Python Basics for Math and Data Science 1.0: Numpy and Sympy
- Yes, A basic knowledge in python is preferred
Hey there! I welcome you all to my course - Python Basics for Mathematics and Data Science 1.0 : Numpy and Sympy . This course mainly focuses on two important libraries in python called as Numpy and Sumpy. If you're someone who know the basics of Python and looking forward to develop a project or kickstart your career in Data Science and Machine Learning, this course will highly motivate you to learn further.
After completing this course, you'll be able to
1. Create 2D Matrices (numpy arrays) in Python
2. Access the elements, rows and columns of a numpy array
3. Do matrix addition, multiplication, transpose operations in Python in a single line code
4. Inbuilt functions for statistical operations
5. Solve linear equation with one unknown in python
6. Solve linear equations with two unknowns in python
7. Solve Quadratic and cubic equations in python
8. Differential Calculus in Python
9. Integral Calculus in Python - Definite and Indefinite Integrals
and a lot more stuff.
NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. The ancestor of NumPy, Numeric, was originally created by Jim Hugunin with contributions from several other developers. In 2005, Travis Oliphant created NumPy by incorporating features of the competing Numarray into Numeric, with extensive modifications. NumPy is open-source software and has many contributors.
Nothing more to write here! I'll see you there in my lectures!
Who this course is for:
- Beginner Python developers
- 07:24A run through the course
- 06:33Installing the Libraries and Setting our environment
- 12:16Numpy Matrices
- 11:33Indexing and Slicing the Numpy Arrays
- 03:26Accessing a column of a matrix
- 11:02Useful functions and methods in numpy
- 10:32Matrix Transformations and Operations using numpy
- 04:48nditer in numpy
- 07:16Concatenate and arange in numpy
- 01:33Summary - Numpy