Python For Machine Learning and Data Science

Python For Machine Learning and Data Science

What is Python?

  • Python is a high-level, interpreted programming language used for web development, scientific computing, data analysis, artificial intelligence, and machine learning.
  • Created by Guido van Rossum in the late 1980s, Python has become one of the most popular programming languages in the world.
  • Python’s syntax is straightforward and readable, making it easy for beginners to learn and use.
  • Python has a large and active community of developers, providing access to many libraries and frameworks for a wide range of programming tasks.
  • Python is an interpreted language, executing code line by line as it is read, which makes it easy to test and debug code and catch errors as they occur.
  • Python is highly portable, meaning that code written on one platform can be run on another platform without modification.
  • Python’s popularity in data science and machine learning is due to the wide range of libraries available, such as NumPy, Pandas, Matplotlib, Scikit-learn, TensorFlow, and PyTorch.
  • With the right tools and knowledge, Python can be an incredibly powerful language for working with data and building intelligent systems.

Python libraries for Machine Learning and Data Science

Python has become one of the most popular programming languages for machine learning and data science due to its ease of use and the large number of libraries available. Some of the most basic Python libraries for machine learning and data science are given below:

  1. NumPy – NumPy is a fundamental library for numerical computing in Python. It provides support for arrays and matrices, which are used extensively in machine learning algorithms.
  2. Pandas – Pandas is a library that provides data structures for efficient data analysis. It is used for data manipulation, data cleaning, and data visualization.
  3. Matplotlib – Matplotlib is a plotting library that provides a variety of graphs and charts to visualize data.
  4. Scikit-learn – Scikit-learn is a machine-learning library that provides a wide range of supervised and unsupervised learning algorithms for classification, regression, and clustering.
  5. TensorFlow – TensorFlow is a popular deep-learning library that provides support for building and training neural networks.

Numpy (Numerical Python)

It is a fundamental library for numerical computing in Python that provides support for large multi-dimensional arrays and matrices, along with a wide range of mathematical functions to operate on these arrays. NumPy is widely used in scientific computing, data analysis, and machine learning applications. It also provides tools for integrating with other languages such as C and Fortran, making it a popular choice for scientific computing in many fields.

Installation

  • Installing NumPy in Anaconda: Open the Anaconda prompt and run the code conda install numpy“.
  • Installing NumPy using Command Prompt: To install NumPy using the command prompt on a Windows machine, you can use “pip install numpy“.

Import NumPy

In order to use the features of NumPy, first you have to import on the python file.

Create an Array using NumPy

Let’s Create a 2-D array using NumPy

Check the dimensions of the arrays using Numpy

Indexing

 

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