The Python ecosystem is growing and may become the dominant platform for machine learning.
The primary rationale for adopting Python for machine learning is because it is a general
purpose programming language that you can use both for R&D and in production. In this
chapter you will discover the Python ecosystem for machine learning. After completing this
lesson you will know:
👉 SciPy and the functionality it provides with NumPy, Matplotlib and Pandas.
👉 scikit-learn that provides all of the machine learning algorithms.
👉 How to setup your Python ecosystem for machine learning and what versions to use
Let’s get started.
Python
Python is a general purpose interpreted programming language. It is easy to learn and use
primarily because the language focuses on readability.
It is a popular language in general, consistently appearing in the top 10 programming
languages in surveys on StackOverflow1. It’s a dynamic language and very suited to interactive
development and quick prototyping with the power to support the development of large applications.
It is also widely used for machine learning and data science because of the excellent library
support and because it is a general purpose programming language (unlike R or Matlab). For
example, see the results of the Kaggle platform survey results in 20112 and the KDD Nuggets
2015 tool survey results3.
This is a simple and very important consideration. It means that you can perform your
research and development (figuring out what models to use) in the same programming language
that you use for your production systems. Greatly simplifying the transition from development
to production.
SciPy
SciPy is an ecosystem of Python libraries for mathematics, science and engineering. It is anadd-on to Python that you will need for machine learning. The SciPy ecosystem is comprised of
the following core modules relevant to machine learning:
☝NumPy: A foundation for SciPy that allows you to efficiently work with data in arrays.
☝Matplotlib: Allows you to create 2D charts and plots from data.
☝ Pandas: Tools and data structures to organize and analyze your data.
To be effective at machine learning in Python you must install and become familiar with
SciPy. Specifically:
☝ You will prepare your data as NumPy arrays for modeling in machine learning algorithms.
☝ You will use Matplotlib (and wrappers of Matplotlib in other frameworks) to create plots
and charts of your data.
☝You will use Pandas to load explore and better understand your data.
scikit-learn
The scikit-learn library is how you can develop and practice machine learning in Python. It isbuilt upon and requires the SciPy ecosystem. The name scikit suggests that it is a SciPy plug-in
or toolkit. The focus of the library is machine learning algorithms for classification, regression,
clustering and more. It also provides tools for related tasks such as evaluating models, tuning
parameters and pre-processing data.
Like Python and SciPy, scikit-learn is open source and is usable commercially under the BSD
license. This means that you can learn about machine learning, develop models and put them
into operations all with the same ecosystem and code. A powerful reason to use scikit-learn.
👉No need to install one by one. Just install Anaconda using below link.
👍After that we can use spyder IDE for coding python regarding machine learning
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