In this article, We will explore Python Machine Learning Library for Data Science. These Libraries may help you to design powerful Machine Learning Applications in python. These Machine Learning Libraries in Python are highly performance-centered. You can directly import in your application and feel the magic of AI. Also, If you need a background overview of machine learning you can refer to our article What is Machine Learning? Machine Learning Libraries can be classified into various buckets. This classification helps us to index them properly onto the mind.
Python Machine Learning Library ( Traditional Algorithms)-
Firstly, Here we will consider those Python machine Learning Libraries which provide the implementation of Machine Learning Algorithms like classification (SVM, Random Forest, Decision Tree, etc), Clustering (K-Mean, etc ), etc. These Libraries solve all the problems of machine learning efficiently except neural networks. Even a few of them also cover the neural Network to some extent. But these are not recommended for the neural networks. Deep Learning python Libraries are more prone to it. Here is the list of these Python Machine Learning Libraries –
1. SciKit-learn –
SciKit-learn python API is one of the most popular Python Machine Learning Library. It is too popular because It supports and compatible with most the Python frameworks like NumPy, SciPy, and Matplotlib. These three libraries are most important when you are dealing with data science / Machine Learning /AI. This integration gives quite a familiar feeling to the developer writing Machine Learning Code. This library covers almost everything which a Data Scientist requires.
2. Statsmodels –
This python machine learning package provides the best implementation of Statistics Algorithms. Even you will get more coverage than Scikit Learn( Above mentioned ) from the Statistics Machine Learning Area. Most precisely It is better for Regression and Time Series.
3. XGBoost –
optimized distributed gradient boosting library with multiprogramming Language compatibility like Python, Java, Julia, C++, etc.
4. LightGBM-
Again a Gradient Boosting Framework for the Tree base python machine learning package. Well, it also supports various advance distributed ecosystems and frameworks. This Python package for Machine Learning also supports GPU for high performance. Not only LightGBM saves time by using GPU but It is quite a memory efficient as well.
5.CatBoost –
This Library is also similar to LightGBM and xgboost. Still, CatBoost has its advantages. This python machine learning library is for high performance. It also deals with categorical data most efficiently CatBoost Python package for Machine Learning, is also GPU processing compatible.
Deep Learning Python Libraries –
Here is the complete list with detail for what deep learning libraries do most data scientists use-
6.TensorFlow-
Let me introduce the best deep learning library in python TensorFlow. Let me tell you an interesting fact about it. There was a time when Google Inc., were busy in developing Google Brain. The Team of Google realized the need for a complete python machine learning library on neural networks. This is how TensorFlow born. Anyways let’s move towards its features It has flexible Architecture. So you can deploy it on a distributed Architecture system on parallel processing as well as a single CPU system. The major part of TensorFlow is on C++ with upper binding on Python.
7, Keras-
Keras is the best deep learning library in python for beginners. It provides a very syntax friendly ecosystem. LSTM, CNN, ANN, or any other kind of complex neural network is a few lines game in Keras. In the current time, deep learning is one of the most complex technology but Keras made it so easy for us. Even the lastest TensorFlow 2.0 is completely Keras.
8. PyTorch –
PyTorch is a Facebook research Team product. This provides the feature of distributive training between nodes. It is also best for research and production. PyTorch Machine Learning Library has tremendous developer community backing. Hence If you stuck somewhere, You will so many hands for your help. Similar to Other Python Deep Learning Libraries, It has cloud support as well.
9. Theano-
Theano is another big name in the world of Python data science. It is quite similar to SciKit Machine Learning Library. It has also built over NumPy. This gives massive control over Mathematics expression. If we talk about data structure handling, it has awesome features . Especially in Multi-Dimensional array, It is far better than other Machine Learning Library. Installation is quite easier because of clear GPU ARCHITECTURE.
For more understanding in Theano (Python Machine Learning Library), You can refer to the Github repo of Theano. The developer uses Theano for Deep Learning Application/Model.
10. Caffe –
Caffe is earlier made for Image classification but later on, it is extended for other kinds of neural networks Like LSTM, etc. There are four pillars of Caffe deep learning library – Expressive architecture, Extensible code, Speed, and Community for support.
11. Apache MXNet –
Just like Caffe, Apache MXNet is great for Image Processing. It’s CNN neural Network Implementation is awesome. Currently, MXNet has 8 programming language support. It has a distributive training feature as well. Gluon is also a deep learning library that is based on the Apache MNNet.
12. Pylearn 2 –
It is just a wrapper of Theano. I am going to tell you a beautiful use case of this Machine Learning Library. Whenever you do not want to customize too much in the existing model code. You just want to use existing functionality under the existing API. Pylearn 2 will be the best solution for you. You can customize the code but there will be limitations. One more thing, I would Like to add is, ” Starter version of Pylearn 2 has few bugs but so many bugs have been resolved by great community support”. But still, Be careful to use it.
If you need more information over Pylearn 2 and you want to import it . just Click on Pylearn2. This Python machine learning library is mostly useful in Deep Learning.
the above mention best Deep learning packages are really helpful for AI developers and data scientists. Developing neural networks and deep learning algorithms fro scratch is too difficult. But these python deep learning packages make these difficult process so easy. These are also known as python neural network library.
NLP Machine Learning Libraries-
Machine Learning Libraries have so many use cases into different streams like computer vision, recommendation engines, etc. Natural Language Processing is one of them. Therefore, Here is the list of NLP Machine Learning Libraries –
13. NLTK
14. Spacy
15. Standford CoreNLP
16. Gensim
17 TextBlob
18 Quepy
19 PyNLPl
20 pattern
21 Vocabulary
Here is the complete article for detail on Python NLP Libraries. This will help you in identifying which NLP library suits you. The chatbot is another application of NLP. Here are few chatbot libraries listing-
Python Computer Vision Libraries –
Image Processing and Intelligent camera APPs are in Trend these days. This all is only possible with magical python computer vision libraries. Here is the list of these python libraries for image processing-
22. OpenCV
23. Scikit-image
24. SimplelTK
25. Pillow
26. SimpleCV
27. OpenFace
28. PyTorchCV
29. face_recognition
30. dockerface
31. Detectron
32. albumentations
33. pytessarct
34. imutils
Well, If you need to know more in detail about these image processing libraries, Please go through our complete article – Best Image Processing Library in Python.
Python Libraries for Data Analysis In Machine Learning –
Actually when this article is complete galaxy for a data scientist. And you know very well that the Top Data science libraries list is not complete without these Python Data Processing libraries. Most importantly these data science libraries are further classified.
Numerical Data processing libraries in python –
35. Numpy
36. Pandas
37. Scipy
Python Library For Web Scraping
python data mining library is so important in the overall data science process. Although here we are only mentioning a few of the most popular essential python packages for web scraping.
38. Scrapy
39. BeautifulSoup
Others Python Library for different data format-
40. Python Libraries for Audio data processing
41.Python PDF libraries
42. Python data validation libraries
43. Python Libraries for Operational Research
python data visualization libraries –
44. Matplotlib
45. Plotly
46. Seaborn
47. ggplot
48. Bokeh
49. pygal
50. geoplotlib
51. Gleam
52. missingno
53. Leather
Here is the complete article to assist you in python data visualization libraries.
Python Reinforcement Learning Libraries-
Reinforcement learning is growing incredibly in the AI era. A self-driving car, Interactive robotics are enough examples to demonstrate it. Therefore, Here is the complete listing of reinforcement learning python packages.
54. Pyqlearning
55. KerasRL
56. Tensorforce
57. MAME RL
58. MushroomRL
59. ChainerRL
60. RL_Coach
Others Machine learning libraries (Bonus)-
Apart from the above-mentioned libraries, There so many other machine learning libraries in python. I am not going to deep in each I will only list the name of them. You can click over their name and can reach their official website. These above written was primarily used machine learning libraries in python. You can do most of the tasks ( Machine Learning ) by using one of them. Still, this basket is important where you get other Machine Learning frameworks. Therefore the list is here –
AutoML frameworks for Machine Learning –
61. H2O AutoML
62. Auto-Keras
63. TPOT
64. MLBox
65. Auto-SKLearn
66. TransmogrifAI
What Else are required?
on the other hand, Most of these Machine Learning Libraries are in Python. Therefore, I will suggest you take an overview of python. If you want to quickly revise or learn python essentials. You can refer to our article Learn Python essentials in 5 Minutes.
Still, If you have doubts in your mind why we should use python for data analysis. The article complete overview of python for data analysis will clear all your queries. In fact along with python, what other skills are required to become a full-stack Data Scientist are also mentioned in our article How to Become a Data Scientist – complete Guide.
Likewise Python, There are so many tremendous Machine Learning Libraries in java and other programming languages. Some of us call these Machine Learning library by the name of Machine Learning Framework. In General, both are similar but in deep context, there are so many differences. I will not speak much about it in this post except,” when the flow of control added with library then it becomes a framework “.
Conclusion-
In other words, I think I have mentioned most of the important and useful Machine Learning libraries of python. However, If you need any other information, you can comment or write back to us. Our Team will help you as soon as possible.
Anyways let’s talk about the performance of these python machine learning libraries. If you import these libraries as a black-box model. The complexity of your Application will depend on how you call these API. In case you are customizing these API and using these Machine Learning Libraries as a white box. Then, in that case, These will be two factors how did you call these API and how did you customize the functions written in these libraries. So be smart while using these API. It will be quite risky to use the model blindly for performance-related issues.
If you look around yourself, You will find intelligent people around you. That is not strange. But If I say Machines around you are also intelligent? It’s not funny and I am not joking. The machines are learning and learning speed is quite faster than humans. You are thinking about how is this magic possible Right? Your expressions are indicating me that I am in the Right Direction. Anyways All this is because of the Powerful Machine Learning Libraries. Above all, I hope you have enjoyed this article. Please write your reviews as a comment on it. Please subscribe to us to keep in touch. We will be writing more on python frameworks for AI and ML.
Data Science Learner Team
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