Cara menggunakan python intermediate course free

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Python is a high-level programming language that is relatively easier to learn. Python is free and open source. Python has a large open source community with lots of free educational resources to help beginners to master python programming. 

Python has several specialized libraries that could be used for various applications such as: 

NumPy

NumPy is the fundamental package for scientific computing in Python. It is a Python library that provides a multidimensional array object, various derived objects (such as masked arrays and matrices), and an assortment of routines for fast operations on arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms basic linear algebra, basic statistical operations, random simulation and much more.

Matplotlib

Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.

Seaborn

Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

Pandas

Pandas is a fast, powerful, flexible, and easy to use open source data analysis and manipulation tool, built on top of the Python programming language.

Scikit-learn

Scikit-learn provides simple and efficient tools for predictive data analysis.

PyTorch

PyTorch is a machine learning framework used for applications such as deep learning, computer vision, and natural language processing.

TensorFlow

TensorFlow is a free and open-source software library for machine learning and artificial intelligence.

For a majority of data science and machine learning positions out there, knowledge in Python Programming is extremely important. That's why you should learn Python now.

Intermediate Python Programming Course from freeCodeCamp.com will help you take your Python skills to the next level. First, you will get a review of basic concepts such as lists, strings, and dictionaries, but with an emphasis on some lesser-known capabilities. Then, you will learn more advanced topics such as threading, multiprocessing, context managers, generators, and more.

Directly from its website, here is what the course promises to cover:

• Lists
• Tuples 
• Dictionaries
• Sets 
• Strings 
• Collections
• Itertools
• Lambda Functions
• Exceptions and Errors
• Logging 
• JSON 
• Random Numbers
• Decorators
• Generators 
• Threading vs Multiprocessing
• Multithreading
• Multiprocessing
• Function Arguments 
• The Asterisk (*) Operator
• Shallow vs Deep Copying
• Context Managers

As you can see, the course goes from basic to advanced concepts. You can find the ~6-hour crash course below, or on YouTube directly.

If you are an aspiring data scientist, I genuinely encourage you to invest some of your time and energy to learn the fundamentals of Python. This free offering from freeCodeCamp will help you gain hands-on practical skills in python programming.