Top 10 Essential Machine Learning Libraries of 2020

In this article I will discuss 10 most used Machine Learning Libraries. we’ll be discussing libraries that can handle most of the machine learning tasks along with their relevant Pros & Cons.

If you are just starting out in Machine Learning / Data Science, I will highly recommend to give a quick glance. Or if you have been in this game for long, comment in the below section which libraries you frequently use/like.

1. NumPy

NumPy stands for Numerical Python. It is one of the most basic (yet advance) Python libraries available for scientific computing and can be used as a multi-dimensional container for data. One can perform Linear Algebra computations which are necessary for Machine learning Algorithms like Linear Regression, Logistic Regression, Na?ve Bayes and so on. It is mostly written in C language (low-level language), due to which it is faster. 

Pros

• NumPy uses less memory.
• Faster as compared to lists in Python.
• Mathematical operations can be performed using Numpy unlike Lists.

Cons

• NumPy arrays are homogeneous contiguous blocks of memory i.e., it will hold only one kind of data.

2. Pandas

Pandas is one of the most important statistical library mainly used in the field of Statistics, Finance, Economics and Data Analysis. It is similar to Excel. It is known for processing large datasets.

Pros

• Pandas creates fast and effective Data frame objects with pre-defined and customized indexing.
• Can be used to manipulate large datasets. It deals with the missing values present in the dataset as well.
• Provides in-built features for creating Excel charts and performing complex data analysis task like data wrangling, data transformation and so on.

Cons

• Pandas does not persist data. 
• Can only handle results that fit in memory, and is easy to fill.

3. Matplotlib

Matplotlib is one of the most popular libraries for data visualization. It provides support for a wide variety of graphs like histogram, bar charts, scatter plot, pie charts and so on. It is basically a two-dimensional graphical library which produces very concise and clear graphs that are important for exploratory analysis. Nowadays I am getting more inclined to Plotly also :)

Pros

• Easy to plot graphs using matplotlib by providing functions for choosing suitable line styles, different font styles, changing and formatting axes.
• Easy to understand these graphs.
• Contains the Pyplot module that provides a basic interface, similar to MATLAB interface.
• Provides Object-Oriented API module that will help in integration of graphs into applications and tools.
• Matplotlib produces research quality graphs as it uses vectors instead of pixels.

Cons

• Graphs in Matplotlib are not interactive.
• Limited variety of visualization.

4. Scikit-learn

One of the most effective library for machine learning, data modelling and model evaluation. It is built on the top of SciPy. It contains lot of functions for the purpose of model creation. Consisting of Supervised and Unsupervised Machine Learning algorithms.

Pros

• Provides a set of standard datasets, to help people to get started machine learning. E.g: Iris dataset.
• It has in-built functions to carry out both supervised and unsupervised learning which includes Clustering, Classification, Regression, Data Mining, Anomaly detection and so on.
• It consists functions for Feature Extraction and Feature Selection, which helps in identifying significant attributes or variables in data.
• It also has functions for Cross-Validation to estimate the performance of the model.
• It integrates really well with NumPy.

Cons

• Sometimes, it becomes really slow, especially during the training of models.
• Less flexible.
• Limited model tuning capabilities for few Machine Learning Model

5. Tensorflow

A Deep Learning maintained by Google. It is a library that is used for building string and precise Neural Networks (algorithms are inspired by the structure of the brain). To represent Tensorflow programs tensor data structure is used. It supports programming languages like C++, Python and R. It can used in Natural Language Processing (NLP), forecasting, text summarization, image/video analytics and handwriting recognition. It is known for faster deployment of algorithms while retaining the same APIs.

Pros

• Tensorflow allows to train multiple neural networks which help to accommodate large datasets.
• It provides functions and methods that provide basic statistical analysis.
• It also provides layered components that perform layer operations on weights and bias.
• Tensorflow also comes with a visualization tool called tensorboard.
• Pre-trained models availability
• Multi-GPU deployment support

Cons

• Can get really slow if you don't know what is happening under the hood.
• Due to its structure, debugging can get difficult.
• Steep learning curve (although it has become better since when I started)

6. Keras

Keras provides full support for creating, analyzing, evaluating and improving neural networks. It is built on top of Theano or Tensorflow libraries which provide additional features to complex and large-scale deep learning models.

Pros

• Keras provide support for building all types of neural networks.
• Lightweight and easy to use.
• It is really straightforward when it comes to building a deep learning model by stacking multiple layers – Keras in a nutshell.
• It has several pre-processed datasets and trained models like MNIST.
• It is easily extensible and provide support to add new modules.
• Fran?ois Chollet is the primary author and maintainer (great guy, follow him on twitter)

Cons

• Errors are difficult to debug.
• It is difficult to customize your layer because Keras already have pre-configured layers.

7. PyTorch

PyTorch is an open-source Python-based scientific computing library, mainly used to implement deep learning techniques and neural networks on large datasets. It competes with Tensorflow. This library is developed by Facebook's AI Research lab (FAIR).

Pros

• It provides easy to use APIs.
• Efficient due to underlying scripting language LuaJIT and C/Cuda implementation.
• Can creates dynamic computation graphs.
• Good documentation and community support.

Cons

• It lacks visualization such as tensorboard in Tensorflow. Therefore, third-party is needed.
• API server needed for production.

8. XGBoost

XGBoost stands for eXtreme Gradient Boosting. It was written in C++. This library was named after XGBoost algorithm. It is an application of gradient boosted decision trees created for speed and performance.

Pros:

• XGBoost has faster execution speed.
• It provides better model performance.
• The core XGBoost algorithm is parallelizable and it can use the power of multi-core computers.
• It can process very large datasets and it can work across a network of datasets.
• It also provides internal parameters to perform cross-validation for performing regularization, cross-validation, handling missing values and so on.

Cons:

• It is computationally expensive.
• It is less interpretable.

9. OpenCV

OpenCV (Open Source Computer Vision) is a library used for computer vision. It supports Python, C++ and Java. In OpenCV, all the images are converted to NumPy arrays as this process will make it easier to integrate with other libraries that uses NumPy.

Pros

• OpenCV is written in C++, making it fast.
• Portable library

Cons

• OpenCV lacks in memory management.

10. NLTK

NLTK (Natural Language Toolkit) is a leading library for building python programs to work with human language data and it also provides easy to use interfaces.

Functions performed using this library are classification, tokenization, parsing and so on. Some of its applications are text processing, recommendation system and sentiment analysis.

There many more libraries which are doing great work if you are looking to work with text data like SpaCy & Gensim.

Pros

• It supports the maximum number of languages as compared to other libraries.

Cons

• Slow speed.
• Can get difficult to use.
• It directly splits the sentences without analyzing the semantic structure of the sentence.

Conclusion

For most of the standard and out-of-the-box stuff Keras stands way ahead. If you are looking to go deeper and build your own layer go for PyTorch or Tensorflow. For other Machine Learning specific tasks like Text analytics there are great libraries like NLTK, or SpaCy.

So make the wise decision based on the current requirement when using the Machine Learning libraries for personal projects or for your Company. I will conclude by quoting Occam's razor principle

"The simplest solution is most likely the right one"