Machine Learning Libraries every beginner should Learn in 2023.
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Starting to learn machine learning can be a confusing process for any newbie especially if you’re still grappling with some concepts in Python. In recent times we have witnessed a rapid growth in the usage of Python and its huge ecosystem of tools. Therefore it is typical for any beginner to spend some time trying to figure out which library or framework to get started with.
In this article, I’ll be sharing a brief overview of the most used and perhaps the very basic tools in machine learning and what you can achieve with them. Even though Python is one of the easiest languages out there, its ecosystem of tools is expansive and could take you a few weeks before figuring out what is used for what.
R, a close competitor, has been there for quite some time now, with the support of over 13,000 packages available in the CRAN, it is a perfect tool for optimized statistical analysis and data analysis. However, unlike Python, R is built mainly for statisticians with a solid grasp of statistical concepts. Therefore its learning curve is a little bit steeper compared to Python.
For this reason, Python remains the best choice for any newbie in the field of machine learning. Here are some of its tools that you may want to learn to help you get started.
Scikit is one of the most used libraries in the field of machine learning, and one of the most enriched tools offering numerous functionalities in classification, regression, dimensionality reduction, and clustering.
It’s built on top of Scipy but also heavily relies on Numpy, Matplotlib and that’s why it would be wise if you consider looking at these three before embarking on studying Scikit.
Here are some of the common functions that Scikit can help you carry out.
- Regression, such as Linear and Logistic Regression — Scikit has some amazing built-in Algorithms that make your life easier whenever you want to train a model based on some data and make some predictions. All you have to do is know how to apply the fit method
- Build Clustering algorithms, including K-Means and K-Means++ — Scikit allows you to build algorithms to classify items easily using the scikit.cluster module and further do some amazing stuff such as Color Quantization, Special clustering for images using cluster segmentation, k-means clustering, apply ensemble methods such as monotonic analysis of constraints, and Decision tree analysis.
- Perform Data cleaning and Preprocessing using techniques such as Min-Max Normalization — If you’re preparing your data for analysis or training an algorithm then you’ll probably need to pre-process and clean. The scikit.preprocessing module does just that, through a tone of methods such as KBinsDiscretizer. In addition to this using this module, we can also apply scaling, centering, normalization, binarization methods to our data.
- Perform Model selection using cross-validation iterators, evaluate the performance of our estimators and also quantify the quality of our predictions using tools such as model_selection.GridSearchCV, and model_selection.cross_val_score.
- Build Classification algorithms such as the K-Nearest Neighbors
These are just some of the operations that we can perform using scikit. Besides this Scikit has amazing documentation for all its features that you can find here.
Among the most common frameworks for machine learning in Python. It is known for its prowess in data analysis and manipulation of huge data.
It is built on top of Numpy and supports various data formats i.e(time series, labeled data, CSV, tabular data, and any other multidimensional data in matrix form).
The fun doesn’t stop here, it is one of the easiest tools to learn and use that has several tools at your disposal to easily import any data format. Here are some of the operations that pandas enables you to perform on your data:
- Data cleansing.
- Data reshaping, pivoting, and normalization.
- Time series analysis and including functions such as frequency generation, lagging, and data shifting.
- You will be able to insert new data, perform some merges and joins and also delete some data.
- Together with another framework known as Numpy that we are gonna look at next, you can easily properly align data to form a data frame.
You wouldn’t want to work with a framework that takes an 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 faster execution is guaranteed.
You can check out the panda’s official website for such and more details.
3.Numpy
Numpy is a scientific computing package that offers the multidimensional array object, and other derived objects such as matrices and other routines for performing various operations on arrays.
Arrays are almost inevitable when working with Python, they are an alternative to lists and are faster when working with them since unlike their counterparts they occupy smaller memory space and are therefore faster to work with.
Apart from giving you the flexibility to work with any dimension of arrays using the ndim module. We can also use Numpy to perform various logical and mathematical operations, Fourier transformation, linear algebra, and other basic statistical operations,
Here are a couple of more staff that you can do with numpy
- You can create 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 rearrangement addition, subtraction and many others 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 into the math module. All you have to do is import the module. Some of these include; trigonometric, Hyperbolic, exponential functions.
MATLAB and S-PLUS are also some of the common software used in Numerical analysis. However, Numpy and Scipy provide an easy set of tools for solving large problems in this area.
4.SCIPY
Both Scipy and Numpy rely on each other since Scipy is built on top of the Numpy extension, they are cross-platform as well and this gives you an edge in learning them. You can study them concurrently.
Numpy is a great tool 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 include:
- Fourier Transformation functions.
- Signal Filtering and processing functions.
- Trigonometric functions
- Exponential functions
- Interpolation techniques.
Scipy is one of the heavyweights when it comes to packages and sub-packages for working with various numerical problems.
Please refer to their official website for more on input and output functions for file handling and multivariate techniques for working with statistical data provided by Scipy and how to use them.
One of the latest entries into the field of machine learning developed by Google primarily for working with Neural networks. There has been a recent buzz around deep learning and artificial neural networks, a trend that has seen the rise in the usage and growth in popularity of Tensor flow. Unlike the usual machine learning algorithms, neural networks allow us to work with more complicated and unstructured data such as images and audio files
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 pattern recognition.
One of the most important features is its ability to work with APIs such as 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 comes 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 your system by first installing that latest Python 3 version and then running the script tags provided on the TensorFlow official page or via Anaconda.
For more such details visit this link.
You may also want to check out Matplotlib, which offers some amazing tools for plotting and visualization, and Pytorch which is an optimized tensor library for deep learning using GPUs and CPUs.
