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machine-learning in Python Reviews & Product Details

machine-learning in Python Product Details

machine learning support vector machine (SVMs), and support vector regression (SVRs) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis.

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machine-learning in Python

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machine-learning in Python Community

Product Description

machine learning support vector machine (SVMs), and support vector regression (SVRs) are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis.

Value at a Glance

Averages based on real user reviews.

Time to Implement

1 month

Perceived Cost

$$$$$

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KA

Komal A.

Spec Analytics

Enterprise (> 1000 emp.)

1/22/2025

"Pandas With Pyton"

4.5/5

What do you like best about machine-learning in Python?

I like that Python offers a rich ecosystem of libraries like TensorFlow, scikit-learn, and PyTorch, making it easy to implement and experiment with machine learning models efficiently. Review collected by and hosted on G2.com.

What do you dislike about machine-learning in Python?

I dislike that machine learning in Python can sometimes be resource-intensive, requiring significant computational power for training large models. Review collected by and hosted on G2.com.

MI

Mikhail I.

Director of Software Engineering

Enterprise (> 1000 emp.)

12/4/2024

"Director of Engineering - Oracle"

4/5

What do you like best about machine-learning in Python?

- Makes Data Preparation and exploration easy, specially at initial stage

- No need for data extraction. Can work with the data in DB

- Pipeline is simple Review collected by and hosted on G2.com.

What do you dislike about machine-learning in Python?

- Limited algorithms supported

- Cost, due to license Review collected by and hosted on G2.com.

What problems is machine-learning in Python solving and how is that benefiting you?

Python is ver popular language now. While keeping data in DB, no need for extraction steps, we can do complete POC for supervised, classification, bag of words, ... solutions for data in DB.

Without doing ETL, we currently are able to do some supervised learning solutions for data in Oracle DB using OML4Py Review collected by and hosted on G2.com.

KM

Kunal M.

Data analysts

Mid-Market (51-1000 emp.)

8/21/2024

"My review on machine learning with python"

5/5

What do you like best about machine-learning in Python?

The thing I like best about machine learning with python is it provides extensive libraries and framework which make our work easy. It's has one of the best community support for coders.

Best for visualization with help of Matplotlib as Seaborn... Review collected by and hosted on G2.com.

What do you dislike about machine-learning in Python?

Currently there is nothing which I see I dislike about machine learning with python. Review collected by and hosted on G2.com.

SM

Shivam M.

Information Technology and Services

Small-Business (50 or fewer emp.)

6/2/2023

""Python: The Ideal Language for Machine Learning ""

5/5

What do you like best about machine-learning in Python?

Since Python is a very easy langauge and for machine learning, we have to write a large, diverse, and very complex code which is quite difficult in any other programing language so from that point python is the best suitable language for machine learning. Also, it has huge libraries that help developers to write code efficiently and effectively. Review collected by and hosted on G2.com.

What do you dislike about machine-learning in Python?

As a machine-learning engineer, I have found Python to be an amazing machine-learning language, and I value its adaptability and breadth of features. Python is well-known not only for its success in machine learning and data science but also as a top choice for web development and other disciplines. Its extensive library ecosystem, user-friendly syntax, and lively community make it a favorite language for developers, enabling them to design new and efficient solutions. Python actually shines at providing a smooth and engaging experience for both machine learning practitioners and fans. Review collected by and hosted on G2.com.

What problems is machine-learning in Python solving and how is that benefiting you?

The use of machine learning in Python has contributed to the creation of powerful tools and libraries that have enabled the development of more advanced and sophisticated AI models like chat gpt. It provides efficient and scalable solutions to complex problems, helps in automating tasks, enhances decision-making processes, enables data-driven insights, and opens up opportunities for innovation and competitive advantage. Python's rich ecosystem of libraries, extensive documentation, and active community further support practitioners in building and deploying machine learning models effectively. Review collected by and hosted on G2.com.

PB

Prasanth B.

Process Specialist

Small-Business (50 or fewer emp.)

10/19/2023

"It's important to know and apply"

4.5/5

What do you like best about machine-learning in Python?

Pandas - I love to explore the data with pandas Review collected by and hosted on G2.com.

What do you dislike about machine-learning in Python?

At times simple task we need to follow the sane steps Review collected by and hosted on G2.com.

SH

Syed Adeel H.

Infrastructure Manager

Small-Business (50 or fewer emp.)

3/29/2023

"Data Mining for machine Learning"

4.5/5

What do you like best about machine-learning in Python?

One of the key benefits of using Python for machine learning is its ease of use. The language has a clean and intuitive syntax that makes it easy to write and understand code, even for those who are new to programming. Additionally, Python has a large and supportive community that provides plenty of resources and tutorials to help users get started. Review collected by and hosted on G2.com.

What do you dislike about machine-learning in Python?

Python is an interpreted language, which means that it is slower than compiled languages like C++ or Java. This can be a disadvantage when working with very large datasets or complex algorithms. Review collected by and hosted on G2.com.

OG

Oliver G.

Technical Sales Engineer

Small-Business (50 or fewer emp.)

6/28/2022

"Super quick to get going, great results"

4.5/5

What do you like best about machine-learning in Python?

Very well supported tools like tensorflo Review collected by and hosted on G2.com.

What do you dislike about machine-learning in Python?

Performance can be problematic and hard to diagnose, especially with by default using 100% of any gpu given Review collected by and hosted on G2.com.

UM

Verified User in Management Consulting

Small-Business (50 or fewer emp.)

11/12/2021

Business partner of the seller or seller's competitor, not included in G2 scores.

"Python for Machine Learning (Model Development & Deployment)"

5/5

What do you like best about machine-learning in Python?

Python is the most advanced programming language for implementing machine learning models from scratch. It provides a vast range of libraries and custom functions for building, training, and developing ML models. It offers easily interpretable, reasonable, and concise code and allows developers to build and test complex machine learning algorithms on structured and unstructured data with ease. Review collected by and hosted on G2.com.

What do you dislike about machine-learning in Python?

Python is an interpreted programming language that has limited speed since code execution occurs line by line. Threading is not supported in Python, which serves as an issue while implementing ML solutions at scale. Review collected by and hosted on G2.com.

Recommendations to others considering machine-learning in Python:

I would definitely recommend using Python for building machine-learning-based applications, provided your team has the expertise in coding. Python requires developers to be familiar with the concept of functions, classes and object oriented programming. Review collected by and hosted on G2.com.

What problems is machine-learning in Python solving and how is that benefiting you?

We are using python in multiple projects for building machine-learning solutions from scratch. It helped the developers to quickly train and test the ML models on structured data for building risk-based scorecards. Scikit-learn library provides all the algorithms for implementing machine-learning models such as Random Forest, XGBoost, SVM, Linear and Logistic Regression. We also use Python for automating processes that involve manual screening and verification. Review collected by and hosted on G2.com.

AD

Verified User in Design

Enterprise (> 1000 emp.)

5/19/2022

"It is very easy to create machine learning model with the help of several python library ."

5/5

What do you like best about machine-learning in Python?

Creating a machine learning model with the help of python is very easy, also if you are integrating it with synchronous pipeline python works very well. Review collected by and hosted on G2.com.

What do you dislike about machine-learning in Python?

I can only think of a little slow otherwise everything is good. Review collected by and hosted on G2.com.

AR

Alvaro R.

Profesor titular

Mid-Market (51-1000 emp.)

6/17/2021

"Powerful and modern machine-learning using python"

5/5

What do you like best about machine-learning in Python?

The last and more advanced models for machine learning are available in python. This allows you to perform up-to-date experiments. There are a lot of tutorials for using machine learning with python and the most modern systems use it.

If I have any problem with the output, or any error, there are a lot of internet forums showing any possible solution. That encourages me to use it because I can be sure of solving any problem I may have. If you do not find the solution, you can post a question and wait for an answer in the next days.

On the other hand, machine learning with python allows using HW acceleration such as GPUs. You only need to set the proper HW.

Another advantage is the fact that there are several libraries for doing machine learning with python. In case you do not like any, you can choose among the others. Review collected by and hosted on G2.com.

What do you dislike about machine-learning in Python?

There are multiple libraries and the documentation for some of them is sometimes incomplete. Besides, some functions change from different versions, making old code incompatible with new code. Review collected by and hosted on G2.com.

Recommendations to others considering machine-learning in Python:

If you already know what algorithm you want to use, you only need to search for the name of that algorithm in the library. If you have any doubt, I suggest to take a look at the API or any examples in the web. Review collected by and hosted on G2.com.

What problems is machine-learning in Python solving and how is that benefiting you?

I mostly work with text classification and other tasks related to natural language processing. I can process text with other python tools and connect the output to any machine learning model. Review collected by and hosted on G2.com.