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Data Science for Beginners: Unlocking the Power of Data with Easy-to-Understand Concepts and Techniques. Part 3
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- FormatePub
- ISBN8224513871
- EAN9798224513871
- Date de parution20/04/2024
- Protection num.pas de protection
- Infos supplémentairesepub
- ÉditeurVirtued Press
Résumé
"Data Science for Beginners: Unlocking the Power of Data with Easy-to-Understand Concepts and Techniques" is a comprehensive guide for those who are new to the field of data science and machine learning. The book provides an overview of the exciting and rapidly-growing field of data science and the role that machine learning plays within it. The book starts with a clear and concise definition of machine learning, followed by an exploration of its different types and basic concepts.
The reader is then introduced to real-world applications of machine learning and the importance of this technology in today's world. The book then covers the basics of setting up a data science development environment and provides an overview of popular programming languages and tools used in data science. The reader will learn how to access and process data using SQL, Excel, and other data analysis tools, as well as techniques for data visualization.
Data preparation and preprocessing are essential components of the data science process, and the book provides a thorough explanation of these techniques, including data cleaning, transformation, and feature selection. The reader will also learn about data normalization, scaling, and dealing with missing and noisy data. The book concludes with an overview of popular machine learning algorithms, including linear regression, logistic regression, decision trees, random forest, support vector machines, naive bayes, and neural networks.
The reader will learn how to evaluate the performance of these algorithms and how to choose the best algorithm for a given problem through model selection and tuning. "Data Science for Beginners" is a comprehensive and accessible guide for anyone looking to start their journey in the exciting and rapidly-growing field of data science and machine learning.
The reader is then introduced to real-world applications of machine learning and the importance of this technology in today's world. The book then covers the basics of setting up a data science development environment and provides an overview of popular programming languages and tools used in data science. The reader will learn how to access and process data using SQL, Excel, and other data analysis tools, as well as techniques for data visualization.
Data preparation and preprocessing are essential components of the data science process, and the book provides a thorough explanation of these techniques, including data cleaning, transformation, and feature selection. The reader will also learn about data normalization, scaling, and dealing with missing and noisy data. The book concludes with an overview of popular machine learning algorithms, including linear regression, logistic regression, decision trees, random forest, support vector machines, naive bayes, and neural networks.
The reader will learn how to evaluate the performance of these algorithms and how to choose the best algorithm for a given problem through model selection and tuning. "Data Science for Beginners" is a comprehensive and accessible guide for anyone looking to start their journey in the exciting and rapidly-growing field of data science and machine learning.
"Data Science for Beginners: Unlocking the Power of Data with Easy-to-Understand Concepts and Techniques" is a comprehensive guide for those who are new to the field of data science and machine learning. The book provides an overview of the exciting and rapidly-growing field of data science and the role that machine learning plays within it. The book starts with a clear and concise definition of machine learning, followed by an exploration of its different types and basic concepts.
The reader is then introduced to real-world applications of machine learning and the importance of this technology in today's world. The book then covers the basics of setting up a data science development environment and provides an overview of popular programming languages and tools used in data science. The reader will learn how to access and process data using SQL, Excel, and other data analysis tools, as well as techniques for data visualization.
Data preparation and preprocessing are essential components of the data science process, and the book provides a thorough explanation of these techniques, including data cleaning, transformation, and feature selection. The reader will also learn about data normalization, scaling, and dealing with missing and noisy data. The book concludes with an overview of popular machine learning algorithms, including linear regression, logistic regression, decision trees, random forest, support vector machines, naive bayes, and neural networks.
The reader will learn how to evaluate the performance of these algorithms and how to choose the best algorithm for a given problem through model selection and tuning. "Data Science for Beginners" is a comprehensive and accessible guide for anyone looking to start their journey in the exciting and rapidly-growing field of data science and machine learning.
The reader is then introduced to real-world applications of machine learning and the importance of this technology in today's world. The book then covers the basics of setting up a data science development environment and provides an overview of popular programming languages and tools used in data science. The reader will learn how to access and process data using SQL, Excel, and other data analysis tools, as well as techniques for data visualization.
Data preparation and preprocessing are essential components of the data science process, and the book provides a thorough explanation of these techniques, including data cleaning, transformation, and feature selection. The reader will also learn about data normalization, scaling, and dealing with missing and noisy data. The book concludes with an overview of popular machine learning algorithms, including linear regression, logistic regression, decision trees, random forest, support vector machines, naive bayes, and neural networks.
The reader will learn how to evaluate the performance of these algorithms and how to choose the best algorithm for a given problem through model selection and tuning. "Data Science for Beginners" is a comprehensive and accessible guide for anyone looking to start their journey in the exciting and rapidly-growing field of data science and machine learning.
A propos de Tom Lesley
