How python Programming language can fix the challenges comes in machine learnings?

How python Programming language can fix the challenges comes in machine learnings?

The growing demand and accessibility of artificial intelligence solution continuously increasing. From health care to hotel management and from finance to the aviation industry. Each and every industry has noticed the use of artificial intelligence in their business.

Although the use of the latest technology has always been required for consideration for companies which are striving to get ahead of the curve and it becomes universal for the core of operations for many businesses.

In the present scenario, the increasing use of technical implementation of all AI concepts created a wide range of problems which requires both the creative as well as efficient tools to resolve it.

In this situation, python plays a key role because it is a highly versatile programming language which offers both the significant flexibility as well as comprehensiveness. It has a huge collection of libraries and a giant community to support may developers and programmers. And this is the thing which makes it perfect to create machine learning solutions.

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Let’s dive into the deep knowledge of python and its qualities.

Flexibility: If you are seeking a flexible, scalable and portable platform the offers all the developers to choose between scripting and OOPs. in some cases, it needs to combine with the other languages.

Popularity: As per the survey happened in 2018 in which thousands of developers participated. This survey was conducted by the python software foundation, in which the language adoption was at 84% and it is continuously growing as the IT sector is also continually growing.

Stack Overflow found that python programming language is the fastest-growing language and it has become the most popular among data scientists.

Great community

As it is a widely popular open-source language, it has a strong and active community of experts across the world, who are strongly striving to improve their knowledge as well as helping the other members. So this is an excellent way to resolve any problem of the beginners.


Highlights of the Standard Library

Having large numbers of libraries makes it more scalable as an open-source language. All the libraries can be utilized for various purpose and that also provides solutions comes under machine learning.

Let’s discuss the challenges comes in machine learning that can be solved with the help of python

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Supervised machine learning

Problems:  Supervised machines, where the algorithm learns structure a marked dataset and its yield is as of now known, is by a wide margin the most well-known over an assortment of utilization cases. Here are the two principle territories where supervised learning is applied, with instances of their genuine utilization.

Classification: this technique is utilized to arrange information into wanted and unmistakable classes. It predicts a discrete value.

For example, classification can be utilized to evaluate financial soundness or to make increasingly precise readings of X-rays outputs to distinguish early indications of cancer.

Regression: the anticipated result is an estimation of a numeric value, the purported “dependent variable,” which relies upon different variables (known as independent variables). Regression is one of the most widely recognized algorithms, utilized in problems that include persistent numbers. A portion of its uses incorporates interest anticipating, property value estimation, and money related forecasting.

Python solutions

A lot of libraries are there that can be used to the problems of classification and regression problems. Here is the list of the most popular ones with excellent community support

Linear and Quadratic Discriminant Analysis, sci-kit-learn, Nearest Neighbors, decision trees, ensemble methods, LightGBM, Caffe2, TensorFlow, PyTorch, Keras.

Numpy is the most popular library as the surveys conducted the Python Developers surveys in 2018.

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Unsupervised machine learning


In unsupervised machine learning, it is handed an unlabeled data set with no preparation directions or known result. It depends on its own capacity to take care of complex problems dependent on the input data.

Clustering and matrix factorization are two instances of unsupervised learning strategies. They are utilized to gather components dependent on the similitude between object properties. Clustering has been utilized effectively across numerous ventures as client segmentation, and matrix factorization is the spine behind recommender frameworks utilized by Netflix, YouTube, and various online shopping platform.


The unsupervised method has been sometime used for supervised machine learning and it is also compatible with a wide range of python libraries. The best one for this is scikit-learn (with algorithms such as k-means, OPTICS, BIRCH, DBSCAN).

Some recommended libraries are also having popularity among the developers which are

Surprise, which is used for neighbourhood-based methods, NMF, PMF,  SVD, SVD++

LighFM, also known as (hybrid latent representation recommender and matrix factorization

LightFM, also known as hybrid latent representation recommender and matrix factorization

Spotlight, also known as PyTorch which is required to build recommender models

Reinforcement learning


Reinforcement learning refers to algorithms that figure out how to accomplish specific destinations and settle on the correct choices by taking various activities and getting input on them. They can change their conduct to accomplish the most ideal outcome and stay away from botches.

The reinforcement learning algorithms have been utilized in computer games to accomplish above-human execution, for example on account of AlphaGo.

Reinforcement learning was additionally tried in a traffic light control system and answered to demonstrate better outcomes than conventional techniques.


The developers found problems in reinforcement learning is much more specific than the other two machine learning.  And its solution is more difficult to find. Although, there are some libraries that can help to fix these issues:

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Keras-rl, known as Deep Reinforcement Learning for Keras

TensorForce, it is known as TensorFlow library which is used for applied reinforcement learning

Coach, which includes DQN, DFP, NAF and many more

Python has the ability to handle a wide variety of machine learning challenges, and it is the reason which made this programming language most used language in the world, which has cover the industries from health care to finance.

It is an open-source and high-level machine learning programming language that means, its popularity is increasing among developers to data scientists.

The main reason, why python is considered the top choice for Machine learning-related problems are:

  • flexibility,
  • simple syntax,
  • It allows rapid testing for complex algorithms,
  • It provides access to comprehensive libraries.

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So this is all about how python can be utilised as the most suitable tool to tackle machine learning challenges. If you want to become an expert in this field, then join AIDM, which is the best python training institute in Laxmi Nagar Delhi. And we also help our students to get them a decent job in a well-reputed company.

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