learning methodologies to solve tough real-world problems. Basic proficiency in machine learning and Python is required.
Practical Data Science With Python
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Author : Nathan George
language : en
Publisher: Packt Publishing Ltd
Release Date : 2021-09-30
Practical Data Science With Python written by Nathan George and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-30 with Computers categories.
Learn to effectively manage data and execute data science projects from start to finish using Python Key FeaturesUnderstand and utilize data science tools in Python, such as specialized machine learning algorithms and statistical modelingBuild a strong data science foundation with the best data science tools available in PythonAdd value to yourself, your organization, and society by extracting actionable insights from raw dataBook Description Practical Data Science with Python teaches you core data science concepts, with real-world and realistic examples, and strengthens your grip on the basic as well as advanced principles of data preparation and storage, statistics, probability theory, machine learning, and Python programming, helping you build a solid foundation to gain proficiency in data science. The book starts with an overview of basic Python skills and then introduces foundational data science techniques, followed by a thorough explanation of the Python code needed to execute the techniques. You'll understand the code by working through the examples. The code has been broken down into small chunks (a few lines or a function at a time) to enable thorough discussion. As you progress, you will learn how to perform data analysis while exploring the functionalities of key data science Python packages, including pandas, SciPy, and scikit-learn. Finally, the book covers ethics and privacy concerns in data science and suggests resources for improving data science skills, as well as ways to stay up to date on new data science developments. By the end of the book, you should be able to comfortably use Python for basic data science projects and should have the skills to execute the data science process on any data source. What you will learnUse Python data science packages effectivelyClean and prepare data for data science work, including feature engineering and feature selectionData modeling, including classic statistical models (such as t-tests), and essential machine learning algorithms, such as random forests and boosted modelsEvaluate model performanceCompare and understand different machine learning methodsInteract with Excel spreadsheets through PythonCreate automated data science reports through PythonGet to grips with text analytics techniquesWho this book is for The book is intended for beginners, including students starting or about to start a data science, analytics, or related program (e.g. Bachelor’s, Master’s, bootcamp, online courses), recent college graduates who want to learn new skills to set them apart in the job market, professionals who want to learn hands-on data science techniques in Python, and those who want to shift their career to data science. The book requires basic familiarity with Python. A "getting started with Python" section has been included to get complete novices up to speed.
The Artificial Intelligence Infrastructure Workshop
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Author : Chinmay Arankalle
language : en
Publisher: Packt Publishing Ltd
Release Date : 2020-08-17
The Artificial Intelligence Infrastructure Workshop written by Chinmay Arankalle and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-08-17 with Computers categories.
Explore how a data storage system works – from data ingestion to representation Key FeaturesUnderstand how artificial intelligence, machine learning, and deep learning are different from one anotherDiscover the data storage requirements of different AI apps using case studiesExplore popular data solutions such as Hadoop Distributed File System (HDFS) and Amazon Simple Storage Service (S3)Book Description Social networking sites see an average of 350 million uploads daily - a quantity impossible for humans to scan and analyze. Only AI can do this job at the required speed, and to leverage an AI application at its full potential, you need an efficient and scalable data storage pipeline. The Artificial Intelligence Infrastructure Workshop will teach you how to build and manage one. The Artificial Intelligence Infrastructure Workshop begins taking you through some real-world applications of AI. You'll explore the layers of a data lake and get to grips with security, scalability, and maintainability. With the help of hands-on exercises, you'll learn how to define the requirements for AI applications in your organization. This AI book will show you how to select a database for your system and run common queries on databases such as MySQL, MongoDB, and Cassandra. You'll also design your own AI trading system to get a feel of the pipeline-based architecture. As you learn to implement a deep Q-learning algorithm to play the CartPole game, you'll gain hands-on experience with PyTorch. Finally, you'll explore ways to run machine learning models in production as part of an AI application. By the end of the book, you'll have learned how to build and deploy your own AI software at scale, using various tools, API frameworks, and serialization methods. What you will learnGet to grips with the fundamentals of artificial intelligenceUnderstand the importance of data storage and architecture in AI applicationsBuild data storage and workflow management systems with open source toolsContainerize your AI applications with tools such as DockerDiscover commonly used data storage solutions and best practices for AI on Amazon Web Services (AWS)Use the AWS CLI and AWS SDK to perform common data tasksWho this book is for If you are looking to develop the data storage skills needed for machine learning and AI and want to learn AI best practices in data engineering, this workshop is for you. Experienced programmers can use this book to advance their career in AI. Familiarity with programming, along with knowledge of exploratory data analysis and reading and writing files using Python will help you to understand the key concepts covered.
Machine Learning Hero
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Author : Cuantum Technologies LLC
language : en
Publisher: Packt Publishing Ltd
Release Date : 2025-01-16
Machine Learning Hero written by Cuantum Technologies LLC and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-01-16 with Computers categories.
Learn machine learning through hands-on Python projects, covering core concepts, essential libraries, and real-world applications for aspiring data scientists. Key Features Comprehensive coverage of machine learning fundamentals and advanced topics Real-world projects to apply skills in practical scenarios Integration of Python libraries for data science and AI development Book DescriptionThis book takes you on a journey through the world of machine learning, beginning with foundational concepts such as supervised and unsupervised learning, and progressing to advanced topics like feature engineering, hyperparameter tuning, and dimensionality reduction. Each chapter blends theory with practical exercises to ensure a deep understanding of the material. The book emphasizes Python, introducing essential libraries like NumPy, Pandas, Matplotlib, and Scikit-learn, along with deep learning frameworks like TensorFlow and PyTorch. You’ll learn to preprocess data, visualize insights, and build models capable of tackling complex datasets. Hands-on coding examples and exercises reinforce concepts and help bridge the gap between knowledge and application. In the final chapters, you'll work on real-world projects like predictive analytics, clustering, and regression. These projects are designed to provide a practical context for the techniques learned and equip you with actionable skills for data science and AI roles. By the end, you'll be prepared to apply machine learning principles to solve real-world challenges with confidence.What you will learn Build machine learning models using Python libraries Apply feature engineering and preprocessing techniques Visualize datasets with Matplotlib and Seaborn Optimize machine learning models with hyperparameter tuning Implement clustering and dimensionality reduction methods Work on real-world projects for practical experience Who this book is for Aspiring data scientists, software developers, and tech enthusiasts seeking to master machine learning concepts and Python libraries. Basic Python knowledge is recommended but not required, as foundational topics are covered.
Learn Solidity With Ai Virtual Tutoring
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Author : Diego Rodrigues
language : en
Publisher: StudioD21
Release Date : 2025-04-07
Learn Solidity With Ai Virtual Tutoring written by Diego Rodrigues and has been published by StudioD21 this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-04-07 with Business & Economics categories.
Imagine purchasing a complete book and, as a bonus, gaining lifetime access to a 24/7 Virtual Tutoring system powered by Artificial Intelligence — capable of personalizing your learning journey, strengthening knowledge retention, and offering technical mentoring for real-world project development… Welcome to the Revolution of Personalized Learning with AI! A multilingual and interactive innovation, created exclusively for the readers of StudioD21 AI Learning Academy. “Choose Your Language”: Português · English · Español · Français · Deutsch · Italiano · العربية · 中文 · हिंदी · 日本語 · 한국어 · Türkçe · Русский You're not just reading a book. You’re stepping into a hands-on experience guided by an embedded AI Agent — one that turns knowledge into practice, questions into progress, and reading into action. Dive into the world of blockchain development with LEARN SOLIDITY: From Basics to Practical Applications - 2025 Edition. This essential guide, written by Diego Rodrigues, a best-selling author with over 180 books published in six languages, is your key to mastering Solidity, the language powering smart contracts on Ethereum and other blockchain platforms. Designed for professionals, students, and enthusiasts, this manual combines theory and practice, covering everything from basic concepts like variables and structures to advanced topics such as ERC standards and security against vulnerabilities. Each chapter is carefully crafted to provide progressive and accessible learning, ensuring you can directly apply the knowledge gained to real-world projects. Discover how to create efficient and secure smart contracts, explore fungible and non-fungible tokens, implement DeFi solutions, and even develop Decentralized Autonomous Organizations (DAOs). With practical examples, detailed scripts, and optimization strategies, this book is an indispensable tool for turning ideas into reality in the decentralized world. LEARN SOLIDITY is not just a technical manual; it’s an invitation to shape the future of technology and explore the endless possibilities of blockchain. Your journey to mastering smart contracts begins here! TAGS: Python Java Linux Kali HTML ASP.NET Ada Assembly BASIC Borland Delphi C C# C++ CSS Cobol Compilers DHTML Fortran General JavaScript LISP PHP Pascal Perl Prolog RPG Ruby SQL Swift UML Elixir Haskell VBScript Visual Basic XHTML XML XSL Django Flask Ruby on Rails Angular React Vue.js Node.js Laravel Spring Hibernate .NET Core Express.js TensorFlow PyTorch Jupyter Notebook Keras Bootstrap Foundation jQuery SASS LESS Scala Groovy MATLAB R Objective-C Rust Go Kotlin TypeScript Dart SwiftUI Xamarin React Native NumPy Pandas SciPy Matplotlib Seaborn D3.js OpenCV NLTK PySpark BeautifulSoup Scikit-learn XGBoost CatBoost LightGBM FastAPI Redis RabbitMQ Kubernetes Docker Jenkins Terraform Ansible Vagrant GitHub GitLab CircleCI Regression Logistic Regression Decision Trees Random Forests AI ML K-Means Clustering Support Vector Machines Gradient Boosting Neural Networks LSTMs CNNs GANs ANDROID IOS MACOS WINDOWS Nmap Metasploit Framework Wireshark Aircrack-ng John the Ripper Burp Suite SQLmap Maltego Autopsy Volatility IDA Pro OllyDbg YARA Snort ClamAV Netcat Tcpdump Foremost Cuckoo Sandbox Fierce HTTrack Kismet Hydra Nikto OpenVAS Nessus ZAP Radare2 Binwalk GDB OWASP Amass Dnsenum Dirbuster Wpscan Responder Setoolkit Searchsploit Recon-ng BeEF AWS Google Cloud IBM Azure Databricks Nvidia Meta Power BI IoT CI/CD Hadoop Spark Dask SQLAlchemy Web Scraping MySQL Big Data Science OpenAI ChatGPT Handler RunOnUiThread() Qiskit Q# Cassandra Bigtable VIRUS MALWARE Information Pen Test Cybersecurity Linux Distributions Ethical Hacking Vulnerability Analysis System Exploration Wireless Attacks Web Application Security Malware Analysis Social Engineering Social Engineering Toolkit SET Computer Science IT Professionals Careers Expertise Library Training Operating Systems Security Testing Penetration Test Cycle Mobile Techniques Industry Global Trends Tools Framework Network Security Courses Tutorials Challenges Landscape Cloud Threats Compliance Research Technology Flutter Ionic Web Views Capacitor APIs REST GraphQL Firebase Redux Provider Bitrise Actions Material Design Cupertino Fastlane Appium Selenium Jest Visual Studio AR VR sql mysql
Data Centric Machine Learning With Python
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Author : Jonas Christensen
language : en
Publisher: Packt Publishing Ltd
Release Date : 2024-02-29
Data Centric Machine Learning With Python written by Jonas Christensen and has been published by Packt Publishing Ltd this book supported file pdf, txt, epub, kindle and other format this book has been release on 2024-02-29 with Computers categories.
Join the data-centric revolution and master the concepts, techniques, and algorithms shaping the future of AI and ML development, using Python Key Features Grasp the principles of data centricity and apply them to real-world scenarios Gain experience with quality data collection, labeling, and synthetic data creation using Python Develop essential skills for building reliable, responsible, and ethical machine learning solutions Purchase of the print or Kindle book includes a free PDF eBook Book DescriptionIn the rapidly advancing data-driven world where data quality is pivotal to the success of machine learning and artificial intelligence projects, this critically timed guide provides a rare, end-to-end overview of data-centric machine learning (DCML), along with hands-on applications of technical and non-technical approaches to generating deeper and more accurate datasets. This book will help you understand what data-centric ML/AI is and how it can help you to realize the potential of ‘small data’. Delving into the building blocks of data-centric ML/AI, you’ll explore the human aspects of data labeling, tackle ambiguity in labeling, and understand the role of synthetic data. From strategies to improve data collection to techniques for refining and augmenting datasets, you’ll learn everything you need to elevate your data-centric practices. Through applied examples and insights for overcoming challenges, you’ll get a roadmap for implementing data-centric ML/AI in diverse applications in Python. By the end of this book, you’ll have developed a profound understanding of data-centric ML/AI and the proficiency to seamlessly integrate common data-centric approaches in the model development lifecycle to unlock the full potential of your machine learning projects by prioritizing data quality and reliability.What you will learn Understand the impact of input data quality compared to model selection and tuning Recognize the crucial role of subject-matter experts in effective model development Implement data cleaning, labeling, and augmentation best practices Explore common synthetic data generation techniques and their applications Apply synthetic data generation techniques using common Python packages Detect and mitigate bias in a dataset using best-practice techniques Understand the importance of reliability, responsibility, and ethical considerations in ML/AI Who this book is for This book is for data science professionals and machine learning enthusiasts looking to understand the concept of data-centricity, its benefits over a model-centric approach, and the practical application of a best-practice data-centric approach in their work. This book is also for other data professionals and senior leaders who want to explore the tools and techniques to improve data quality and create opportunities for small data ML/AI in their organizations.