Step By Step Tutorial
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How To Build Ai Chatbots Without Coding Step By Step Tutorial
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Author :
language : en
Publisher: StoryBuddiesPlay
Release Date : 2025-07-18
How To Build Ai Chatbots Without Coding Step By Step Tutorial written by and has been published by StoryBuddiesPlay this book supported file pdf, txt, epub, kindle and other format this book has been release on 2025-07-18 with Technology & Engineering categories.
Unlock the future of digital interaction with “How to Build AI Chatbots Without Coding: Step-by-Step Tutorial.” This comprehensive ebook takes you from the basics to advanced tips, guiding you through selecting the right tools, designing conversational flows, integrating services, automating tasks, and analyzing performance. Whether you’re a small business owner, marketer, educator, or entrepreneur, you’ll gain hands-on techniques to create, launch, and optimize powerful AI chatbots no programming experience required. no code chatbot, AI chatbot tutorial, build chatbot without coding, chatbot ebook, chatbot automation, chatbot for beginners, chatbot integration, chatbot design, chatbot business, chatbot step by step
Step By Step Tutorials On Deep Learning Using Scikit Learn Keras And Tensorflow With Python Gui
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-06-18
Step By Step Tutorials On Deep Learning Using Scikit Learn Keras And Tensorflow With Python Gui written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-18 with Computers categories.
In this book, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to implement deep learning on classifying fruits, classifying cats/dogs, detecting furnitures, and classifying fashion. In Chapter 1, you will learn to create GUI applications to display line graph using PyQt. You will also learn how to display image and its histogram. In Chapter 2, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying fruits using Fruits 360 dataset provided by Kaggle (https://www.kaggle.com/moltean/fruits/code) using Transfer Learning and CNN models. You will build a GUI application for this purpose. Here's the outline of the steps, focusing on transfer learning: 1. Dataset Preparation: Download the Fruits 360 dataset from Kaggle. Extract the dataset files and organize them into appropriate folders for training and testing. Install the necessary libraries like TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, and NumPy; Data Preprocessing: Use OpenCV to read and load the fruit images from the dataset. Resize the images to a consistent size to feed them into the neural network. Convert the images to numerical arrays using NumPy. Normalize the image pixel values to a range between 0 and 1. Split the dataset into training and testing sets using Scikit-Learn. 3. Building the Model with Transfer Learning: Import the required modules from TensorFlow and Keras. Load a pre-trained model (e.g., VGG16, ResNet50, InceptionV3) without the top (fully connected) layers. Freeze the weights of the pre-trained layers to prevent them from being updated during training. Add your own fully connected layers on top of the pre-trained layers. Compile the model by specifying the loss function, optimizer, and evaluation metrics; 4. Model Training: Use the prepared training data to train the model. Specify the number of epochs and batch size for training. Monitor the training process for accuracy and loss using callbacks; 5. Model Evaluation: Evaluate the trained model on the test dataset using Scikit-Learn. Calculate accuracy, precision, recall, and F1-score for the classification results; 6. Predictions: Load and preprocess new fruit images for prediction using the same steps as in data preprocessing. Use the trained model to predict the class labels of the new images. In Chapter 3, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying cats/dogs using dataset provided by Kaggle (https://www.kaggle.com/chetankv/dogs-cats-images) using Using CNN with Data Generator. You will build a GUI application for this purpose. The following steps are taken: Set up your development environment: Install the necessary libraries such as TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy, and any other dependencies required for the tutorial; Load and preprocess the dataset: Use libraries like OpenCV and NumPy to load and preprocess the dataset. Split the dataset into training and testing sets; Design and train the classification model: Use TensorFlow and Keras to design a convolutional neural network (CNN) model for image classification. Define the architecture of the model, compile it with an appropriate loss function and optimizer, and train it using the training dataset; Evaluate the model: Evaluate the trained model using the testing dataset. Calculate metrics such as accuracy, precision, recall, and F1 score to assess the model's performance; Make predictions: Use the trained model to make predictions on new unseen images. Preprocess the images, feed them into the model, and obtain the predicted class labels; Visualize the results: Use libraries like Matplotlib or OpenCV to visualize the results, such as displaying sample images with their predicted labels, plotting the training/validation loss and accuracy curves, and creating a confusion matrix. In Chapter 4, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform detecting furnitures using Furniture Detector dataset provided by Kaggle (https://www.kaggle.com/akkithetechie/furniture-detector) using VGG16 model. You will build a GUI application for this purpose. Here are the steps you can follow to perform furniture detection: Dataset Preparation: Extract the dataset files and organize them into appropriate directories for training and testing; Data Preprocessing: Load the dataset using Pandas to analyze and preprocess the data. Explore the dataset to understand its structure, features, and labels. Perform any necessary preprocessing steps like resizing images, normalizing pixel values, and splitting the data into training and testing sets; Feature Extraction and Representation: Use OpenCV or any image processing libraries to extract meaningful features from the images. This might include techniques like edge detection, color-based features, or texture analysis. Convert the images and extracted features into a suitable representation for machine learning models. This can be achieved using NumPy arrays or other formats compatible with the chosen libraries; Model Training: Define a deep learning model using TensorFlow and Keras for furniture detection. You can choose pre-trained models like VGG16, ResNet, or custom architectures. Compile the model with an appropriate loss function, optimizer, and evaluation metrics. Train the model on the preprocessed dataset using the training set. Adjust hyperparameters like batch size, learning rate, and number of epochs to improve performance; Model Evaluation: Evaluate the trained model using the testing set. Calculate metrics such as accuracy, precision, recall, and F1 score to assess the model's performance. Analyze the results and identify areas for improvement; Model Deployment and Inference: Once satisfied with the model's performance, save it to disk for future use. Deploy the model to make predictions on new, unseen images. Use the trained model to perform furniture detection on images by applying it to the test set or new data. In Chapter 5, you will learn how to use TensorFlow, Keras, Scikit-Learn, OpenCV, Pandas, NumPy and other libraries to perform classifying fashion using Fashion MNIST dataset provided by Kaggle (https://www.kaggle.com/zalando-research/fashionmnist/code) using CNN model. You will build a GUI application for this purpose. Here are the general steps to implement image classification using the Fashion MNIST dataset: Import the necessary libraries: Import the required libraries such as TensorFlow, Keras, NumPy, Pandas, and Matplotlib for handling the dataset, building the model, and visualizing the results; Load and preprocess the dataset: Load the Fashion MNIST dataset, which consists of images of clothing items. Split the dataset into training and testing sets. Preprocess the images by scaling the pixel values to a range of 0 to 1 and converting the labels to categorical format; Define the model architecture: Create a convolutional neural network (CNN) model using Keras. The CNN consists of convolutional layers, pooling layers, and fully connected layers. Choose the appropriate architecture based on the complexity of the dataset; Compile the model: Specify the loss function, optimizer, and evaluation metric for the model. Common choices include categorical cross-entropy for multi-class classification and Adam optimizer; Train the model: Fit the model to the training data using the fit() function. Specify the number of epochs (iterations) and batch size. Monitor the training progress by tracking the loss and accuracy; Evaluate the model: Evaluate the trained model using the test dataset. Calculate the accuracy and other performance metrics to assess the model's performance; Make predictions: Use the trained model to make predictions on new unseen images. Load the test images, preprocess them, and pass them through the model to obtain class probabilities or predictions; Visualize the results: Visualize the training progress by plotting the loss and accuracy curves. Additionally, you can visualize the predictions and compare them with the true labels to gain insights into the model's performance.
Step By Step Project Based Tutorials Data Science With Python Gui Traffic And Heart Attack Analysis And Prediction
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Author : Vivian Siahaan
language : en
Publisher: BALIGE PUBLISHING
Release Date : 2023-06-21
Step By Step Project Based Tutorials Data Science With Python Gui Traffic And Heart Attack Analysis And Prediction written by Vivian Siahaan and has been published by BALIGE PUBLISHING this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-06-21 with Computers categories.
In this book, you will implement two data science projects using Scikit-Learn, Scipy, and other libraries with Python GUI. In chapter 1, you will learn how to use Scikit-Learn, Scipy, and other libraries to perform how to predict traffic (number of vehicles) in four different junctions using Traffic Prediction Dataset (https://viviansiahaan.blogspot.com/2023/06/step-by-step-project-based-tutorials.html). This dataset contains 48.1k (48120) observations of the number of vehicles each hour in four different junctions: 1) DateTime; 2) Juction; 3) Vehicles; and 4) ID. Here's the outline of the steps involved in predicting traffic: Dataset Preparation: Extract the dataset files to a local folder. Import the necessary libraries, such as pandas and numpy. Load the dataset into a pandas DataFrame. Exploratory Data Analysis (EDA). Explore the dataset to understand its structure and characteristics. Check for missing values or anomalies in the data. Examine the distribution of the target variable (number of vehicles). Visualize the data using plots or graphs to gain insights into the patterns and trends.; Data Preprocessing: Convert the DateTime column to a datetime data type for easier manipulation. Extract additional features from the DateTime column, such as hour, day of the week, month, etc., which might be relevant for traffic prediction. Encode categorical variables, such as Junction, using one-hot encoding or label encoding. Split the dataset into training and testing sets for model evaluation.; Feature Selection/Engineering: Perform feature selection techniques, such as correlation analysis or feature importance, to identify the most relevant features for traffic prediction. Engineer new features that might capture underlying patterns or relationships in the data, such as lagged variables or rolling averages.; Model Selection and Training: Choose an appropriate machine learning model for traffic prediction, such as linear regression, decision trees, random forests, or gradient boosting. Split the data into input features (X) and target variable (y). Split the data further into training and testing sets. Fit the chosen model to the training data. Evaluate the model's performance using appropriate evaluation metrics (e.g., mean squared error, R-squared). Model Evaluation and Hyperparameter Tuning. Assess the model's performance on the testing set. Tune the hyperparameters of the chosen model to improve its performance. Use techniques like grid search or randomized search to find the optimal hyperparameters.; Model Deployment and Prediction: Once satisfied with the model's performance, retrain it on the entire dataset (including the testing set). Save the trained model for future use. Utilize the model to make predictions on new, unseen data for traffic prediction. In chapter 2, you will learn how to use Scikit-Learn, NumPy, Pandas, and other libraries to perform how to analyze and predict heart attack using Heart Attack Analysis & Prediction Dataset (https://viviansiahaan.blogspot.com/2023/06/step-by-step-project-based-tutorials.html). Following are the outline steps for analyzing and predicting heart attacks using the Heart Attack Analysis & Prediction Dataset. Introduction and Dataset Description: Provide an introduction to the topic of heart attack analysis and prediction. Briefly explain the dataset's source and its features, such as age, sex, blood pressure, cholesterol levels, etc.; Data Loading: Explain how to load the Heart Attack Analysis & Prediction Dataset into your Python environment using libraries like Pandas. You can mention that the dataset should be in a CSV format and demonstrate how to load it.; Data Exploration: Describe the importance of exploring the dataset before analysis. Show how to examine the dataset's structure, check for missing values, understand the statistical summary, and visualize the data using plots or charts.; Data Preprocessing: Explain the steps required to preprocess the dataset before feeding it into a machine learning model. This may include handling missing values, encoding categorical variables, scaling numerical features, and dealing with any other necessary data transformations.; Data Splitting: Describe how to split the preprocessed data into training and testing sets. Emphasize the importance of having separate data for training and evaluation to assess the model's performance accurately.; Model Building and Training: Explain how to choose an appropriate machine learning algorithm for heart attack prediction and how to build a model using libraries like Scikit-Learn. Outline the steps involved in training the model on the training dataset.; Model Evaluation: Describe how to evaluate the trained model's performance using appropriate evaluation metrics, such as accuracy, precision, recall, and F1 score. Demonstrate how to interpret the evaluation results and assess the model's predictive capabilities.; Predictions on New Data: Explain how to use the trained model to make predictions on new, unseen data. Demonstrate the process of feeding new data to the model and obtaining predictions for heart attack risk.
Tutorial Guide To Autocad 2025
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Author : Shawna Lockhart
language : en
Publisher: SDC Publications
Release Date :
Tutorial Guide To Autocad 2025 written by Shawna Lockhart and has been published by SDC Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on with Computers categories.
• Covers 2D drawing and 3D modeling • Uses step-by-step tutorials and written for novice users • Organization that parallels an introductory engineering course • Mechanical, electrical, civil, and architectural based end of chapter problems • Prepares you for the AutoCAD Certification Exam • This edition includes all new videos with greater coverage of AutoCAD's tools and features Tutorial Guide to AutoCAD 2025 provides a step-by-step introduction to AutoCAD with commands presented in the context of each tutorial. In fifteen clear and comprehensive chapters, author Shawna Lockhart guides you through all the important commands and techniques in AutoCAD 2025, from 2D drawing to solid modeling and finally finishing with rendering. In each lesson, the author provides step-by-step instructions with frequent illustrations showing exactly what appears on the AutoCAD screen. Later, individual steps are no longer provided, and you are asked to apply what you've learned by completing sequences on your own. A carefully developed pedagogy reinforces this cumulative-learning approach and supports you in becoming a skilled AutoCAD user. Tutorial Guide to AutoCAD 2025 begins with three Getting Started chapters that include information to get readers of all levels prepared for the tutorials. The author includes tips that offer suggestions and warnings as you progress through the tutorials. Key Terms and Key Commands are listed at the end of each chapter to recap important topics and commands learned in each tutorial. Also, a glossary of terms and Commands Summary list the key commands used in the tutorials. Each chapter concludes with end of chapter problems providing challenges to a range of abilities in mechanical, electrical, and civil engineering as well as architectural problems. AutoCAD Video Tutorials This textbook includes access to videos that are designed to help you get started using the most common tools in AutoCAD. These tutorials complement the textbook content by providing a practical, hands-on approach to understanding the basics of AutoCAD. These videos parallel the tutorials in the book and serve as an excellent starting point for learners who prefer to see the tools in action, reinforcing the written instructions and deepening your understanding of AutoCAD’s essential functionalities. Although these videos do not encompass the entire scope of the textbook, they offer a comprehensive overview of the basics, facilitating a strong foundational knowledge. In this edition, we've significantly expanded our video resources to encompass a broader range of AutoCAD's tools, features, commands, and functionalities.
Commands Guide Tutorial For Solidworks 2010
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Author : David C. Planchard
language : en
Publisher: SDC Publications
Release Date : 2010
Commands Guide Tutorial For Solidworks 2010 written by David C. Planchard and has been published by SDC Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2010 with Computers categories.
The Commands Guide Tutorial for SolidWorks 2010 is a comprehensive reference book written to assist beginner to intermediate users of SolidWorks. SolidWorks is an immense software package, and no one book can cover all topics for all users. The book provides a centralized reference location to address many of the System and Document properties, FeatureManagers, PropertyManagers, ConfigurationManagers and RenderManagers along with 2D and 3D Sketch tools, Sketch entities, 3D Feature tools, Motion Study, SustainabilityXpress, DFMXpress, SimulationXpress, Sheet Metal, PhotoView 360 and more. Chapter 1 provides a basic overview of the concepts and terminology used throughout this book using SolidWorks 2010 software. If you are completely new to SolidWorks, you should read Chapter 1 in detail and complete Lesson 1, Lesson 2 and Lesson 3 in the SolidWorks Tutorials. If you are familiar with an earlier release of SolidWorks, you still might want to skim Chapter 1 to become acquainted with some of the commands, menus and features that you have not used; or you can simply jump to any section in any chapter. Each chapter (17 total) provides detailed PropertyManager information on key topics with individual stand alone short tutorials to reinforce and demonstrate the functionality and ease of the SolidWorks tool or feature. All models for the 230 plus tutorials are located on the enclosed CD with their solution (initial and final). Learn by doing, not just by reading! Formulate the skills to create, modify and edit sketches and solid features. Learn the techniques to reuse features, parts and assemblies through symmetry, patterns, copied components, design tables, configurations and more. The book is designed to compliment the Online Tutorials and Online Help contained in SolidWorks 2010. The goal is to illustrate how multiple design situations and systematic steps combine to produce successful designs. The authors developed the tutorials by combining their own industry experience with the knowledge of engineers, department managers, vendors and manufacturers. These professionals are directly involved with SolidWorks everyday. Their responsibilities go far beyond the creation of just a 3D model.
A Beginner S Fundamentals Guide For An Easy Online Affiliate Business
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Author : Cheryl Schneider
language : en
Publisher: Lulu.com
Release Date : 2011-07-19
A Beginner S Fundamentals Guide For An Easy Online Affiliate Business written by Cheryl Schneider and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2011-07-19 with Business & Economics categories.
With minimal start-up costs and no buying inventory, an online affiliate business is one of the easiest, least risky, and most flexible ways for starting an internet-based business today. Learn the easy fundamental concepts and steps for turning an ordinary website or blog into a money-making online affiliate business. Also included, a must-have bookkeeping and tax tips guide for U.S. and foreign affiliates.
The Definitive Guide To The Net Compact Framework
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Author : Dan Fergus
language : en
Publisher: Apress
Release Date : 2008-01-01
The Definitive Guide To The Net Compact Framework written by Dan Fergus and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2008-01-01 with Computers categories.
The .NET Compact Framework brings the world of managed code and XML Web services to smart devices, and it enables the execution of secure, downloadable applications on devices such as personal digital assistants (PDAs), mobile phones, and set-top boxes. The Definitive Guide to the .NET Compact Framework targets both first-time and experienced mobile developers, and is a comprehensive guide to building mobile applications using the .NET Compact Framework (CF) and Smart Device Extensions (SDE) for Visual Studio .NET. Expert authors Larry Roof and Dan Fergus provide developers with everything they need to produce robust applications, including how to create effective user interfaces, work with device-based data sources, access enterprise data, leverage Web services, incorporate XML into applications, build help systems, and generate setup routines.
Tutorial Guide To Autocad 2015
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Author : Shawna Lockhart
language : en
Publisher: SDC Publications
Release Date : 2014-06-06
Tutorial Guide To Autocad 2015 written by Shawna Lockhart and has been published by SDC Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-06-06 with Computers categories.
Tutorial Guide to AutoCAD 2015 provides a step-by-step introduction to AutoCAD with commands presented in the context of each tutorial. In fifteen clear and comprehensive chapters, author Shawna Lockhart guides readers through all the important commands and techniques in AutoCAD 2015, from 2D drawing to solid modeling and finally finishing with rendering. In each lesson, the author provides step-by-step instructions with frequent illustrations showing exactly what appears on the AutoCAD screen. Later, individual steps are no longer provided, and readers are asked to apply what they've learned by completing sequences on their own. A carefully developed pedagogy reinforces this cumulative-learning approach and supports readers in becoming skilled AutoCAD users. Tutorial Guide to AutoCAD 2015 begins with three Getting Started chapters that include information to get readers of all levels prepared for the tutorials. The author includes tips that offer suggestions and warnings as you progress through the tutorials. Key Terms and Key Commands are listed at the end of each chapter to recap important topics and commands learned in each tutorial. Also, a glossary of terms and Commands Summary list the key commands used in the tutorials. Each chapter concludes with end of chapter problems providing challenges to a range of abilities in mechanical, electrical, and civil engineering as well as architectural problems.
Infoworld
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Author :
language : en
Publisher:
Release Date : 1986-04-21
Infoworld written by and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 1986-04-21 with categories.
InfoWorld is targeted to Senior IT professionals. Content is segmented into Channels and Topic Centers. InfoWorld also celebrates people, companies, and projects.
Bioinformatics
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Author : Shui Qing Ye
language : en
Publisher: CRC Press
Release Date : 2007-08-20
Bioinformatics written by Shui Qing Ye and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-08-20 with Computers categories.
An emerging, ever-evolving branch of science, bioinformatics has paved the way for the explosive growth in the distribution of biological information to a variety of biological databases, including the National Center for Biotechnology Information. For growth to continue in this field, biologists must obtain basic computer skills while computer spe