What you would learn in NumPy for Data Science and Machine Learning in Python course?
Welcome! Here are Deep Learning, Machine Learning, and Data Science Prerequisites: The Numpy Stack in Python.
One issue or concern I receive a lot of is that people would like to master the field of deep learning and data science, so they enroll in these courses and are lost since they don't understand enough about the Numpy stack to translate these ideas into codes.
Even even if I wrote the program incomplete, you don't know Numpy; the code will be complicated to read.
This course was designed to eliminate that hurdle by showing how to accomplish tasks using Numpy. Numpy platform that is commonly required in deep learning and data science.
So what are those things?
Numpy. It is the foundation for all other things. The primary object of Numpy is called the Numpy array, upon which you can perform various operations.
It's important to note that the Numpy array isn't a regular array used in a programming language such as Java or C++. Still, it's an abstract mathematical concept like the matrix or vector.
This means that you can perform matrix and vector operations such as subtraction, addition, or multiplication.
The most significant aspect of Numpy arrays is that they're optimized to speed. We'll be doing demonstrations where I show that using the Numpy operations vectorized operations is more efficient than using an ordinary Python list.
Then, we'll examine more complex matrix operations such as products, inverses determination factors, and the ability to solve linear systems.
Content of the Course:
Basics of Lists, Arrays, and Numpy.
Accessing/changing specific elements, rows, and Columns
Initializing Different Arrays (1s, 0s, full, random, etc.)
Basic Math (arithmetic trigonometry, algebra, etc.)
Linear Algebra and Statistics
Transfer data into an existing file
Advanced Indexing as well as Boolean Masking
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