machine-learning in Python Pros and Cons

It's available on open-source platform & have a very good community for clarifying all the doubts Review collected by and hosted on G2.com.

Data security is one of the concerns here, but there's a lot of ways to protect it. Review collected by and hosted on G2.com.

It will reduce our work drastically. Example: last month I worked on text summarisation with ml at the end of the project I concluded it reduces 80% of our manual effort. Review collected by and hosted on G2.com.

There is a drawback that it is not accurate sometime Review collected by and hosted on G2.com.

Python is easy to use machine learning programming language which have extensive libraries and packages .Its packages provide efficient visualization to understand .Also nowadays used for cyber security purpose for automated scripting Review collected by and hosted on G2.com.

Syntax is less user friendly comparatively to other machine programming languages like R which makes it less efficient for beginners . Review collected by and hosted on G2.com.

Compatibility with dataframes and online community. Also its easy to learn and easy to use Review collected by and hosted on G2.com.

Less flexibility on changing the algorithm. Modify libraries is not that easy Review collected by and hosted on G2.com.

The fact its operates on a simplified route. Review collected by and hosted on G2.com.

Sometimes the error code can be difficult to identify Review collected by and hosted on G2.com.

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.