machine-learning in Python Reviews & Product Details


What is machine-learning in Python?

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 Profile Details

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Project Engineer
Computer Software
Small-Business
(11-50 employees)
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"Machine Learning Era"

What do you like best?

Packages like Sci-kit learn and Keras are very useful for fast deployment in the production line. Deep Learning in computer vision shows a considerable result. With a huge amount of data, Python Machine Learning frameworks help us to develop faster and reduce our development time. Frameworks like Tensorflow, caffe, pytorch are very effective in deep learning development and deployment.

What do you dislike?

When we have a large amount of dataset, it is necessary to analyze it before we use it for development. Here in Python Machine Learning, there is no good data analysis framework in python. I dislike python because it's development time is very high.

Recommendations to others considering the product:

Good tool.

What problems are you solving with the product? What benefits have you realized?

We use machine learning in quality analysis. Earlier we used custom algorithms for finding defects on a product in the production line. After getting into python machine learning all the features are learned by the framework itself, we only need to make sure the data we give in is unbiased. The development burden is reduced a lot. Now we are focussing on making better hardware for our products.

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Computer Software
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"One of the best library for implementing machine learning"

What do you like best?

It's amalgamation of all sorts of machine learning algorithms along with their examples and tutorials is the best thing. It is very well documented which makes it easy to implement. It is also user friendly.

What do you dislike?

It has provided many methods of implementation which is quite good but arises too much confusion at the same time. So one needs to do some research as to which one to select among the available options.

Recommendations to others considering the product:

Machine learning in python is one full package of great libraries one of which is "scikit-learn". Along with it has provided all sorts of useful examples as well as tutorials which makes us easy for implementing machine learning.

What problems are you solving with the product? What benefits have you realized?

It has helped quite a lot in research works related to data mining and also in big data analysis. We could train our data with the different algorithms available and so were able to judge the accuracy. The most important usage is of the "scikit-learn" library.

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Machine Learning Engineer
Wireless
Mid-Market
(51-200 employees)
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"The simplicity of using python for machine learning"

What do you like best?

I like how simple python is to use as well as the amount of libraries that are already jn existence to help with reducing the time of development.

What do you dislike?

The only thing I really dislike is when they update the version of python and you are left with codes that work only on certain versions. This becomes your job to then update or reduce the code syntax depending on the version you are using.

What problems are you solving with the product? What benefits have you realized?

I am using Python in order to classify specific images and also to localize specific objects.

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Research Assistant
Mid-Market
(201-500 employees)
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"Best Library for Machine Learning"

What do you like best?

Given the huge amount of investment different companies have made on python for machine learning there are really nice tools available for all sort of machine learning algorithms in python. Almost every deep neural network framework is written mainly for Python or has a Python wrapper. SciPy Library provides all you need to do most of the basic machine learning algorithms work.

What do you dislike?

Unlike MATLAB different companies are developing tools for Python. There are always new libraries that are incompatible with others. I usually don't upgrade to a new version of a library until I something stops working.

Recommendations to others considering the product:

If you are familiar with basic of OO programming. Using Python machine-learning tools should be easy for you.

What problems are you solving with the product? What benefits have you realized?

We train different machine learning algorithm for computer vision applications.

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GH
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(10,001+ employees)
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"Extensive machine learning libraries and tools"

What do you like best?

Comprehensive collections of machine learning algorithms and lots of examples and tutorials, in particular scikit-learn library have almost every possible machine learning algorithm included

What do you dislike?

Documentation for some functions is rather limited. Not every implemented algorithm is present. Most of the additional libraries are easy to install but some can be quite cumbersome and take a while.

Recommendations to others considering the product:

Machine learning in Python have lots of great libraries, check out tutorials for each module before using it as it usually has lots of useful examples.

What problems are you solving with the product? What benefits have you realized?

Unsupervised clustering and classification. The most popular library scikit-learn (or sklearn) have a collection of examples and tutorials that can be easily followed. Other modules are easy to install.

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Laboratory Specialist
Hospital & Health Care
Enterprise
(5001-10,000 employees)
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"Train and gain "

What do you like best?

It’s said that training a network is very hard on machine learning, but if do same through python, it becomes easier. Try , you will get amazed

What do you dislike?

There is nothing I dislike about doing machine learning in python

What problems are you solving with the product? What benefits have you realized?

Currently analyzing genome data using python machine learning and trying to build some algorithm that could detect the genetic variant

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Post-Baccalaureate IRTA
Research
Enterprise
(1001-5000 employees)
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"Machine learning in python can be used by even the most technologically challenged!"

What do you like best?

There are so many well-documented, common-sense, easily implementable python scripts and packages for machine learning. Scikit learn has some amazing tutorials, for concept learning, function learning or “predictive modeling”, and clustering and finding predictive patterns. With the language of python itself, it is easy to understand how to utilize the Kmeans algorithm, and implement aspects of machine learning with your own data.

What do you dislike?

Getting started can be difficult! Tutorials can be hard to find, especially if you aren't used to using open-source languages like python.

What problems are you solving with the product? What benefits have you realized?

Big data analysis to measure outcomes for our smartphone app intervention

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Mid-Market
(51-200 employees)
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"Machine Learning with Python Pandas"

What do you like best?

Its easy to use. Lots of documentation online.

What do you dislike?

Currently, nothing. I prefer it to Matlab or R.

Recommendations to others considering the product:

Learn and use it as much as you can. This is definitely going to be used in the future as well.

What problems are you solving with the product? What benefits have you realized?

It depends what comes my way. I have done forecasting of stocks, developed churn prediction models for retail industry.

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(501-1000 employees)
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"Easy to learn, many resource = efficient!"

What do you like best?

Machine learning with Python is very much easy to set up. Once you have download Python, assuming if you download with Spyder and Anaconda, everything will be pre-packaged.

For people with amataeur coding knowledge like me, whenever I hit a brick wall, I’m able to go online and find solutions.

What do you dislike?

Unlike Tableau, there is no official platform, at least I couldn’t find one. Plus there’s way too many packages for machine learning. You need to do your research to know which is suitable for your scenario.

What problems are you solving with the product? What benefits have you realized?

Natural lanaguage processing, text classification.

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GH
Enterprise
(1001-5000 employees)
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"It really easy running machine learning applications using python "

What do you like best?

scikit-learn package included with most of efficient and recent machine learning tools such as Random Forest, SVM, Boosting and so on. Its really easy and fast with python scikit-learn package.

What do you dislike?

You just need basic coding skills in python. Once you are familiar with python coding which is pretty easy, machine learning applications are piece of cake using python.

What problems are you solving with the product? What benefits have you realized?

I have used mostly in my research work related to data mining and signal processing.

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Small-Business
(Myself Only)
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"Machine Learning using Python"

What do you like best?

Python is one of the most popular programming languages for solving the problems associated with machine learning. Python libraries like Keras, Theanos, TensorFlow, and Scikit-Learn have made programming machine learning relatively easy.

What do you dislike?

Sometimes because of data Python IDE gets hanged.

Recommendations to others considering the product:

Machine learning is best used in Python because of ML libraries and especially for data visualization.

What problems are you solving with the product? What benefits have you realized?

I use machine learning to build classification models to solve industrial problems. I have realized that it is easy to interpret and understandable. Easy to create a confusion matrix which is used for getting classification accuracy.

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Process Data Engineer(Subsurface and Wells)
Oil & Energy
Enterprise
(10,001+ employees)
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"Great Language for Machine Learning"

What do you like best?

A lot of modules available for machine learning, just have to prepare the data as per the requirement and then the modules take care of algorithm

What do you dislike?

Preparation of data for training the algorithm

What problems are you solving with the product? What benefits have you realized?

Great way to automate processes and analysis of data. Also helps to predict a lot of values.

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"Python is one of the best tools for machine learning"

What do you like best?

Tensor flow tool for deep learning. This is the best thing I like about python as it offers so much flexibility for deep learning

What do you dislike?

I find debugging a little tedious sometimes.

Recommendations to others considering the product:

Great for deep learning tools

What problems are you solving with the product? What benefits have you realized?

Recognition problems. Python offers a bunch of libraries.

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G
Mid-Market
(51-200 employees)
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"Machine Learning with Python"

What do you like best?

Ease of Setup, plethora of options, tutorials, blogs, resources available, Ease of start

What do you dislike?

Nothing. It is great. Because everything is open source, finding support or help can be a bit tricky for custom problems.

Recommendations to others considering the product:

There are great courses available online. Pick one. Get started. Buy some cloud space if you don't have processing power, find a project on Kaggle and just get started

What problems are you solving with the product? What benefits have you realized?

Multiple things. Trying sentiment detection, voice profiling, NLP on phonecalls

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GC
Enterprise
(10,001+ employees)
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"Python - The easiest way to get your hands dirty in machine learning"

What do you like best?

The ease of implementation that python libraries offer and available documentation.

What do you dislike?

Too many ways to implement the same thing, sometimes ot becomes confusing.

Recommendations to others considering the product:

Start with the basic iris dataset classification problem.

What problems are you solving with the product? What benefits have you realized?

I solve a lot of classification and regression problems with scikit learn library. A well explained documentation is available online. There are many websites for beginners. With just a few lines of code you can train your own Machine learning model.

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Research Assistant
Hospital & Health Care
Enterprise
(10,001+ employees)
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"no comments"

What do you like best?

It is easy to learn and improve your coding skills.

What do you dislike?

Sometimes the code is incomplete and therefore the project remains incomplete.

What problems are you solving with the product? What benefits have you realized?

Learning from someone else's code.

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GI
Enterprise
(1001-5000 employees)
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"great ML functions"

What do you like best?

available build in functions

open-source

free

available tutorial and learning material online

What do you dislike?

slight learning curve if a transition from another language

Recommendations to others considering the product:

you only need to know the higher level programming language to use it

very easy to learn

python is the new R if you are in DS field

What problems are you solving with the product? What benefits have you realized?

medical and disease classifications

diagnosis prediction

it's quick to run ML algorithms in python

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G
Mid-Market
(201-500 employees)
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"Well advanced software"

What do you like best?

It's exactly what it's supposed to be. Python is one of the best coding languages out there still to this day and this software makes it so much easier to test.

What do you dislike?

Honestly, nothing really to dislike about this software.

What problems are you solving with the product? What benefits have you realized?

I am able to automate a lot of the testing that I would spend hours on end to complete.

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GI
Mid-Market
(51-200 employees)
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"Best Open-Source Tool Out For Machine Learning"

What do you like best?

Everything. Python is the most production friendly, scalable fast, object oriented, open source language with best support in the world to build machine learning model in the industry

What do you dislike?

There is nothing which I don't like about Python

Recommendations to others considering the product:

Go for it. Hands Down!

What problems are you solving with the product? What benefits have you realized?

Building Production grade machine learning model within the company for many many projects.

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"ML in Python review"

What do you like best?

Gradient descent and Linear regression model

What do you dislike?

It takes more time to execute the code, but the ide is really helpful, that is what i like about this software.

What problems are you solving with the product? What benefits have you realized?

I am making machine to learn more with this.

machine-learning in Python User Ratings

9.2
Ease of Use
Average: 8.3*
8.6
Quality of Support
Average: 8.2*
8.7
Ease of Setup
Average: 8.3*
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