2021 NumPy, Pandas and Matplotlib A-Z™: for Machine Learning
NumPy is a leading scientific computing library in Python while Pandas is for data manipulation and analysis. Also, learn to use Matplotlib for data visualization. Whether you are trying to go into Data Science, dive into machine learning, or deep learning, NumPy and Pandas are the top Modules in Python you should understand to make the journey smooth for you. In this course, we are going to start from the basics of Python NumPy and Pandas to the advanced NumPy and Pandas. This course will give you a solid understanding of NumPy, Pandas, and their functions.
At the end of the course, you should be able to write complex arrays for real-life projects, manipulate and analyze real-world data using Pandas.
WHO IS THIS COURSE FOR?
√ This course is for you if you want to learn NumPy, Pandas, and Matplotlib for the first time or get a deeper knowledge of NumPy and Pandas to increase your productivity with deep and Machine learning.
√ This course is for you if you are coming from other programming languages and want to learn Python NumPy and Pandas fast and know it really well.
√ This course is for you if you are tired of NumPy, Pandas, and Matplotlib courses that are too brief, too simple, or too complicated.
√ This course is for you if you want to build real-world applications using NumPy or Panda and visualize them with Matplotlib.
√ This course is for you if you have to get the prerequisite knowledge to understanding Data Science and Machine Learning using NumPy and Pandas.
√ This course is for you if you want to master the in-and-out of NumPy, Pandas, and data visualization.
√ This course is for you if you want to learn NumPy and Pandas by doing exciting real-life challenges that will distinguish you from the crowd.
√ This course is for you if plan to pass an interview soon.
Who this course is for:
- All levels of students
- Just a little knowledge of Python
Last Updated 7/2021
2021 NumPy, Pandas and Matplotlib A-Z™: for Machine Learning.zip (3.0 GB) | Mirror