machine-learning in Python Reviews & Product Details

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.

Preparation of data for training the algorithm Review collected by and hosted on G2.com.

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 Review collected by and hosted on G2.com.

There is nothing I dislike about doing machine learning in python Review collected by and hosted on G2.com.

Its easy to use. Lots of documentation online. Review collected by and hosted on G2.com.

Currently, nothing. I prefer it to Matlab or R. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Getting started can be difficult! Tutorials can be hard to find, especially if you aren't used to using open-source languages like python. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

Sometimes because of data Python IDE gets hanged. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.

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. Review collected by and hosted on G2.com.