Months ago, I wrote about my first blog post about Python (https://paparadit.blogspot.com/2020/07/python-for-dummies.html) - as technically I curious about how it works as a machine learning tools. On that link, you can learn (a very) basic Python codes including: (1) How to print out on screen? (2) Learn about variable assignment and data type, (3) Sequences operation, (4) Math operation, (5) Ask for input, (6) Branches, (7) Looping, (8) Array, (9) Quit from looping Function and that's all is a good start for you to learn Python syntax structures. I wrapped it all on 2 video series below.
On current post, I'm going to explain you about Python common libraries I used based on my experiment to create proof-of-concept of several machine learning algorithms. I'll try to update this post as soon as I found new library algorithm used on my code.
A main Python library purposed for graph visualizing.
import matplotlib.pyplot as plt
A famous Python library for scientific computing.
import numpy as np
I bundled about how to using those Matplotlib and Numpy libraries in a single video below:
Stand for Python for Data Analysis or some said as PANel DAtaS. It mainly used for data analytical.
import pandas as pd
Commonly used for Python statistical graph and strongly integrated with Pandas data structure.
import seaborn as sns
I packed about Pandas and Seaborn example in a video below:
Mostly contains core statistical algorithms and powerful package to do high-performance linear algebra and array operation with Numpy.
from sklearn.<package> [import <sub_package>]
So, I hope you exactly know what you have to import before playing around with Python. And, remember one thing, "Don't code what you can't debug" :)
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