TOP MACHINE LEARNING LIBRARIES IN PYTHON TO STICK TO IN 2021.
Article By Isaac Tonyloi.
Starting out to learn machine learning be a confusing process especially if python is the first choice language. By just typing python frameworks on the search bar all you’ll get are thousands of blogs all advocating for different frameworks. You’ll probably spend the next week or so trying to figure out which one to go with and which to leave out.
I’ll be sharing with you my experience when I was starting out and how I finally started writing code, you’ll probably spent a couple of days before you can deploy that first algorithm but it is all worth it in the end.
If you would ask anyone who has been around technology and writing code about the easiest and most friendly programming language, python would most likely be the answer except for a few stray guys with energy and brains above the clouds.
Besides being used in web, applications development, scripting and running in thousands embedded devices it has also gained favour by most of developers working in machine learning and artificial intelligence as well.
R a close competitor that has been there for quite some time now and it also has a huge community around it and some amazing features as well, but for a beginner its learning curve is a little bit steeper especially if you do not have a solid grasp of statistical concepts.
I find it handy for some specific tasks, like when I need an elaborate visualization for a complex problem.
Python has managed to stay as a top choice for me and most data scientists around the globe .One of the main reasons is its extensive provision of and frameworks that make life easier for developers. Lets dive in and look at the top most libraries that are used widely in real industry applications.
In this article I’ll assume that you’ve got all the basics of machine learning and its sub-branches figured
One of the most enriched library when it comes to tools, besides offering numerous functionalities in classification, regression, dimensionality reduction and clustering.
Heavily relies on numpy, matplotlib and Scipy and that's why it would be wise if you consider looking at this three before embarking on studying Scikit.
I’ll highlight some of the features that Scikit offers below;
-Making estimates , model fitting and predictions: Scikit has some amazing built in Algorithms that make you life easier whenever you want to train a model based on some data and make some predictions all you have to do is know ow to apply the fit method.
-Clustering :Scikit allows you to build algorithms to classify items easily uisng the scikit.cluter module and futher do some amzing stuf such as Color Quantization, Special clutering for images using cluster segmentaion, k-means clustering, apply essemble methods such as monotonic analysis of constraints and Decision tree analysis.
-If you’re preparing your data for anlysis then you’ll probably need to pre-process it as explained here. The scikit.preprocessing does just that through a tone on methods such as KBinsDiscretizer and Function Transformer for selecting columns.
-One more feature that just makes Scikit to standout for me is the sklearn.feature_extraction.text module that enable you to work with text documents and allowing you to further perform analysis such as classification, clustering and even more.
This is not it for the Sckit learn Library what you could do with this Library is jus out of imagination its modules span to hundreds and things will only get more interesting if you’re just starting out. More on this in the next post for now just check out their documentation here.
Among the most common frameworks for machine learning in Python. It is known for its prowess in data analysis in real world scenarios working with huge data.
Besides being so efficient in manipulating data as well it gives its users the flexibility to work with different kinds of data sets i.e(time series, labeled data, CSV, tabular data any other multidimensional data in matrix form).
The fun doesn’t stop here when working with pandas you’ve got a number of tools at your disposal to easily import any data format. Well you might be asking what if my data has missing values and it is too random and mixed up ?, with pandas you can do pretty anything that you want with your data here are jus but a few of those:
-Data reshaping and pivoting
-Time series analysis including functions such as frequency generation and lagging and data shifting.
-You will be able to insert new data and delete some data.
-Together with another framework known as Numpy that we are gonna look at next yo can easily properly align data to form a data frame.
You wouldn’t want to work with a framework that takes eternity to load or perform an operation and on that end pandas has got you covered. With much of the low level algorithms already optimized as long as you feed your model with quality data even faster execution is guaranteed.
However it good to note that pandas works with a number of tools that we call dependencies, this includes other frameworks
And they usually important whenever you would wish to call a certain method, but the good news is that python will always raise an error if such a tool is note installed in your system.
You can check out the pandas official website for such and more details.
Arrays are almost inevitable when working with python, they are an alternative to lists and are faster when working with since unlike their counterparts they occupy a smaller memory space and are therefore faster to work with.
Apart from giving you the flexibility to work with any dimension of arrays all you have to do is specify the dimension using the ndim module
Here are a couple of more staff that you can do with numpy
-You can crate an array using the formal lists in python or you can optionally use the array function.
-Ability to print any dimension of arrays which are then displayed in different forms according to their dimension for instance one dimension arrays are displayed as a row while tridimensional are displayed as matrices and so on.
-Perform basic operations such as a rearrangement addition, subtraction and many other refer to the official documentation for more details.
Iteration, slicing, array indexing and advanced indexing techniques are just but a few of the operations you can do with arrays.
You can work with hundreds of mathematical functions built in to the math module all you have to do is import the module some of this are; trigonometric, Hyperbolic, exponential functions.
If you have been through some high level mathematics in college or university you must have been taught numerical analysis at some point . It is a unit commonly taught in engineering and mathematics schools around the world, and it simply entails coming up with algorithms for numerical approximations according to wikipedia.
MATLAB and S-PLUS are some of the common software know for their efficiency In this area. Numpy as together with Scipy also for an alternative for this software they provide an easy set of tools for solving large problems in this area.
Both Scipy and Numpy rely on each other as I had earlier on mentioned, they are cross-platform as well an this gives you an edge in learning them you can study them concurrently.
Numpy is a great tools for working with basic and advanced array functions but Scipy has quite a number of packages for various scientific computations.
-Apart from basic tools Scipy gives you the ability to work with;
- Fourier Transformation using the ffpack package.
-Signal processing using the Signal Package.
-Accessing virtually all Statistical Distributions using the stats package.
-Applying linear algebra techniques using the linalg package.
-Clustering Algorithms and techniques using the cluster Package.
Performing integration in ordinary and partial differential equations using the integrate package.
Besides these you can also access special mathematical functions and even some Physics equations using the special package some of the functions that you can access are;
-Fourier Transformation functions.
-Signal Filtering and processing functions.
Scipy is on of the heavy weights when it come to packages and sub-packages for working with various numerical problems this is just a sketch on the same.
Please refer to their official website for more such as input and out put functions for file handling and multivariate techniques for working with statistical data.
One of the newest arrivals in the field of machine Learning but has already been applauded by most developers around the world. There has been a recent buzz around deep learning and artificial neural networks which are part of machine learning.
Deep learning essentially entails coming up with models that transform raw data through a series of layers to extract desired features. Has proved to be effective in the field of image processing and recognition.
One of the most important feature is its ability to work with APIs such Keras which simply make life easier for developers, apart from that it has an extensive library for working with classification, regression and neural network problems.
In its recent releases TensorFlow come with the flexibility to now be able to run on multiple CPUs and GPUs as well.
Before you can get to learn TensorFlow and other concepts in Deep learning as well it is good that you first get to know the basics of machine learning and all the other libraries that we have previously discussed.
I will not say that it is a difficult Library to work with or learn but it can present numerous challenges to a beginner before you can figure out most concepts.
You can get TensorFlow running on you system by first installing that latest Python 3 version and then running the script tags provided on the TensorFlow official page or via Anaconda.
I’ll probably feel guilty for not adding Matplotlib, Pytorch and some other amazing libraries to this list but matplotlib as its name suggests offer some amazing tools for plotting tools that can enable you communicate with data.
Contributor: Isaac Tonyloi.