I can provide a general overview of some popular libraries that are commonly used in the field of data science and machine learning.
1. NumPy I have discussed it in my other blog so i wil not go in detail, please check the link below
2.pandas Pandas is a library for data manipulation and analysis that provides tools for handling and manipulating large datasets. I have discussed it in my another blog also where you can refer and go in detail
3. Scikit-learn is another library that is widely used in the field of data science and machine learning. It is a library for building and evaluating machine learning models, and it includes a variety of algorithms for tasks such as classification, regression, clustering, and dimensionality reduction.
4. TensorFlow, is a library for building and training machine learning models, and Keras, which is a high-level library for building and training neural networks.
5.Matplotlib is a library for creating visualizations of data, such as plots, histograms, and scatter plots. It is often used in conjunction with NumPy and pandas.
6.Seaborn is another library for creating data visualizations, and it is built on top of Matplotlib. It provides a higher-level interface for creating more complex and sophisticated plots.
7.Statsmodels is a library for statistical modeling and testing. It includes a wide range of statistical tests and models, such as regression, ANOVA, and time series analysis.
8.NLTK (Natural Language Toolkit) is a library for working with natural language data, such as text. It includes tools for tokenizing, stemming, and lemmatizing text, as well as tools for building and evaluating models for tasks such as language modeling and sentiment analysis.
9.Gensim is a library for topic modeling and document similarity. It includes algorithms for tasks such as Latent Dirichlet Allocation (LDA) and word embeddings.
10.Scipy is a library for scientific computing that includes a wide range of numerical algorithms and functions, such as optimization, integration, interpolation, and signal processing.
11.OpenCV is a library for computer vision that includes a wide range of algorithms and functions for tasks such as image and video processing, object detection and recognition, and machine learning.
12.PyTorch is a library for deep learning and machine learning that provides tools for building and training neural networks. It is designed to be flexible and easy to use, and it includes support for distributed training and hardware acceleration.
13.LightGBM is a library for gradient boosting that is designed to be fast and efficient. It is often used for tasks such as classification and regression.
14.XGBoost is another library for gradient boosting that is widely used for tasks such as classification and regression. It is known for its speed and performance, and it has been used in a number of winning solutions in data science competitions.
15.spaCy is a library for natural language processing that includes tools for tasks such as tokenization, part-of-speech tagging, and dependency parsing.
16.networkx is a library for working with graphs and networks. It includes tools for creating and manipulating graphs, as well as algorithms for tasks such as shortest path finding and community detection.
17.h5py is a library for working with HDF5, a file format for storing large amounts of numerical data. It is often used for storing large datasets that are too large to fit in memory.
18.scikit-optimize is a library for optimization that includes a wide range of optimization algorithms and functions, such as gradient descent and evolutionary algorithms.
19.Dask is a library for distributed computing that provides tools for scaling up data processing and machine learning workloads. It can be used in conjunction with other libraries such as NumPy and pandas.
20.graph-tool is a library for working with graphs and networks that is designed for large-scale graphs. It includes a wide range of algorithms and functions for tasks such as graph layout and visualization, community detection, and graph similarity.
Again, these are just a few examples of the many libraries that are available for data science and machine learning in Python. It is important to keep in mind that there are many other libraries available, and the best choice of library will depend on your specific needs and goals.
I will write more about eack libraries with some examples in my next Blogs Sonn, So keep updated.
— — — — — — — — — — — — — — — — — — — — — — — — — — — — — — — —
＃Python
＃Artificial Intelligence
＃Machine Learning
＃Towards Data Science
＃Ml So Good