Python, because of its simplicity, readability, and versatility, has become one of the most popular programming languages for Machine Learning (ML). The power of python, however, lies in the vast array of libraries available for a variety of purposes, ranging from data manipulation to modelling and visualisation.
As a Machine Learning enthusiast, it’s essential to master the most commonly used Python libraries to improve your ML skills and build robust models. In this blog, we’ll go over the key Python/Machine Learning libraries you should know and provide resources to get you started.
Contents
1. NumPy for python
NumPy (Numerical Python) is one of the most popular libraries used for scientific computing in Python. It can handle multidimensional arrays, linear algebra, and mathematical operations. It is a freely available open-source library that is widely used in data science, scientific research, and engineering.
NumPy’s significance in data science stems from its ability to perform efficient numerical computations and data manipulation. It enables users to easily perform complex mathematical operations on large datasets. It’s multidimensional array is a popular choice for scientific computing and data analysis because it is flexible and efficient.
One of the reasons for NumPy’s popularity in data science is its ability to serve as a foundation for other popular Python data science libraries like Pandas, Matplotlib, and Scikit-Learn. These libraries use NumPy to perform efficient data manipulation and mathematical operations. Furthermore, many Machine Learning algorithms use NumPy arrays, making it an essential tool for developing and implementing Machine Learning models.
Finally, NumPy is an important data science library because it provides a powerful array and mathematical operations that allow for efficient data manipulation, computation, and analysis. It is a must-have tool for anyone working with Python data, whether you are a data scientist, engineer, or researcher.
Now let’s see some resources that you can use to learn this library.
Resources
Documentation
Video Tutorial
Python NumPy Tutorial for Beginners | freeCodeCamp | YouTube
2. Pandas Python Library
Pandas is a popular Python library used in data science for data manipulation and analysis. It provides Python with high-performance, user-friendly data structures and data analysis tools.
Pandas provides two main data structures – Series and DataFrame – that make working with messy, real-world data simple. It offers a wide range of functions and methods for data cleaning, transformation, and analysis, making it an essential tool for data science projects involving these tasks.
Using Pandas, you can do things like remove missing values, fill in missing values, handle duplicates, reshape data, merge and join data, create new variables, aggregate data, compute summary statistics, and visualise data.
It is a highly adaptable Python library that can be integrated with other Python libraries such as NumPy, Matplotlib, and Scikit-learn, making it an invaluable tool for data scientists.
Furthermore, Pandas is a highly sought-after skill in the field of data science. Python and Pandas are used by many companies for data science projects, making it a valuable skill for anyone interested in a career in data science. Learning Pandas can give you a competitive advantage in the job market and open up new possibilities for data analysis and visualisation.
Now let’s see some resources that you can use to learn this library.
Resources
Documentation
Video tutorial
Pandas Tutorials by Corey Schafer
3. Matplotlib for python
Matplotlib is a well-known open-source Python library that is used to generate high-quality 2D and 3D plots and visualisations. It is widely used in data science for data visualisation because it offers a wide range of customizable visualisation options.
Matplotlib provides numerous visualisation tools, including line plots, scatter plots, bar charts, histograms, pie charts, and many others. It also includes advanced features like annotations, subplots, and animations, making it a highly versatile data visualisation tool.
The library is built on NumPy and integrates with other data science libraries such as Pandas and Scikit-Learn. This integration enables data scientists to create visualisations from their data quickly and efficiently, as well as gain insights into complex datasets.
Matplotlib is a useful tool in data science because it allows complex data to be presented in a clear and concise manner, making it easier for decision-makers to interpret and understand the data. Effective data visualisation with Matplotlib can aid in the identification of patterns, trends, and outliers in data, resulting in more informed decision-making.
Now let’s see some resources that you can use to learn this library.
Resources
Documentation
Video Tutorials
Matplotlib Tutorials by Corey Schafer
4. Scikit-Learn Python Library
Scikit-Learn, also known as sklearn, is a well-known open-source Python machine learning library. It offers a diverse set of machine learning algorithms, tools, and utilities for tasks like classification, regression, clustering, and dimensionality reduction.
Scikit-Learn is built on top of other Python scientific computing libraries like NumPy, SciPy, and Matplotlib, making it a powerful and versatile tool for data scientists and machine learning practitioners. It is intended to be simple to use, with a consistent interface across different algorithms that makes switching between methods and models simple.
The following are some of the most popular machine learning algorithms and techniques available in Scikit-Learn:
- Linear regression and classification models
- Random forests and decision trees
- Vector support machines (SVMs)
- Algorithms for gradient boosting
- K-closest neighbours (KNN)
- Deep learning models and neural networks
- Principal component analysis (PCA) and other techniques for dimensionality reduction
- Algorithms for clustering, such as k-means and hierarchical clustering
In addition to the algorithms, Scikit-Learn also includes tools for data preprocessing, feature extraction, model selection, and evaluation. It also includes utilities for manipulating text, image, and time-series data.
It is widely used in industry and academia, and new features and improvements are constantly added. Learning this would definitely make your Data Science portfolio look brilliant.
Now let’s see some resources that you can use to learn this library.
Resources
Documentation
Video Tutorial
Scikit-Learn Python Tutorial | Machine Learning with Scikit-learn by ProgrammingKnowledge
5. PyTorch Python Library
PyTorch is an open-source Python machine learning framework used for developing and training neural networks in AI and deep learning. Its dynamic computational graph feature allows for automatic differentiation, which simplifies gradient computation for optimization algorithms.
PyTorch has a straightforward interface for creating and training neural networks, and it supports both CPU and GPU computing, making it simple to scale models for large datasets. Its versatility and ease of use make it popular among researchers and developers, and a thriving developer community ensures that it remains a cutting-edge tool for AI research and applications.
PyTorch is used by industry leaders such as Facebook in a variety of applications such as computer vision, natural language processing, and speech recognition.
Now let’s see some resources that you can use to learn this library.
Resources
Documentation
Video Tutorial
PyTorch 101: An Applied Tutorial by Abhishek Thakur
6 .TensorFlow for python
TensorFlow is an open-source machine learning framework developed by Google. It is widely used for developing and training neural networks in the fields of artificial intelligence and deep learning. TensorFlow includes a number of tools and utilities for developing and deploying machine learning models, such as pre-built models, transfer learning, and support for CPU and GPU computing.
TensorFlow’s scalability is one of its key advantages, making it suitable for large-scale applications. TensorFlow is well-known for its ability to distribute computation across multiple devices and machines, allowing for the quick and efficient training of complex neural networks.
TensorFlow also includes a high-level interface for developing machine learning models, enabling rapid experimentation and model development. It is popular among researchers and developers due to its flexibility and ease of use, and a large community contributes to its development and support.
Overall, TensorFlow is a powerful and flexible machine learning framework, widely used in AI and deep learning applications, and its scalability, flexibility, and ease of use make it a popular choice among researchers and developers.
Now let’s see some resources that you can use to learn this library.
Resources
Documentation
Video Tutorials
Deep Learning With Tensorflow 2.0, Keras and Python by codebasics
7. OpenCV python library
OpenCV is a free and open-source computer vision library that can be used to create applications like object detection, face recognition, and image segmentation.
It includes image filtering, edge detection, and feature detection tools and utilities for processing and analysing images and videos. It also comes with a machine learning library for building machine learning models for computer vision tasks.
OpenCV’s compatibility with multiple programming languages, including C++, Python, and Java, as well as support for multiple platforms, including Windows, Linux, and macOS, is one of its strengths. It is widely used in robotics, surveillance, and medical imaging applications.
Overall, OpenCV is a powerful and flexible computer vision library with a diverse set of features and tools, making it a popular choice among developers working in computer vision and image processing.
Let’s see some resources that you can use to learn OpenCv library.
Resources
Documentation
Video Tutorial
OpenCV Course – Full Tutorial with Python by freeCodeCamp
8. NLTK python library
The Natural Language Toolkit (NLTK) is a popular open-source library used in data science for processing human language data. It offers a set of natural language processing (NLP) tools and resources, such as tokenization, stemming, part-of-speech tagging, and sentiment analysis.
Text analysis and machine learning applications that require language understanding, such as chatbots, sentiment analysis, and information retrieval, benefit greatly from NLTK. NLTK’s extensive resource set enables data scientists to efficiently process and analyse large amounts of text data.
NLTK also has a large contributor community that updates and improves its resources on a regular basis, ensuring that it remains a cutting-edge tool for NLP research and applications.
Let’s see some resources that you can use to learn NLTK library.
Resources
Documentation
Natural Language Toolkit Documentation
Video Tutorial
NLTK with Python 3 for Natural Language Processing by sentdex
9. HuggingFace for python
HuggingFace is an open-source machine learning library focused on natural language processing (NLP). It provides pre-trained models, tokenizers, and transformers, with a special emphasis on transformer models such as BERT and GPT-2, which have demonstrated significant improvements in NLP tasks.
HuggingFace has an easy-to-use API and integrations with other popular machine learning frameworks like PyTorch and TensorFlow, making it simple for developers to experiment and build models. It has a large and active developer community that contributes to the development and support of its resources.
HuggingFace has been used to create a variety of natural language processing (NLP) applications, such as chatbots, sentiment analysis tools, and translation models. HuggingFace is a popular choice among NLP developers due to its ease of use, powerful tools, and low cost.
Now let’s see some resources that you can use to learn HuggingFace library.
Resources
Documentation
Video Tutorial
Hugging Face Course by HuggingFace
Conclusion
In conclusion, mastering key Python and machine learning libraries can boost productivity and enable developers to create advanced data analysis, machine learning, and natural language processing applications. NumPy, Pandas, Matplotlib, Scikit-Learn, PyTorch, TensorFlow, OpenCV, NLTK, and HuggingFace are among these libraries.
NumPy offers numerical computing capabilities for array manipulation and mathematical operations, whereas Pandas is a robust data manipulation and analysis library. Users can create high-quality charts and graphs with Matplotlib, and machine learning algorithms rely on Scikit-Learn.
Deep learning libraries such as PyTorch and TensorFlow are popular, while OpenCV provides essential functions for image and video processing. HuggingFace provides state-of-the-art natural language processing models and tools, while NLTK enables natural language processing tasks such as sentiment analysis and language modelling.
Overall, these libraries make Python a versatile and powerful data science and machine learning tool. And learning these would make a master in Data Science.
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