Python for machine learning (ML)
Python is one of the most popular languages among data scientists and others working in the field of Machine learning. The reason behind this popularity is the availability of many python machine learning libraries. So basically a library or a framework is a readily available program that can be used to perform common coding tasks. In machine learning project these machine learning libraries are very helpful. Today we are going to see a few of them which are very extensively used in the machine learning area.
However, let us first try to understand why python is preferred for Machine Learning?
Why python is best for Machine Learning?
Over the past few years python has become the most popular programming language. It is favorite in domains like web development, web scraping, automation, scripting, etc. Along with these areas, python has become the most preferred and favorite programming language in the field of Machine Learning(ML) and Artificial Intelligence (AI) projects.
In the early days of the so-called “Machine Learning” field, people used to manually write machine learning algorithms, mathematical and statistical formulas. Everything uses to be hardcoded and the Major disadvantages of this process were that It made the process of machine learning time consuming, rigid and inefficient. Data scientists/Researchers had to write repeated lines of code along with focusing on data analysis, statistical and mathematical formulas.
The main advantage of python for data science is the extensive collection of Machine Learning libraries. These robust set of libraries helped data scientists/Researchers to dedicate more quality time on the actual machine learning task. Since most of the code writing burden is managed by the readily available machine learning libraries.
Along with machine learning libraries, Python has other advantages like easy to read syntax which made the data scientists/Researchers life easy. So these are the few reasons why python is best for machine learning (ML).
Classification of python Machine Learning Libraries
Machine learning workflow consists of different steps like data preparation, data visualization, Machine learning, Deep learning, etc. and fortunately at each of these steps, we have python machine learning libraries available to help us.
Below is the list of libraries that are classified according to their usage in a machine learning project.
- Data Preparation/Management
- Data Visualization
- Machine Learning
- SciKit Learn
- Deep Learning
- Natural Language Processing
List of top python machine learning libraries:
Out of all the above-listed libraries we have selected a few python machine learning libraries and we are going to learn more detail about each of them;
Python Pandas is a best-suited library for data Extraction, Preparation, and analysis work. We can create DataFrames using Pandas. DataFrames is data presented in a structured format (Think of data presented in a spreadsheet with rows and columns). So Pandas basically helps us to refine the data into a very nice and clear structure that gives us clear and intuitive analysis.
As we know every dataset needs to be prepared before actually being used for machine learning purposes. Only the best-prepared dataset can yield good, reliable results and Pandas is the library that helps us in data preparation. So below are the key features of Python Pandas library;
- Pandas have easy to use high-level data structures. Also, It is very easy to manipulate numerical tables and time series by using Pandas.
- It comes very handily for a task like data manipulation which involves grouping, slicing, concatenation, and filtering of data.
- It is also effectively used in data cleaning works like data filling, replacing, etc.
- Data extraction capability: Pandas can easily be used to fetch data from different sources like SQL, CSV, JSON, Excel, etc.
- Data preparation capability: Pandas has in-built features for grouping, combining and filtering of data.
- We can use Pandas in conjunction with other python libraries like Plotly to create information graphs directly from Pandas DataFrames.
- Pandas is used in applications like Google Maps and Uber.
License: It is free to use open-source library which comes under the New BSD License.
Official website: https://pandas.pydata.org/
Numpy word is an acronym of “Numerical Python“. It is a popular python library used for Multidimensional array processing and Scientific computing. Some of the key features of Python Numpy library are;
- Numpy provides the capability to work with powerful N-dimensional array objects by providing different tools.
- It has a collection of high-level mathematical functions and capability to handle different mathematical operations like Linear algebra, Fourier transform and a random number.
- It has the most efficient way of data storing as it takes very little memory to store the data when compared with Python lists.
- Other python libraries like TensorFlow, SciKit Learn, SciPy, etc. depends upon Numpy due to its built-in capabilities.
- Code from C, C++or Fortran can easily be implemented using Numpy.
License: It is free to use open-source library which comes under the BSD License.
Official website: https://www.numpy.org/
Scikit-learn (Scipy toolkit) is a python library which is used for supervised and unsupervised learning purpose. It provides various tools for data pre-processing, model fitting, model selection, and evaluation. It has a range of algorithms used for machine learning purposes like RandomForestClassifier, K-means, Support Vector machines, etc.
Key features of python Scikit-learn library are;
- It is built upon Numpy, Scipy, and Matplotlib so it has inherited all the capabilities of these three libraries.
- Scikit-learn goes well with many other python libraries such as Plotly for plotting, NumPy for array vectorization, pandas for data frames, SciPy, etc.
- Most of the code is written in python and some core code is written in Cython. It uses Numpy extensively for algebra and N-Dimensional array processing.
- It features different classification, regression and clustering algorithms including support vector machines, random forests, gradient boosting, k-means, etc.
License: It is free to use open-source library which comes under the New BSD License.
Official website: https://scikit-learn.org/
SciPy is a python machine learning library used for scientific and technical computing. It contains various modules for optimization, integration, interpolation, statistics and image processing which are common tasks in science and engineering. Some of the key features of python Scipy library are;
- Python SciPy library is built upon the NumPy array object, Matplotlib, and Pandas along with other scientific computing libraries.
- It provides many user-friendly and efficient numerical routines such as routines for numerical integration and optimization.
- SciPy is a fully-featured version of linear algebra when compared with NumPy. So most of the required data science features are available with the SciPy library.
License: It is free to use open-source library which comes under the BSD license.
Official website: scipy.org/scipylib/
Python TensorFlow is an open-source python library for numerical computation, Deep neural network research, and large scale machine learning. TensorFlow library is developed by Google’s Brain team working in the Machine Intelligence Research organization. This team was mainly focusing on machine learning and deep neural networks research. However, TensorFlow is capable of doing a wide variety of domain operations as well.
Python TensorFlow basically helps in performing tasks like Data acquisition, training model, prediction and refining future predictions. Biggest advantage of TensorFlow is “Abstraction” capability i.e You don’t have to worry about the algorithm implementation and other details since TensorFlow will take care of all those things.
Key Features of python TensorFlow library:
- TensorBoard: A web-based visualization suite helps you to perform data visualizations operations like plotting, inspecting and understanding the graphs.
- Availability of many open source trained models that we can try and tune for further use. This is known as “Transfer Learning”. Few of the APIs like object detection using which we can build an image detection application.
- SyntaxNet is a neural-network framework for analyzing and understanding the grammatical structure of sentences. It is a model trained for analyzing English, followed quickly by a collection of pre-trained models for 40 additional languages.
Theano is a Python library and a compiler that allows you to define, optimize and evaluate mathematical expressions involving multi-dimensional arrays efficiently. It is mostly used in building Deep Learning Projects. It is primarily developed by a Montreal Institute for Learning Algorithms (MILA).
For problems involving large amounts of data Python Theano attain high speeds that gives a tough competition to C implementations. It can take advantage of GPUs which makes it perform better than C on a CPU by considerable orders of magnitude under certain circumstances.
It is mainly designed to handle the types of computation required for large neural network algorithms used in Deep Learning. That is the reason it is a very popular library in the field of Deep Learning. Some of the key features of python Theano are;
- Execution (Speed Optimization): Theano can use g++ or nvcc to compile parts of your expression graph into CPU or GPU instructions, which run much faster than pure Python.
- Symbolic Differentiation: It can automatically build symbolic graphs for computing gradients.
- Stability Optimization: Python Theano can recognize numerically unstable expressions and compute them with more stable algorithms.
License: It comes under The 3-Clause BSD License.
Official website: www.deeplearning.net/software/theano/
Python Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK or Theano. Being able to go from idea to result with the least possible delay is key to doing good research. So Keras was developed with the main focus on enabling fast experimentation.
It was developed by François Chollet, a Google engineer as part of the TensorFlow core. It offers a higher-level, more intuitive set of abstractions that make it easy to develop deep learning models regardless of the computational backend used. Some of the key features of python Keras is;
- User Friendly: It offers consistent and simple APIs, It minimizes the number of user actions required for common use cases and it provides clear and actionable feedback upon user error. User-friendliness is one of the most important aspects of Keras.
- Easy extensibility: New modules are simple to add (as new classes and functions) and existing modules provide ample examples. To be able to easily create new modules allows for total expressiveness, making Keras suitable for advanced research.
- Works with Python. No need for separate models configuration files in a declarative format. Models are described in Python code, which is compact, easier to debug, and allows for ease of extensibility.
- All the above-mentioned features make Keras easy and fast prototyping.
- Supports both convolutional networks and recurrent networks, as well as combinations of the two.
- Runs seamlessly on CPU and GPU.
License: It is an open-source library that comes under the MIT license.
Official website: keras.io
PyTorch is a Python-based scientific computing package for deep learning research platform that provides maximum flexibility and speed. It is based on the Torch library and mainly used for digital image processing and natural language processing projects.
Python Pytorch library is primarily developed by Facebook’s AI research lab. Some of the key features of this python machine learning library are;
- Distribute Training: PyTorch supports distributed training which enables researchers as well as practitioners to parallelize their computations. Distributed training makes it possible to use multiple GPUs to process larger batches of input data. This, in turn, reduces the computation time.
- Easy to use API: PyTorch APIs are so simple to use as Python.
- Python support – As mentioned above, PyTorch smoothly integrates with the python data science stack. It is so similar to NumPy that you might not even notice the difference.
- Dynamic computation graphs – Instead of predefined graphs with specific functionalities, PyTorch provides a framework for us to build computational graphs as we go, and even change them during runtime. This is valuable for situations where we don’t know how much memory is going to be required for creating a neural network.
License: It is an open-source library that comes under the BSD license.
Official website: pytorch.org
The next python machine learning library is Matplotlib. It is a multi-platform data visualization library built on NumPy arrays. It basically provides an API for embedding graphs into applications using any GUI toolkit.
Python Matplotlib was originally written by John D. Hunter. The main use of python MatPlotlib is 2D plotting of data which produces publication-quality figures in a variety of hard copy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, Python, web application servers and various graphical user interface toolkits.
Matplotlib is designed to be as usable as MATLAB, with the ability to use Python and the advantage of being free and open-source. Some of the key features of Python MatPlotlib are;
- Collection of different plots i.e using MatPlotlib you can plot different types of graphs such as line, Scatter, Histogram, Power spectra, bar charts, error charts, etc. with just a few lines of code.
- For simple plotting, the
pyplotmodule provides a MATLAB-like interface, particularly when combined with IPython.
License: It is an open-source library that comes under the Matplotlib license.
Official website: matplotlib.org