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.
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. Review collected by and hosted on G2.com.
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. Review collected by and hosted on G2.com.
Tensor flow tool for deep learning. This is the best thing I like about python as it offers so much flexibility for deep learning Review collected by and hosted on G2.com.
I find debugging a little tedious sometimes. Review collected by and hosted on G2.com.
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 Review collected by and hosted on G2.com.
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. Review collected by and hosted on G2.com.
Ease of Setup, plethora of options, tutorials, blogs, resources available, Ease of start Review collected by and hosted on G2.com.
Nothing. It is great. Because everything is open source, finding support or help can be a bit tricky for custom problems. Review collected by and hosted on G2.com.
The ease of implementation that python libraries offer and available documentation. Review collected by and hosted on G2.com.
Too many ways to implement the same thing, sometimes ot becomes confusing. Review collected by and hosted on G2.com.