Pratik Ranjan (8 results)

Paperback. Condition: New. Discover how you can supervise machine learning algorithms in Python and personalize predictive models with the help of real-world datasetsKey FeaturesExplore the fundamentals of supervised machine learning and its applicationsLearn how to label and process data correctly using Python librariesGain a comprehensive overview of different machine learning algorithms used for building prediction modelsBook DescriptionWould you like to understand how and why machine learning techniques and data analytics are spearheading enterprises globally? From analyzing bioinformatics to predicting climate change, machine learning plays an increasingly pivotal role in our society.Although the real-world applications may seem complex, this book simplifies supervised learning for beginners with a step-by-step interactive approach. Working with real-time datasets, you'll learn how supervised learning, when used with Python, can produce efficient predictive models.Starting with the fundamentals of supervised learning, you'll quickly move to understand how to automate manual tasks and the process of assessing date using Jupyter and Python libraries like pandas. Next, you'll use data exploration and visualization techniques to develop powerful supervised learning models, before understanding how to distinguish variables and represent their relationships using scatter plots, heatmaps, and box plots. After using regression and classification models on real-time datasets to predict future outcomes, you'll grasp advanced ensemble techniques such as boosting and random forests. Finally, you'll learn the importance of model evaluation in supervised learning and study metrics to evaluate regression and classification tasks.By the end of this book, you'll have the skills you need to work on your real-life supervised learning Python projects.What you will learnImport NumPy and pandas libraries to assess the data in a Jupyter NotebookDiscover patterns within a dataset using exploratory data analysisUsing pandas to find the summary statistics of a datasetImprove the performance of a model with linear regression analysisIncrease the predictive accuracy with decision trees such as k-nearest neighbor (KNN) modelsPlot precision-recall and ROC curves to evaluate model performanceWho this book is forIf you are a beginner or a data scientist who is just getting started and looking to learn how to implement machine learning algorithms to build predicting models, then this book is for you. To expedite the learning process, a solid understanding of Python programming is recommended as you'll be editing the classes or functions instead of creating from scratch.…

Paperback. Condition: Brand New. 132 pages. 8.66x5.91x0.30 inches. In Stock.

Taschenbuch. Condition: Neu. Conducting Polymers as Functional Binders for Lithium Ion Battery | Pratik Ranjan Das | Taschenbuch | 132 S. | Englisch | 2016 | LAP LAMBERT Academic Publishing | EAN 9783330009134 | Verantwortliche Person für die EU: preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, mail[at]preigu[dot]de | Anbieter: preigu.…

Paperback. Condition: New. Discover how you can supervise machine learning algorithms in Python and personalize predictive models with the help of real-world datasetsKey FeaturesExplore the fundamentals of supervised machine learning and its applicationsLearn how to label and process data correctly using Python librariesGain a comprehensive overview of different machine learning algorithms used for building prediction modelsBook DescriptionWould you like to understand how and why machine learning techniques and data analytics are spearheading enterprises globally? From analyzing bioinformatics to predicting climate change, machine learning plays an increasingly pivotal role in our society.Although the real-world applications may seem complex, this book simplifies supervised learning for beginners with a step-by-step interactive approach. Working with real-time datasets, you'll learn how supervised learning, when used with Python, can produce efficient predictive models.Starting with the fundamentals of supervised learning, you'll quickly move to understand how to automate manual tasks and the process of assessing date using Jupyter and Python libraries like pandas. Next, you'll use data exploration and visualization techniques to develop powerful supervised learning models, before understanding how to distinguish variables and represent their relationships using scatter plots, heatmaps, and box plots. After using regression and classification models on real-time datasets to predict future outcomes, you'll grasp advanced ensemble techniques such as boosting and random forests. Finally, you'll learn the importance of model evaluation in supervised learning and study metrics to evaluate regression and classification tasks.By the end of this book, you'll have the skills you need to work on your real-life supervised learning Python projects.What you will learnImport NumPy and pandas libraries to assess the data in a Jupyter NotebookDiscover patterns within a dataset using exploratory data analysisUsing pandas to find the summary statistics of a datasetImprove the performance of a model with linear regression analysisIncrease the predictive accuracy with decision trees such as k-nearest neighbor (KNN) modelsPlot precision-recall and ROC curves to evaluate model performanceWho this book is forIf you are a beginner or a data scientist who is just getting started and looking to learn how to implement machine learning algorithms to build predicting models, then this book is for you. To expedite the learning process, a solid understanding of Python programming is recommended as you'll be editing the classes or functions instead of creating from scratch.…

Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Conducting polymers can be used as a functional binder material in lithium ion battery positive electrode. This work demonstrates the improvement in energy density and rate capability of LiFePO4 composite positive electrodes by replacing conventional binders with conducting polymers such as polyaniline (PANI), poly-o-methoxy aniline (POMA), poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS). Conducting polymers act in a dual role as binder and conducting additive in the positive composite electrode. The capacity and charging rate capability of the electrodes are improved by using PANI and POMA. However, due to their inherent lower conductivity in the emeraldine state, complete replacement of carbon additive is not possible. On the other hand in this work it is shown that PEDOT:PSS is stable and highly conducting in the working potential window of LiFePO4 electrodes (2.8-4 V). Hence composite electrodes free of any carbon additive are prepared with PEDOT:PSS and LiFePO4. 132 pp. Englisch.…

Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Das Pratik RanjanPratik Ranjan Das is a scientist and researcher in the field of lithium ion battery. He studied his bachelors in material science in India. He studied his masters in advanced materials and process engineering at univ.…

Taschenbuch. Condition: Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Conducting polymers can be used as a functional binder material in lithium ion battery positive electrode. This work demonstrates the improvement in energy density and rate capability of LiFePO4 composite positive electrodes by replacing conventional binders with conducting polymers such as polyaniline (PANI), poly-o-methoxy aniline (POMA), poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS). Conducting polymers act in a dual role as binder and conducting additive in the positive composite electrode. The capacity and charging rate capability of the electrodes are improved by using PANI and POMA. However, due to their inherent lower conductivity in the emeraldine state, complete replacement of carbon additive is not possible. On the other hand in this work it is shown that PEDOT:PSS is stable and highly conducting in the working potential window of LiFePO4 electrodes (2.8-4 V). Hence composite electrodes free of any carbon additive are prepared with PEDOT:PSS and LiFePO4.…

Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -Conducting polymers can be used as a functional binder material in lithium ion battery positive electrode. This work demonstrates the improvement in energy density and rate capability of LiFePO4 composite positive electrodes by replacing conventional binders with conducting polymers such as polyaniline (PANI), poly-o-methoxy aniline (POMA), poly(3,4-ethylenedioxythiophene):polystyrene sulfonate (PEDOT:PSS). Conducting polymers act in a dual role as binder and conducting additive in the positive composite electrode. The capacity and charging rate capability of the electrodes are improved by using PANI and POMA. However, due to their inherent lower conductivity in the emeraldine state, complete replacement of carbon additive is not possible. On the other hand in this work it is shown that PEDOT:PSS is stable and highly conducting in the working potential window of LiFePO4 electrodes (2.8-4 V). Hence composite electrodes free of any carbon additive are prepared with PEDOT:PSS and LiFePO4.VDM Verlag, Dudweiler Landstraße 99, 66123 Saarbrücken 132 pp. Englisch.…