The Wizard's Workshop

The Wizard's Workshop

Author: Jennifer K. Clark

Publisher: Plain Sight Publishing

Published: 2018

Total Pages: 0

ISBN-13: 9781462121670

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An imaginative science activity book for children.


Science Workshop

Science Workshop

Author: Wendy Saul

Publisher: Heinemann Educational Books

Published: 2002

Total Pages: 0

ISBN-13: 9780325005102

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This second edition, chock-full of new information and ideas, leaves teachers even more eager to implement an inquiry-based science curriculum.


Leonardo's Science Workshop

Leonardo's Science Workshop

Author: Heidi Olinger

Publisher: Rockport Publishers

Published: 2019-01-01

Total Pages: 147

ISBN-13: 1631595245

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Leonardo’s Science Workshop leads children on an interactive adventure through key science concepts by following the multidisciplinary approach of the Renaissance period polymath Leonardo da Vinci: experimenting, creating projects, and exploring how art intersects with science and nature. Photos of Leonardo’s own notebooks, paintings, and drawings provide visual inspiration. More than 500 years ago, Leonardo knew that the fields of science, technology, engineering, art, and mathematics (STEAM) are all connected. The insatiably curious Leonardo examined not just the outer appearance of his art subjects, but the science that explained them. He began his studies as a painter, but his curiosity, diligence, and genius made him also a master sculptor, architect, designer, scientist, engineer, and inventor. The Leonardo’s Workshop series shares this spirit of multidisciplinary inquiry with children through accessible, engaging explanations and hands-on learning. This fascinating book harnesses children’s innate curiosity to explore some of Leonardo’s favorite subjects, including flight, motion, technology design, perspective, and astronomy. After each topic is explained with concepts from physics, chemistry, math, and engineering, kids can experience the principles first-hand with step-by-step STEAM projects. They will explore: The physics of flight by observing birds and experimenting with paper airplane designs The science of motion by building a windup dragonfly Gravitational acceleration with water balloons The movement of electrons by making cereal “dance” Technology design by making paper and fabric using recycled material Scientific perspective by drawing a 3D illusion Insight from other great thinkers—such as Galileo Galilei, James Clerk Maxwell, and Sir Isaac Newton—are woven into the lessons throughout. Introduce vital STEAM skills through visually rich, hands-on learning with Leonardo’s Science Workshop.


The Data Science Workshop

The Data Science Workshop

Author: Anthony So

Publisher: Packt Publishing Ltd

Published: 2020-01-29

Total Pages: 817

ISBN-13: 1838983082

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Cut through the noise and get real results with a step-by-step approach to data science Key Features Ideal for the data science beginner who is getting started for the first time A data science tutorial with step-by-step exercises and activities that help build key skills Structured to let you progress at your own pace, on your own terms Use your physical print copy to redeem free access to the online interactive edition Book DescriptionYou already know you want to learn data science, and a smarter way to learn data science is to learn by doing. The Data Science Workshop focuses on building up your practical skills so that you can understand how to develop simple machine learning models in Python or even build an advanced model for detecting potential bank frauds with effective modern data science. You'll learn from real examples that lead to real results. Throughout The Data Science Workshop, you'll take an engaging step-by-step approach to understanding data science. You won't have to sit through any unnecessary theory. If you're short on time you can jump into a single exercise each day or spend an entire weekend training a model using sci-kit learn. It's your choice. Learning on your terms, you'll build up and reinforce key skills in a way that feels rewarding. Every physical print copy of The Data Science Workshop unlocks access to the interactive edition. With videos detailing all exercises and activities, you'll always have a guided solution. You can also benchmark yourself against assessments, track progress, and receive content updates. You'll even earn a secure credential that you can share and verify online upon completion. It's a premium learning experience that's included with your printed copy. To redeem, follow the instructions located at the start of your data science book. Fast-paced and direct, The Data Science Workshop is the ideal companion for data science beginners. You'll learn about machine learning algorithms like a data scientist, learning along the way. This process means that you'll find that your new skills stick, embedded as best practice. A solid foundation for the years ahead.What you will learn Find out the key differences between supervised and unsupervised learning Manipulate and analyze data using scikit-learn and pandas libraries Learn about different algorithms such as regression, classification, and clustering Discover advanced techniques to improve model ensembling and accuracy Speed up the process of creating new features with automated feature tool Simplify machine learning using open source Python packages Who this book is forOur goal at Packt is to help you be successful, in whatever it is you choose to do. The Data Science Workshop is an ideal data science tutorial for the data science beginner who is just getting started. Pick up a Workshop today and let Packt help you develop skills that stick with you for life.


DATA SCIENCE WORKSHOP: Parkinson Classification and Prediction Using Machine Learning and Deep Learning with Python GUI

DATA SCIENCE WORKSHOP: Parkinson Classification and Prediction Using Machine Learning and Deep Learning with Python GUI

Author: Vivian Siahaan

Publisher: BALIGE PUBLISHING

Published: 2023-07-26

Total Pages: 373

ISBN-13:

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In this data science workshop focused on Parkinson's disease classification and prediction, we begin by exploring the dataset containing features relevant to the disease. We perform data exploration to understand the structure of the dataset, check for missing values, and gain insights into the distribution of features. Visualizations are used to analyze the distribution of features and their relationship with the target variable, which is whether an individual has Parkinson's disease or not. After data exploration, we preprocess the dataset to prepare it for machine learning models. This involves handling missing values, scaling numerical features, and encoding categorical variables if necessary. We ensure that the dataset is split into training and testing sets to evaluate model performance effectively. With the preprocessed dataset, we move on to the classification task. Using various machine learning algorithms such as Logistic Regression, K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Naive Bayes, Adaboost, Extreme Gradient Boosting, Light Gradient Boosting, and Multi-Layer Perceptron (MLP), we train multiple models on the training data. To optimize the hyperparameters of these models, we utilize Grid Search, a technique to exhaustively search for the best combination of hyperparameters. For each machine learning model, we evaluate their performance on the test set using various metrics such as accuracy, precision, recall, and F1-score. These metrics help us understand the model's ability to correctly classify individuals with and without Parkinson's disease. Next, we delve into building an Artificial Neural Network (ANN) for Parkinson's disease prediction. The ANN architecture is designed with input, hidden, and output layers. We utilize the TensorFlow library to construct the neural network with appropriate activation functions, dropout layers, and optimizers. The ANN is trained on the preprocessed data for a fixed number of epochs, and we monitor its training and validation loss and accuracy to ensure proper training. After training the ANN, we evaluate its performance using the same metrics as the machine learning models, comparing its accuracy, precision, recall, and F1-score against the previous models. This comparison helps us understand the benefits and limitations of using deep learning for Parkinson's disease prediction. To provide a user-friendly interface for the classification and prediction process, we design a Python GUI using PyQt. The GUI allows users to load their own dataset, choose data preprocessing options, select machine learning classifiers, train models, and predict using the ANN. The GUI provides visualizations of the data distribution, model performance, and prediction results for better understanding and decision-making. In the GUI, users have the option to choose different data preprocessing techniques, such as raw data, normalization, and standardization, to observe how these techniques impact model performance. The choice of classifiers is also available, allowing users to compare different models and select the one that suits their needs best. Throughout the workshop, we emphasize the importance of proper evaluation metrics and the significance of choosing the right model for Parkinson's disease classification and prediction. We highlight the strengths and weaknesses of each model, enabling users to make informed decisions based on their specific requirements and data characteristics. Overall, this data science workshop provides participants with a comprehensive understanding of Parkinson's disease classification and prediction using machine learning and deep learning techniques. Participants gain hands-on experience in data preprocessing, model training, hyperparameter tuning, and designing a user-friendly GUI for efficient and effective data analysis and prediction.


DATA SCIENCE WORKSHOP: Chronic Kidney Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI

DATA SCIENCE WORKSHOP: Chronic Kidney Disease Classification and Prediction Using Machine Learning and Deep Learning with Python GUI

Author: Vivian Siahaan

Publisher: BALIGE PUBLISHING

Published: 2023-08-15

Total Pages: 361

ISBN-13:

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In the captivating journey of our data science workshop, we embarked on the exploration of Chronic Kidney Disease classification and prediction. Our quest began with a thorough dive into data exploration, where we meticulously delved into the dataset's intricacies to unearth hidden patterns and insights. We analyzed the distribution of categorized features, unraveling the nuances that underlie chronic kidney disease. Guided by the principles of machine learning, we embarked on the quest to build predictive models. With the aid of grid search, we fine-tuned our machine learning algorithms, optimizing their hyperparameters for peak performance. Each model, whether K-Nearest Neighbors, Decision Trees, Random Forests, Gradient Boosting, Naive Bayes, Extreme Gradient Boosting, Light Gradient Boosting, or Multi-Layer Perceptron, was meticulously trained and tested, paving the way for robust predictions. The voyage into the realm of deep learning took us further, as we harnessed the power of Artificial Neural Networks (ANNs). By constructing intricate architectures, we designed ANNs to discern intricate patterns from the data. Leveraging the prowess of TensorFlow, we artfully crafted layers, each contributing to the ANN's comprehension of the underlying dynamics. This marked our initial foray into the world of deep learning. Our expedition, however, did not conclude with ANNs. We ventured deeper into the abyss of deep learning, uncovering the potential of Long Short-Term Memory (LSTM) networks. These networks, attuned to sequential data, unraveled temporal dependencies within the dataset, fortifying our predictive capabilities. Diving even further, we encountered Self-Organizing Maps (SOMs) and Restricted Boltzmann Machines (RBMs). These innovative models, rooted in unsupervised learning, unmasked underlying structures in the dataset. As our understanding of the data deepened, so did our repertoire of tools for prediction. Autoencoders, our final frontier in deep learning, emerged as our champions in dimensionality reduction and feature learning. These unsupervised neural networks transformed complex data into compact, meaningful representations, guiding our predictive models with newfound efficiency. To furnish a granular understanding of model behavior, we employed the classification report, which delineated precision, recall, and F1-Score for each class, providing a comprehensive snapshot of the model's predictive capacity across diverse categories. The confusion matrix emerged as a tangible visualization, detailing the interplay between true positives, true negatives, false positives, and false negatives. We also harnessed ROC and precision-recall curves to illuminate the dynamic interplay between true positive rate and false positive rate, vital when tackling imbalanced datasets. For regression tasks, MSE and its counterpart RMSE quantified the average squared differences between predictions and actual values, facilitating an insightful assessment of model fit. Further enhancing our toolkit, the R-squared (R2) score unveiled the extent to which the model explained variance in the dependent variable, offering a valuable gauge of overall performance. Collectively, this ensemble of metrics enabled us to make astute model decisions, optimize hyperparameters, and gauge the models' fitness for accurate disease prognosis in a clinical context. Amidst this whirlwind of data exploration and model construction, our GUI using PyQt emerged as a beacon of user-friendly interaction. Through its intuitive interface, users navigated seamlessly between model selection, training, and prediction. Our GUI encapsulated the intricacies of our journey, bridging the gap between data science and user experience. In the end, our odyssey illuminated the intricate landscape of Chronic Kidney Disease classification and prediction. We harnessed the power of both machine learning and deep learning, uncovering hidden insights and propelling our predictive capabilities to new heights. Our journey transcended the realms of data, algorithms, and interfaces, leaving an indelible mark on the crossroads of science and innovation.


The The Data Science Workshop

The The Data Science Workshop

Author: Anthony So

Publisher: Packt Publishing Ltd

Published: 2020-08-28

Total Pages: 823

ISBN-13: 1800569408

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Gain expert guidance on how to successfully develop machine learning models in Python and build your own unique data platforms Key FeaturesGain a full understanding of the model production and deployment processBuild your first machine learning model in just five minutes and get a hands-on machine learning experienceUnderstand how to deal with common challenges in data science projectsBook Description Where there’s data, there’s insight. With so much data being generated, there is immense scope to extract meaningful information that’ll boost business productivity and profitability. By learning to convert raw data into game-changing insights, you’ll open new career paths and opportunities. The Data Science Workshop begins by introducing different types of projects and showing you how to incorporate machine learning algorithms in them. You’ll learn to select a relevant metric and even assess the performance of your model. To tune the hyperparameters of an algorithm and improve its accuracy, you’ll get hands-on with approaches such as grid search and random search. Next, you’ll learn dimensionality reduction techniques to easily handle many variables at once, before exploring how to use model ensembling techniques and create new features to enhance model performance. In a bid to help you automatically create new features that improve your model, the book demonstrates how to use the automated feature engineering tool. You’ll also understand how to use the orchestration and scheduling workflow to deploy machine learning models in batch. By the end of this book, you’ll have the skills to start working on data science projects confidently. By the end of this book, you’ll have the skills to start working on data science projects confidently. What you will learnExplore the key differences between supervised learning and unsupervised learningManipulate and analyze data using scikit-learn and pandas librariesUnderstand key concepts such as regression, classification, and clusteringDiscover advanced techniques to improve the accuracy of your modelUnderstand how to speed up the process of adding new featuresSimplify your machine learning workflow for productionWho this book is for This is one of the most useful data science books for aspiring data analysts, data scientists, database engineers, and business analysts. It is aimed at those who want to kick-start their careers in data science by quickly learning data science techniques without going through all the mathematics behind machine learning algorithms. Basic knowledge of the Python programming language will help you easily grasp the concepts explained in this book.


Proceedings of the Third Workshop on Science with the New Generation of High Energy Gamma-ray Experiments

Proceedings of the Third Workshop on Science with the New Generation of High Energy Gamma-ray Experiments

Author: Alessandro De Angelis

Publisher: World Scientific

Published: 2006

Total Pages: 230

ISBN-13: 9812773541

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Introduction -- I. Detectors for high-energy gamma-rays. First results from the MAGIC experiment / D. Bastieri for the MAGIC collaboration. H.E.S.S. / P. Vincent for the H.E.S.S. collaboration. CANGAROO / M. Mori for the CANGAROO-II, III Team. The status of VERITAS / M.K. Daniel on behalf of the VERITAS collaboration. Gamma ray bursts: recent results obtained by the SWIFT mission / G. Chinearini on behalf of the SWIFT team. Functional tests and performance characterization during the assembly phase of the modules of the AGILE silicon tracker / M. Basset [und weitere]. Status of GLAST, the gamma-ray large-area space telescope / L. Rochester on behalf of the GLAST team. Status of the ARGO-YBJ experiment / P. Camarri for the ARGO-YBJ collaboration. Gamma Air Watch (GAW) - an imaging atmospheric Cherenkov telescope large with large field of view / T. Mineo [und weitere] -- II. Topics in fundamental physics. Frontiers of high energy cosmic rays / M. Pimenta. Measurement of cosmological parameters / A. Balbi. The present and the future of cosmology with gamma ray bursts / G. Ghirlanda, G. Ghisellini. Supersymmetry breaking, extra dimensions and neutralino dark matter / A.M. Lionetto. Dark matter at [symbol]-rays / L. Pieri. Populations of subhalos in cold dark matter halos / E. Bisesi -- III. Multiwavelength observations. WEBT multifrequency support to space observations / C.M. Raiteri and M. Villata for the WEBT collaboration. REM - The Remote Observatory for GRB et al. / E. Molinari on behalf of the REM/ROSS team. Planck-LFI: operation of the scientific ground segment / F. Pasian [und weitere]. INTEGRAL three years later / L. Foschini, G. Di Cocco, G. Malaguti. XMM observations of Geminga, PSR B1055-52 and PSR B0656+14: phase resolved spectroscopy as a tool to investigate the X-[symbol] connection / P.A. Caraveo [und weitere] -- IV. Poster session. Software time-calibration of the ARGO-YBJ detector / A.K. Calabrese Melcarne for the ARGO-YBJ collaboration. Gamma-ray burst physics with GLAST / N. Omodei. Observations of blazars and EGRET sources with INTEGRAL / V. Vitale [und weitere]. A third level trigger programmable on FPGA for the gamma/hadron separation in a Cherenkov telescope using Pseudo-Zernike moments and the SVM classifier / M. Frailis [und weitere]. PulsarSpectrum: simulating gamma-ray pulsars for the GLAST mission / M. Razzano [und weitere]