h2>Dating : Why python in Machine Learning?
Python is becoming more and more popular because of plenty of reasons. It is even considered that it is mandatory to master the Python syntax before doing anything interesting like data science. Though there are plenty of reasons to learn Python, one of the key reasons is that it is the best language to master if you want to analyze the data or get into the field of data analysis and data sciences. In order to start your data science journey, you will have to first learn the bare minimum syntax. After that, you would have to focus on developing structured projects. Then, you may want to start developing your very own, free-form projects.
Over 83% of the almost 24,000 data professionals used Python, use python on a daily basis.
It combines the power of general-purpose programming languages with the ease of use of domain-specific scripting languages like MATLAB or R. Python has libraries for data loading, visualization, statistics, natural language processing, image processing, and more. This vast toolbox provides data scientists with a large array of general- and special-purpose functionality. One of the main advantages of using Python is the abil‐ ity to interact directly with the code, using a terminal or other tools like the Jupyter Notebook, which we’ll look at shortly. Machine learning and data analysis are funda‐ mentally iterative processes, in which the data drives the analysis. It is essential for these processes to have tools that allow quick iteration and easy interaction. As a general-purpose programming language, Python also allows for the creation of complex graphical user interfaces (GUIs) and web services, and for integration into existing systems.
Scikit-learn provides a range of supervised and unsupervised learning algorithms via a consistent interface in Python.
It is licensed under a permissive simplified BSD license and is distributed under many Linux distributions, encouraging academic and commercial use.
The library is built upon the SciPy (Scientific Python) that must be installed before you can use scikit-learn. This stack that includes:
NumPy: Base n-dimensional array package
SciPy: Fundamental library for scientific computing
Matplotlib: Comprehensive 2D/3D plotting
IPython: Enhanced interactive console
Sympy: Symbolic mathematics
Pandas: Data structures and analysis
Extensions or modules for SciPy care conventionally named SciKits. As such, the module provides learning algorithms and is named scikit-learn.
The vision for the library is a level of robustness and support required for use in production systems. This means a deep focus on concerns such as easy of use, code quality, collaboration, documentation and performance.
Although the interface is Python, c-libraries are leverage for performance such as numpy for arrays and matrix operations, LAPACK, LibSVM and the careful use of cython.
Jupyter Notebook
The Jupyter Notebook is an interactive environment for running code in the browser.It is a great tool for exploratory data analysis and is widely used by data scientists.While the Jupyter Notebook supports many programming languages, we only need the Python support. The Jupyter Notebook makes it easy to incorporate code, text, and images, and all of this book was in fact written as a Jupyter Notebook. All of the code examples we include can be downloaded from GitHub.
NumPy
NumPy is one of the fundamental packages for scientific computing in Python. It contains functionality for multidimensional arrays, high-level mathematical functions such as linear algebra operations and the Fourier transform, and pseudorandom number generators.
Pandas
Pandas is a popular Python package for data science, and with good reason: it offers powerful, expressive and flexible data structures that make data manipulation and analysis easy, among many other things. The DataFrame is one of these structures.
If you are just about to start with your machine learning and want to learn from scratch, I will be making this series that would be 5–6 minutes long on machine learning and some side projects at the end of every chapter so stay tuned and happy learning
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