What is Machine Learning?
Machine learning allows a machine to automatically learn from data, enhance performance from experience, and predict things without being explicitly programmed. It is the branch of Artificial Intelligence, which allows a machine to learn from past data without programming explicitly automatically. Machine learning is a data-driven technology, and it is much similar to data mining as it also deals with the massive amount of data.
What is the Need for Machine Learning?
As a human, we have some weaknesses because we can’t manually access a large amount of data, and for that, we need some computer systems, and here comes machine learning to make it simpler for us.
Following are some key points which show the importance of Machine Learning:
- It is used for solving complex problems, which are difficult for a human
- It is used for decision making in various sector including finance
- It is used for finding hidden patterns and extracting useful information from data.
Why we use Python for Machine Learning?
In the earlier days, people used to execute Machine Learning tasks by writing all the algorithms and maths and mathematical calculations manually. This rendered the method slow, tedious, and time-consuming. But in the modern day’s specific python libraries, systems, and modules are becoming very simple and powerful relative to the older days. Currently, Python is one of the most common programming languages for this challenge and has replaced other languages in the industry, one consideration being its extensive library collection. Python’s versatility has attracted many developers into building new machine learning libraries. Python is becoming increasingly famous among machine learning experts because of the vast selection of libraries.
Some of the vital factor to use python in machine learning are as follows,
- Easy to learn: Python employs a very basic syntax to execute basic computations such as connecting two strings to complex processes like constructing complex Machine Learning models. Python is user friendly and easy to learn.
- Platform Independent and Open Source: Python can run on multiple platforms, including Windows, macOS, Linux, Unix, and so on. It is an open-source language.
- Less line of Code: To implement or create a machine learning model, we have different types of algorithms and python supports for pre-defined packages, we don’t have to code these algorithms. With a few lines of code, one can implement any machine learning algorithm.
- Inbuilt Libraries: Python has several inbuilt libraries to implement various Machine Learning algorithms.
- Huge Community Supports: Python has several communities, classes, and forums where programmers post and assist each other with their errors.
Popular Python Libraries for Machine Learning
Some of the famous python libraries which are used in machine learning are as follows,
1. NumPy Library
- NumPy is the fundamental package used in machine learning for performing scientific calculations.
- It is a python library that gives a multidimensional array object, various derived objects.
- It has multiple operations such as mathematical, logical, shape manipulation, sorting, selecting, basic statistical operations, etc.
- It is convenient when handling linear algebra, Fourier transforms, and random numbers.
- The other libraries, such as Tensor Flow, uses NumPy at the backend for manipulating tensors.
- The command to import the NumPy library is as follows,
>>import NumPy as np
- PANDAS is an open-source library known as one of Python’s most popular machine-learning library.
- It is used for Data Analysis, which provides different data analysis tools that are easy to use.
- It is used to calculate statistics like average, median, max, or min of each column
- It is a popular tool for presenting Series and Data Frames.
- It is used to explore a dataset stored in a CSV on your computer. Pandas will extract the data from that CSV into a DataFrame — a table
- It also plays the leading role in the data preprocessing phase of machine learning for handling the missing values with some statistical measure.
- It can be used to manipulate large datasets and to perform subsetting, data slicing, indexing, etc.
- To import pandas, we usually import it with a shorter name since it’s used so much:
>>import pandas as pd
- SciPy Library
- SciPy is a very prominent library of machine learning, as it includes numerous modules for optimization, linear algebra, integration, and statistics.
- It is also very useful for image processing.
- The SciPy library was designed to work with NumPy arrays and to provide numerical functions that are user-friendly and reliable.
- It provides support for signal processing, data structures, and statistical algorithms, the creation of sparse matrices, and so on.
- To import scipy, we need to write the following line of code
>>> import scipy as spy
- Matplotlib Library
- Matpoltlib is a prevalent Data Visualization library in Python.
- This can be very helpful in machine learning when discovering and getting to know a dataset and can help with classifying patterns, corrupt data, outliers, and much more.
- The library assists in producing the following types of graph /plots
- Line Graph / Plot
- Bar Graph / Plot
- Histogram Graph / Plot
- Scatter Graph / Plot
- Pie Graph / Plot
- pyplot is the library that is a Python-compatible version of MATLAB.
- To import matplotlib.pyplot, we need to write following line of code
>>import matplotlib.pyplot as plt
- Seaborn Library
- It is one of the useful libraries in machine learning and Data Science related projects for better visualization of the data.
- It is a Python library that is defined as a multi-platform data visualization library built on top of Matplotlib.
- You can generate line plots, scatter plot, bar plot, box plot, count plot, relational plot, and many more plots with just a few lines of code.
- It comes with various built-in style themes and generates matplotlib graphs
- To import the Seaborn Library, we need to write following line of code
>>> import seaborn as sns
- Scikit-learn Library
- Scikit-learn is a free machine learning library for Python.
- The Scikit-learn library was created by David Cournapeau as part of the Google Summer of Code initiative in 2007. In 2010 INRIA engaged and released the public in January 2010.
- Built on top of two Python libraries-NumPy and SciPy, Scikit-learn has become the most popular Python machine learning library to develop machine learning algorithms.
- It comes with dozens of functions specifically intended to build a pattern. It comprises all the algorithms of Supervised and Unsupervised Machine Learning.
- It requires various algorithms to incorporate traditional machine learning and data mining functions, such as reducing dimensionality, classification, regression, clustering, and model selection.
- To import the Scikit-learn, we need to write the following line of code
>>> import sklearn as skl