{"product_id":"9781838552169","title":"Data Science with Python: Combine Python with machine learning principles to discover hidden patterns in raw data","description":"\u003cb\u003eLeverage the power of the Python data science libraries and advanced machine learning techniques to analyse large unstructured datasets and predict the occurrence of a particular future event.\u003c\/b\u003e\u003cp\u003e\u003cstrong\u003eKey Features\u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003eGain useful insights into data science, from data collection through to visualization\u003c\/li\u003e\n\u003cli\u003eGet up to speed with pandas, scikit-learn, and Matplotlib\u003c\/li\u003e\n\u003cli\u003eStudy a variety of data science algorithms using real-world datasets\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cstrong\u003eBook Description\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eData Science with Python begins by introducing you to data science and teaches you to install the packages you need to create a data science coding environment. You will learn three major techniques in machine learning: unsupervised learning, supervised learning, and reinforcement learning. You will also explore basic classification and regression techniques, such as support vector machines, decision trees, and logistic regression.\u003c\/p\u003e\u003cp\u003eAs you make your way through the book, you will understand the basic functions, data structures, and syntax of the Python language that are used to handle large datasets with ease. You will learn about NumPy and pandas libraries for matrix calculations and data manipulation, discover how to use Matplotlib to create highly customizable visualizations, and apply the boosting algorithm XGBoost to make predictions. In the concluding chapters, you will explore convolutional neural networks (CNNs), deep learning algorithms used to predict what is in an image. You will also understand how to feed human sentences to a neural network, make the model process contextual information, and create human language processing systems to predict the outcome.\u003c\/p\u003e\u003cp\u003eBy the end of this book, you will be able to understand and implement any new data science algorithm and have the confidence to experiment with tools or libraries other than those covered in the book.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWhat you will learn\u003c\/strong\u003e\u003c\/p\u003e\u003cul\u003e\n\u003cli\u003ePre-process data to make it ready to use for machine learning\u003c\/li\u003e\n\u003cli\u003eCreate data visualizations with Matplotlib\u003c\/li\u003e\n\u003cli\u003eUse scikit-learn to perform dimension reduction using principal component analysis (PCA)\u003c\/li\u003e\n\u003cli\u003eSolve classification and regression problems\u003c\/li\u003e\n\u003cli\u003eGet predictions using the XGBoost library\u003c\/li\u003e\n\u003cli\u003eProcess images and create machine learning models to decode them\u003c\/li\u003e\n\u003cli\u003eProcess human language for prediction and classification\u003c\/li\u003e\n\u003cli\u003eUse TensorBoard to monitor training metrics in real time\u003c\/li\u003e\n\u003cli\u003eFind the best hyperparameters for your model with AutoML\u003c\/li\u003e\n\u003c\/ul\u003e\u003cp\u003e\u003cstrong\u003eWho this book is for\u003c\/strong\u003e\u003c\/p\u003e\u003cp\u003eData Science with Python is designed for data analysts, data scientists, database engineers, and business analysts who want to move towards using Python and machine learning techniques to analyze data and predict outcomes. Basic knowledge of Python and data analytics will prove beneficial to understand the various concepts explained through this book.\u003c\/p\u003e","brand":"Packt Publishing","offers":[{"title":"Default Title","offer_id":46547635339505,"sku":"9781838552169","price":27.99,"currency_code":"USD","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0674\/5433\/7265\/files\/9781838552169_p0.jpg?v=1765589191","url":"https:\/\/shop.barnesandnoble.com\/products\/9781838552169","provider":"Barnes \u0026 Noble","version":"1.0","type":"link"}