Working with Multidimensional Data Using NumPy
Год выпуска: 2018
Производитель: Pluralsight
Сайт производителя://app.pluralsight.com/library/courses/numpy-working-with-multidimensional-data
Автор: Janani Ravi
Продолжительность: 1h 43m
Тип раздаваемого материала: Видеоурок
Язык: Английский
Описание:
As working with huge numeric datasets becomes the norm, using the right tools and libraries to work with the data becomes very important. NumPy allows data analysts and data scientists to work with multi-dimensional data to solve these problems.
As machine learning and deep learning techniques become popular, getting the dataset into the right numeric form and engineering the right features to feed into ML models becomes critical.
In this course, Working with Multidimensional Data Using NumPy, you'll learn the simple and intuitive functions and classes that NumPy offers to work with data of high dimensionality.
First, you will get familiar with basic operations to explore multi-dimensional data, such as creating, printing, and performing basic mathematical operations with arrays. You'll study indexing and slicing of array data and iterating over lists and see how images are basically 3D arrays and how they can be manipulated with NumPy.
Next, you will move on to complex indexing functions. NumPy arrays can be indexed with conditional functions as well as arrays of indices. You'll then see how broadcasting rules work which allows NumPy to perform operations on arrays with different shapes as well as, study array operations such as np.argmax() which are very common when working with ML problems.
Finally, you'll study how NumPy integrates with other libraries in the PyData stack. You will also cover specific implementations with SciPy and with Pandas.
At the end of this course, you will be comfortable using the array manipulation techniques that NumPy has to offer to get your data in the right form for extracting insights.
Related Topics:
[Pluralsight] Operations on Arrays with NumPy
[Pluralsight] Building Image Processing Applications Using scikit-image
[Pluralsight] Building Data Visualizations Using Matplotlib
[Pluralsight] Pandas Fundamentals ► Advanced Pandas
[Pluralsight] Interpreting Data with Python | Path
[Pluralsight] Machine Learning Literacy — Practical Application | Path
[Pluralsight] Machine Learning Literacy | Path
[Pluralsight] Deep Learning Literacy — Practical Application | Path
[Pluralsight] Deep Learning Literacy | Path
[Pluralsight] Feature Engineering | Path
[Pluralsight] Data Analytics Literacy ► Data Science Literacy | Path
[Pluralsight] Python for Data Analysts | Path
[Pluralsight] Core Python | Path
Содержание
1. Course Overview
1. Course Overview
2. Exploring Multidimensional Data Using NumPy
01. Version Check
02. Module Overview
03. Prerequisites and Course Overview
04. Creating Arrays
05. Printing Arrays
06. Basic Array Operations
07. Universal Functions
08. Indexing and Slicing Arrays
09. Iterating Over Arrays
10. Reshaping Arrays
11. Splitting Arrays Horizontally and Vertically
12. Image Manipulation
13. Shallow Copies Using View
14. Deep Copies Using Copy
3. Complex Indexing Using NumPy
01. Module Overview
02. Indexing Arrays Using Arrays
03. Fancy Indexing with GDP Data
04. Indexing with Boolean Arrays
05. Arrays with Structured Data
06. Broadcasting
07. Broadcasting Scalars and Arrays
08. Automatic Reshaping
09. Stacking Arrays
10. Histograms
11. Miscellaneous Operations
4. Leveraging Other Python Libraries with NumPy
1. Module Overview
2. Working with Pandas
3. Working with SciPy
4. Vectorization
5. Summary and Further Study
Файлы примеров: присутствуют
Субтитры: присутствуют
Формат видео: MP4
Видео: H.264/AVC, 1280x720, 16:9, 30fps, 238 kb/s
Аудио: AAC, 44.1 kHz, 63.6 kb/s, 2 channels
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