Now in today’s lesson, we learn Why Python is only more suitable and helpful in understanding and implementing Machine learning concepts while some other languages such as Java , Scala & R also provide libraries and frameworks, so why we should give priority to it ok.
Why we use Python for Machine Learning
As we all know that Python is general purpose programming & dynamic language as it have vast libraries that have built-in classes and method that have reduced our work so i give you example i guarantee your all doubt will be clear:
For example: As we know when we have to used mathematical formulas or using mathematical expressions and we have to do some calculations and operations on that mathematical expression. so what we do we just import math library from our modules in python so it make all easy for us like we can do integration or differentiation easily with it rather without it is very difficult for us see a line of code as example below :
import math
a= int(input("enter a number\n"))
b= math.sqrt(a)
print("square root is : ",
Output
enter a number 81 square root is : 9.0
you see we do not need to program our self to create a square-root function but instead of making function we just simple import math class that solve our problem give us built-in function of sqrt we call it through math.sqrt so similarly in learning machine learning concept there are algorithms that we need to built our models that can solve real world problems so Python is a great choice it has a huge set of libraries that give us pre-built or you can say built in modules and methods that can solve our problem easily i hope you understand purpose of using Python for Machine learning. See below some of useful libraries that we use in our next lectures here we just take overview of it okay so don’t worry about that we will learn all about libraries that we should discuss below.
Libraries of Python use in Machine Learning
As Python is a vast general-purpose language, it has a library for Machine learning purposes. There are some libraries that we use. See below the list:
- NumPy
- Pandas
- Matplotlib
- Seaborn
- SciPy
- Scikit-Learn (sklearn)
- TensorFlow
- Keras
- PyTorch
- XGBoost
- LightGBM
- OpenCV
1-Why we use NumPy for Machine Learning with Python
Travis Oliphant was the founder of NumPy, which was first released in 2006. The NumPy community maintains it means responsible for it’s maintenance. NumPy is the foundation of many other machine-learning libraries. It supports small to large, multi-dimensional arrays (2D or 3D) and matrices, along with mathematical functions to operate on these arrays(2D or 3D) . It’s essential for numerical computations and data manipulation.
2-Why we use Pandas for Machine Learning with Python
Wes McKinney was the founder of Pandas and it was first released in 2008. It is now maintained by the Pandas Development Team means they are responsible for it’s maintenance. Pandas is used for data manipulation(to evaluate it) and analysis. It provides data structures (like Data-Frame and Series) to efficiently and accurately handle and analyze tabular data, making tasks like data cleaning, transformation, and exploration much more convenient & easier.
3-Why we use Matplotlib for Machine Learning with Python
John D. Hunter was the founder of Matplotlib and it wass first released in in 2003. It is maintained by the Matplotlib Development Team, means they are responsible for it’s maintenance. Matplotlib is a graph-plotting library that allows you to create various types of visualizations, such as line plots, scatter plots, histograms, & much more. It’s used to visualize data and results in a graphical representation or form.
4-Why we use Seaborn for Machine Learning with Python
Seaborn is built on top of Matplotlib .Michael Waskom was the founder of it. It provides a higher-level interface for creating statistical graphics. It’s particularly useful for creating aesthetically pleasing and informative visualizations of data.
5-Why we use SciPy for Machine Learning with Python
The scientific Python community has developed the SciPy, and they maintains it , which means they are responsible for its maintenance. It builds on top of NumPy and provides additional functionality or, you can say, features of scientific and technical computing. SciPy is a collection of mathematical algorithms & functions built on top of NumPy. It includes modules for optimization, integration, interpolation, signal processing, and more. It’s used for scientific and technical computing tasks.
6-Why we use Scikit-Learn (sklearn) for Machine Learning with Python
Scikit-Learn is an open-source project that is developed by a community of contributors. It was started by David Cournapeau and is now maintained by a group of volunteers in a community. It is one of the most widely used machine learning libraries because, it provides a simple and efficient way to implement a broad range of machine learning rules, algorithms, including classification, regression, clustering etc. It’s also includes tools for data preprocessing, model selection, and evaluation.
7-Why we use TensorFlow for Machine Learning with Python
Google Brain team develops TensorFlow and it was first released in 2015. It is maintained by Google, which means they are responsible for its maintenance. TensorFlow is an open-source deep learning library invented by Google It’s used for building and training neural-networks and other machine learning models. TensorFlow offers flexibility and scalability for large-scale projects.
8-Why we use Keras for Machine Learning with Python
Keras is an API(Application Programming Interface) specification for building deep-learning-models and is compatible with multiple backends, including TensorFlow etc. It was developed/invented by François Chollet. It is an API(Application Programming Interface) that can run or you can say execute on top of TensorFlow (among other backends). It simplifies the process of building and training deep learning models, making it more user-friendly and accessible.
9-Why we use PyTorch for Machine Learning with Python
Facebook’s AI Research lab developed PyTorch & it was first released in 2016. It is maintained by Facebook, which means they are responsible for its maintenance. PyTorch is another deep learning framework known for its dynamic computation graph and ease of use. It’s favored and used by researchers and developers for building neural networks and conducting experiments.
10-Why we use XGBoost for Machine Learning with Python
Tianqi Chen is the man who develops XG Boost & it is widely used in machine learning competitions, researches. It is an optimized, reliable and high performance gradient boosting library mainly used for creating models that combines multiple weak models to create a robust predictive model whose accuracy is almost good. That’s why developers favoured it.
11-Why we use LightGBM for Machine Learning with Python
Microsoft developed LightGBM (Light Gradient Boosting Machine), purpose of developing to be efficient and scalable framework. It is similar to XGBoost because of it funtionality and purpose. LightGBM is a gradientboosting framework intended to be Designed to work fast and handle big amounts of data, so it’s good for big and complicated projects with lots of information. Developers love to use this in complex projects.
12-Why we use OpenCV for Machine Learning with Python
OpenCV stands for Open Source Computer Vision Library. It is an open-source computer-vision & machine learning library. It was initially developed by Intel and is now maintained by the OpenCV Development Team , which means they are responsible for its maintenance. OpenCV is a computer vision library which provides a set of tools for image and video analysis. It’s often used for tasks like object detection, image recognition, and video processing etc.
NOTE: That all above libraries discussed above it is just an overview. We will discuss each in detail in future. so please don’t worry about that. You must subscribe or register to our website it is free. When we post you will be notify or mailed by us so you will sat updated thanks.
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