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Machine Learning for Absolute Beginners - Level 2 /
Prepare your data for machine learning algorithms with the power of Python and pandas library About This Video Learn how to use Python for data science and machine learning projects Understand the interface of Jupyter lab tool Discover how to get data ready for machine learning algorithms In Detail...
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| Formato: | Electrónico Video |
| Idioma: | Inglés |
| Publicado: |
Packt Publishing,
2020.
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| Edición: | 1st edition. |
| Acceso en línea: | Texto completo (Requiere registro previo con correo institucional) |
| Sumario: | Prepare your data for machine learning algorithms with the power of Python and pandas library About This Video Learn how to use Python for data science and machine learning projects Understand the interface of Jupyter lab tool Discover how to get data ready for machine learning algorithms In Detail Machine learning is one of the most exciting fields in the hi-tech industry, gaining momentum in various applications. Companies are looking for data scientists, data engineers, and machine language (ML) experts to develop products, features, and projects that will help them unleash the power of machine learning. This course will show you how to prepare data for machine learning algorithms using Python and pandas library. The course starts by explaining the installation process of Anaconda and Jupyter. Once you are ready with the setup, you will understand Python fundamentals, such as variables, data types, conditional statements, loops, and modules. Next, you will go through the pandas library and learn how to use it for loading real-world large datasets. Towards the end, you will learn the steps and techniques to clean data and make it ready to move into machine learning algorithms. By the end of this course, you will be well-versed with Python fundamentals and pandas library and will be ready to take on data science projects. |
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| Descripción Física: | 1 online resource (1 video file, approximately 3 hr., 60 min.) |
| ISBN: | 9781801071918 1801071918 |