If you want to learn about machine learning or deep learning, you might have considered working with either Scikit Learn or TensorFlow.
Both of these services cover different bases, so you might not know which is best to start with, especially since there is some overlap in what each service offers.
So, which is best to start with? For a quick answer we recommend starting with Scikit Learn and then moving onto TensorFlow once you are accustomed to working with Scikit Learn.
The reason for this is that jumping right into deep learning can be incredibly challenging if you are not accustomed to machine learning, and since Scikit Learn focuses more on basic machine learning, while TensorFlow is more focused on deep learning, this is why Scikit Learn is the best place to start!
Now you know that it is recommended to start with Scikit Learn (unless of course you have some good knowledge on basic machine learning) instead of TensorFlow, we will spend the rest of this article going into detail on what these two services offer, and which is best for you to go with!
So if you want to learn more about Scikit Learn or TensorFlow, but you do not know where to start with getting informed on them, keep reading to get all the basic information on these two services that you will need to get started!
More On Scikit Learn And TensorFlow
One of the most important things to do after cleaning your data is to make a machine learning model to test it out, this is often seen as the most rewarding element of machine learning.
To do this you will need a framework to keep track of the results with the models and to compare them, this is where you will mainly be using Scikit Learn and TensorFlow.
However, while these two services have this in common, they also have plenty of differences which make working with one preferable at different times!
What Is Scikit Learn?
If you have not heard of it, Scikit Learn is a package that is open source and it is used to evaluate as well as create models for machine learning for all different types of Python.
This package lets you define the different algorithms for machine learning and then evaluate them against each other.
There are also different tools included like the one that lets you preprocess the dataset as well.
There are plenty of different machine learning tools in Scikit Learn like the Random Forests, Support Vector Machines, as well as K Means Clustering.
All of these have different uses and contribute to how versatile and useful Scikit Learn is. The main draw for Scikit Learn is the tools for model evaluation as well as selection within the framework.
These let you cross validate as well as perform different hyperparameter searches among models.
If you are unsure whether you are choosing the best model for a job, then Scikit Learn is going to be the best tool for working this out!
What Is TensorFlow?
Like Scikit Learn, TensorFlow is also an open source package that is a framework being maintained by Google.
It is also used similarly for evaluating different models in machine learning as well as prototyping them as well. These have a focus on the neural networks that are integral for working in deep learning.
What makes TensorFlow even more versatile than Scikit Learn is that it is available to work in a wide variety of different data science languages, for example, it works with Python like Scikit Learn, but it also supports Java, JavaScript, C++, Go, as well as Swift.
There is also a thriving community that also supports the service and allows it to work with more obscure data languages as well.
You will find that TensorFlow is most commonly utilised by ML Engineers as well as Data Scientists. The reason for this is how closely this service is associated with neural networks and working with them.
However, while TensorFlow is well known for its work with deep learning, it can still work with any method of machine learning and is optimized for this task.
It does this through the use of different gradients with a common example of this being Boosted Trees.
TensorFlow is also popular for the visualization tools it uses as well like TensorBoard which is used to both compare and track the different models you are working with!
The main appeal you will find for TensorFlow is its optimization tools as well as the speed of the neural networks that are created on it.
It is also capable of running models not just on CPUs, but also GPUs, and TPUs.
What Is The Difference Between These Services?
While both Scikit Learn and TensorFlow are designed to aid developers in making new models and tracking them, they have some key differences.
For example, Scikit Learn is most commonly used for practicing with these models and has a wider scope for practicing, on the other hand, TensorFlow is used mainly for the neural networks linked to deep learning.
The algorithms for machine learning that are used on Scikit Learn are done using a base estimator, you can find this also being used on TensorFlow when looking at the evaluator used to compare models.
While these estimators are similar, you can find that the estimators on Scikit Learn are a little more flexible, whereas the ones used on TensorFlow are a lot more optimized for functioning with neural networks.
This is not to say that there are no neural networks being used on Scikit Learn however, it does not support the more complex neural networks without being adapted.
This is not the case with TensorFlow that is a lot more simple to adapt.
You will find that Scikit Learn will be abstracting details of the machine learning away from the developer, whereas if you are working with TensorFlow you will need to be aware of these inner details.
This is why working with Scikit Learn will tend to be a little faster and more efficient, however it is less flexible because of this.
Can You Use These Services Together?
Because you can implement your estimators when working with Scikit Learn, there is nothing that will limit you from using TensorFlow when working in a Scikit Learn framework.
This can be used to compare the TensorFlow model to one in Scikit Learn. Having the flexibility to be able to do this is incredibly versatile and useful and is something you will want to be doing if you are working with most models.
This is because you can use this comparison to see if you will need to tweak your work within TensorFlow, or perhaps use a different model on Scikit Learn.
Summary
Hopefully this guide has given you the information to know whether it is best for you to work with Scikit Learn or TensorFlow.
Generally speaking it is better to start with Scikit Learn since it will help you practice fundamental skills you will need to work well with TensorFlow.
However, if you have the knowledge of transitional machine learning already, then you could move straight onto TensorFlow instead.
