Blaine Bateman – författare
Visar alla böcker från författaren Blaine Bateman. Handla med fri frakt och snabb leverans.
2 produkter
2 produkter
Pandas Workshop
A comprehensive guide to using Python for data analysis with real-world case studies
Häftad, Engelska, 2022
685 kr
Skickas inom 5-8 vardagar
Learn the fundamentals of data science with Python by analyzing real datasets and solving problems using pandasKey FeaturesLearn how to apply data retrieval, transformation, visualization, and modeling techniques using pandasBecome highly efficient in unlocking deeper insights from your data, including databases, web data, and moreBuild your experience and confidence with hands-on exercises and activitiesBook DescriptionThe Pandas Workshop will teach you how to be more productive with data and generate real business insights to inform your decision-making. You will be guided through real-world data science problems and shown how to apply key techniques in the context of realistic examples and exercises. Engaging activities will then challenge you to apply your new skills in a way that prepares you for real data science projects.You’ll see how experienced data scientists tackle a wide range of problems using data analysis with pandas. Unlike other Python books, which focus on theory and spend too long on dry, technical explanations, this workshop is designed to quickly get you to write clean code and build your understanding through hands-on practice. As you work through this Python pandas book, you’ll tackle various real-world scenarios, such as using an air quality dataset to understand the pattern of nitrogen dioxide emissions in a city, as well as analyzing transportation data to improve bus transportation services.By the end of this data analytics book, you’ll have the knowledge, skills, and confidence you need to solve your own challenging data science problems with pandas.What you will learnAccess and load data from different sources using pandasWork with a range of data types and structures to understand your dataPerform data transformation to prepare it for analysisUse Matplotlib for data visualization to create a variety of plotsCreate data models to find relationships and test hypothesesManipulate time-series data to perform date-time calculationsOptimize your code to ensure more efficient business data analysisWho this book is forThis data analysis book is for anyone with prior experience working with the Python programming language who wants to learn the fundamentals of data analysis with pandas. Previous knowledge of pandas is not necessary.
Supervised Learning Workshop
Predict outcomes from data by building your own powerful predictive models with machine learning in Python
Häftad, Engelska, 2020
510 kr
Skickas inom 5-8 vardagar
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.