Machine learning is a process that applies algorithms to analyse and interpret large amounts of data online in order to make predictions or decisions. This allows companies to gain insights into consumer behaviour, identify trends, and improve efficiency and decision-making.

One prime example of machine learning in action is the development of self-driving cars.

Companies like Tesla use machine learning algorithms to process video footage from their vehicles' cameras, helping them program the functionality of their cars more effectively. Another application of machine learning is using Python language code to determine how much screen time an actor has had in a movie. This can then be used to calculate their pay for that film.

Overall, machine learning plays an essential role in modern business and technology, helping companies to understand customer needs and optimise operations.

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Why is Python preferred for AI?

As per the previous examples, we saw how a video is like an assortment of pictures for computers. These images are referred to as "unstructured data," and Python helps assign codes that can be used by machines to convert this image into structured data. This will create a basic understanding of the video.

Here are the top reasons why operators prefer Python language for AI learning:

1. Python is easy to learn:

Python language has a very easy-to-understand syntax which is in the English language. It is also an interpreted language, which means that there is no need to compile code before running it.

For example, this is the method of writing a string:

my_string = 'Welcome to my blog"

print(my_string)

2. Python has many libraries:

Python language has a large number of libraries that can be used for machine learning. These libraries provide ready-made functions and modules that can be used to build machine learning models quickly and easily. This enables people with a non-science background to easily progress from a beginner to an intermediate level in machine learning.

3. Python is versatile:

Python is an advanced programming language that can be used with other types of languages as well. You don't have to find the source code and are free to make instant changes to get results.

4. Is Python an open-source?

Python is an open-source computer language, which implies that it is free to read, function, and distribute. This makes it a very popular choice for businesses and individuals looking to save money.

5. Python has a large community:

Python language has a large and active online community of users and courses which can contribute to the development of the language. This makes it easy to find help, support and free tutorials when needed.

Learn python with a community
A large community of Python learners, Source: Unspalsh

6. Compatible with many operating systems

Name any basic operating system like Windows or macOS; the Python language can be read using it. You can easily transfer any Python file to different operating systems.

Why is Python a very useful programming language?

Python is a powerful programming language. Even if you have just started, its simplicity enables the development of trustworthy systems while machine learning and AI are used behind them. Instead of focusing on the language's technical intricacies, developers may now devote all of their time to addressing an ML problem.

However, the main reason Python language is one of the best programming languages is its modules and libraries that support machine learning. Several popular machine learning algorithms are linear regression and k-means clustering. These can be implemented in Python using the scikit-learn library. Learn about the blend of robotics and python.

Here is a list of famous Python libraries used in machine learning:

Django

It's a python framework that makes it simple to build sophisticated web applications for free. Complex machine learning algorithms can be employed by issuing an API endpoint with the aid of the Django REST framework.

Flask

The goal of Flask is to assist you in constructing web apps quickly. It's effortless to deploy machine learning algorithms using Flask. It allows end-users to interact with your Python code directly from their browser without the need for any libraries, code files, or other complicated infrastructure.

Machine learning with Python
Python codes on the laptop screen, Source: Unsplash

TensorFlow

This is a free and open-source library for numerical calculation based on data flow graphs. Initially, Google's Brain team created it for internal use at Google. TensorFlow combines various machine learning and deep learning (also known as neural networking) models and algorithms to offer a unified metaphor for making them useful. Python language is used to provide an online front-end API for constructing applications with the framework while still running them in high-performance C++.

PyTorch

This is a free machine-learning software package based on the Torch library. It was created by Facebook's AI Research lab. PyTorch features dynamic computation, which allows for more creative possibilities in the construction of complex architectures. It employs core Python language concepts such as classes, structures, and conditional loops - all of which are very familiar to our eyes and much easier to comprehend than equivalent C++ code.

Scikit-Learn

This open-source software library provides tools for machine learning and statistical modelling. It offers a consistent interface in Python, which includes classification algorithms such as regression or clustering with dimensionality reduction through standard nonlinear transformations.

Matplotlib

It is a python module that allows you to generate 2D and 3D charts. It includes a variety of graphs, including the Line Plot, Bar Plot, Scatter Plot, Histogram e.t.c through which we can create a wide range of data types.

NumPy

NumPy is a python library that allows you to work with large data arrays. In simple terms, it is the numerical extension of Python. It is the keystone in the machine learning stack.

Pandas

It is a python library used for data manipulation and analysis. This package, which is based on NumPy, provides support for multi-dimensional arrays.

Keras

This is a python library that makes it simple to create deep learning models. It was created with the intention of doing experimentation in less time. As for good research, it is critical to move from concept to conclusion as quickly as possible.

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Industries that uses Python Machine Learning

There are many industries in which you can use machine learning. Here are some examples!

Finance

Machine learning has the ability to anticipate stock prices and create investment selections. ML may process millions of data sets in a short period of time to improve results without having to be specifically trained to do so. This can assist in identifying fraudulent transactions and open the door for a safer and more secure internet transaction.

Healthcare

You can use the science of machine learning to diagnose illnesses, forecast patient outcomes, and create new therapies. Pattern recognition can also help physicians detect tumours on scans. The wearable devices can be analysed for various data like steps, oxygen levels, and heart rates. This real-time monitoring generates an enormous amount of data that allows doctors to analyse their patient's well-being and progress in treatment.

Transportation

You can use machine learning to optimise traffic flow, route vehicles, and predict travel times. This can help you in work like highway management and road work supervision. But, one of the popular reasons is self-driving cars. Often complex algorithms are created for a system to incorporate new functions such as analysing and optimising data gathered from a variety of sources, routing, mapping, and navigating the situations in the real world.

Cybersecurity

Machine Learning offers a unique opportunity to analyse patterns, learn from them, and apply that learning to safeguard products and networks against future breaches.

Software development

The demand for software development is increasing, and ML can assist with a variety of procedures. A budget and timetable are common worries, but ML may read and analyse data from prior projects such as user stories, and forecasts, and provide a more accurate financial estimate.

Manufacturing

With AI and ML becoming more popular in creating and assembling items, it has helped lower production costs and time. When it comes to handling a large quantity of data, ML is essential, as well as its use during real-time error detection, supply chain monitoring, and object tracking.

Telecommunications

You can use machine learning to improve customer service, reduce fraud, and optimise network performance. Conversational chatbots are a significant step forward in the field of AI-powered customer service. Providing an AI-powered 24/7 client support chat may assist with most inquiries and transfer customers to live agents when required. This lowers consumer service costs while also improving consumer satisfaction. Learn about the use of python in gaming!

Machine learning after class 12th CBSE
Python in the field of agriculture, Source: Unsplash

Agriculture

Sensor data, machine learning digital tools, and artificial intelligence have all been used to improve crop yield predictions, evaluate agricultural crops, detect plant disease and identify plant species.

Best Machine Learning Python Tutorials Online

There are several ways to learn Python for ML, like reading a book, online or offline courses for beginners, modules, or video tutorials. So whether you are looking for the best Machine learning after class 12th CBSE or free programs, check out some of the resources below.

Some of the best tutorials and courses available online are on:

  • Coursera
  • Udemy
  • edX tutorials
  • Datacamp tutorials
  • Tutorials Point
  • Python for You
  • Learn Python
  • Real Python
  • Udacity tutorials
  • Machine Learning Crash Course by Google

Books to read to become a pro in Python and ML:

  • Introduction to Machine Learning with Python by Sarah Guido
  • Hands-On Machine Learning with Scikit, Keras, and TensorFlow - Aurélien Géron
  • Machine Learning Engineering - Andriy Burkov
  • Machine Learning for Absolute Beginners: A Plain English Introduction - Oliver Theobald

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