{"product_id":"machine-learning-for-decision-sciences-with-case-studies-in-python-9781032193564","title":"Machine Learning for Decision Sciences with Case Studies in Python","description":"\u003cp\u003e\u003c\/p\u003e\u003cblockquote\u003e\n\u003cbr\u003ePython is a powerful programming language for machine learning, with comprehensive coverage of feature engineering, real-time case studies, and structured exercises. It covers unsupervised, supervised, and reinforcement learning algorithmic concepts. \u003c\/blockquote\u003e\u003cp\u003e\u003cstrong\u003eFormat\u003c\/strong\u003e: Hardback\u003cbr\u003e\u003cstrong\u003eLength\u003c\/strong\u003e: 454 pages\u003cbr\u003e\u003cstrong\u003ePublication date\u003c\/strong\u003e: 08 July 2022\u003cbr\u003e\u003cstrong\u003ePublisher\u003c\/strong\u003e: Taylor \u0026amp; Francis Ltd\u003cbr\u003e\u003c\/p\u003e \u003cp\u003e\u003cbr\u003ePython is a versatile programming language that has gained widespread popularity in the field of machine learning due to its simplicity, readability, and extensibility. It is widely used for a wide range of tasks, from data analysis and manipulation to building complex machine learning models.\u003cbr\u003e\u003cbr\u003eAt its core, Python is an object-oriented programming language, which means that it is built around objects rather than functions. This makes it easy to create and manipulate data structures, such as lists, dictionaries, and arrays. Additionally, Python has a rich library of built-in functions and modules that can be used for a variety of tasks, including data processing, numerical analysis, and machine learning.\u003cbr\u003e\u003cbr\u003eOne of the key features of Python that makes it popular for machine learning is its ability to handle large amounts of data efficiently. Python's data structures, such as NumPy arrays and Pandas DataFrames, are designed to handle large datasets and allow for fast and efficient computations. This makes it possible to train machine learning models on large datasets and obtain accurate predictions.\u003cbr\u003e\u003cbr\u003eAnother important feature of Python for machine learning is its ability to integrate with other programming languages and libraries. Python is compatible with a wide range of programming languages, including R, C++, and Java, which makes it easy to combine Python with existing codebases and libraries. This integration allows for the development of complex machine learning pipelines and the reuse of existing code.\u003cbr\u003e\u003cbr\u003ePython is also widely used for feature engineering, which is the process of transforming raw data into features that can be used for machine learning. This includes tasks such as data cleaning, feature extraction, and feature selection. Feature engineering is an important step in machine learning because it allows for the identification of important features that can improve the accuracy of machine learning models.\u003cbr\u003e\u003cbr\u003eIn addition to feature engineering, Python is also used for building machine learning models. There are a wide range of machine learning algorithms available in Python, including supervised learning, unsupervised learning, and reinforcement learning. Each algorithm has its own strengths and weaknesses, and Python provides a flexible platform for developing and testing different models.\u003cbr\u003e\u003cbr\u003eOne of the advantages of using Python for machine learning is its simplicity and readability. Python's syntax is relatively easy to learn, and its code is often concise and easy to understand. This makes it possible for beginners to quickly get up to speed with machine learning and start building complex models.\u003cbr\u003e\u003cbr\u003eAnother advantage of Python is its extensive community of developers and users. There is a large and active community of Python developers who are constantly working on improving the language and developing new tools and libraries for machine learning. This community provides a wealth of resources and support for developers, making it easier to collaborate and share ideas.\u003cbr\u003e\u003cbr\u003eIn conclusion, Python is a versatile programming language that has gained widespread popularity in the field of machine learning due to its simplicity, readability, and extensibility. It is widely used for a wide range of tasks, from data analysis and manipulation to building complex machine learning models. Python's ability to handle large amounts of data efficiently, integrate with other programming languages and libraries, and provide a flexible platform for developing and testing machine learning models make it an ideal choice for beginners and experienced developers alike.\u003c\/p\u003e\u003cp\u003e\u003cstrong\u003eWeight\u003c\/strong\u003e: 1040g\u003cbr\u003e\u003cstrong\u003eDimension\u003c\/strong\u003e: 254 x 178 (mm)\u003cbr\u003e\u003cstrong\u003eISBN-13\u003c\/strong\u003e: 9781032193564\u003c\/p\u003e","brand":"S. Sumathi,Surekha Paneerselvam,Suresh V. Rajappa,L. Ashok Kumar","offers":[{"title":"Hardback","offer_id":44105041772794,"sku":"9781032193564","price":180.56,"currency_code":"GBP","in_stock":true}],"thumbnail_url":"\/\/cdn.shopify.com\/s\/files\/1\/0522\/4297\/2845\/products\/1657286649154_book.jpg?v=1657604950","url":"https:\/\/shulphink.com\/products\/machine-learning-for-decision-sciences-with-case-studies-in-python-9781032193564","provider":"Shulph Ink","version":"1.0","type":"link"}