\u003cp\u003e\u003cb\u003eGain expert guidance on how to successfully develop machine learning models in Python and build your own unique data platforms\u003c/b\u003e\u003c/p\u003e\u003ch4\u003eKey Features\u003c/h4\u003e\u003cul\u003e\u003cli\u003eGain a full understanding of the model production and deployment process\u003c/li\u003e\u003cli\u003eBuild your first machine learning model in just five minutes and get a hands-on machine learning experience\u003c/li\u003e\u003cli\u003eUnderstand how to deal with common challenges in data science projects\u003c/li\u003e\u003c/ul\u003e\u003ch4\u003eBook Description\u003c/h4\u003e\u003cp\u003eWhere thereâs data, thereâs insight. With so much data being generated, there is immense scope to extract meaningful information thatâll boost business productivity and profitability. By learning to convert raw data into game-changing insights, youâll open new career paths and opportunities.\u003c/p\u003e\u003cp\u003eThe Data Science Workshop begins by introducing different types of projects and showing you how to incorporate machine learning algorithms in them. Youâll 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, youâll get hands-on with approaches such as grid search and random search.\u003c/p\u003e\u003cp\u003eNext, youâll 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. Youâll also understand how to use the orchestration and scheduling workflow to deploy machine learning models in batch.\u003c/p\u003e\u003cp\u003eBy the end of this book, youâll have the skills to start working on data science projects confidently. By the end of this book, youâll have the skills to start working on data science projects confidently.\u003c/p\u003e\u003ch4\u003eWhat you will learn\u003c/h4\u003e\u003cul\u003e\u003cli\u003eExplore the key differences between supervised learning and unsupervised learning\u003c/li\u003e\u003cli\u003eManipulate and analyze data using scikit-learn and pandas libraries\u003c/li\u003e\u003cli\u003eUnderstand key concepts such as regression, classification, and clustering\u003c/li\u003e\u003cli\u003eDiscover advanced techniques to improve the accuracy of your model\u003c/li\u003e\u003cli\u003eUnderstand how to speed up the process of adding new features\u003c/li\u003e\u003cli\u003eSimplify your machine learning workflow for production\u003c/li\u003e\u003c/ul\u003e\u003ch4\u003eWho this book is for\u003c/h4\u003e\u003cp\u003eThis 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.\u003c/p\u003e
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