The Python libraries that I need to learn for competitive programming

There are atleast 32 Python Libraries You need to learn to become a full fledged programmer are as follows.

Python Libraries	Stands for	for Machine Learning (ML)	Types	Used for
Apriori		✔️	ML Algorithm	Mining frequent itemsets and devising association rules from a transactional database.
BeautifulSoup			Data Mining/Scraping	Scraping data from web
Bokeh		✔️	ML Library	Creating interactive visualizations for modern web browsers
Caffe	Convolutional Architecture for Fast Feature Embedding	✔️	ML Framework	Academic research projects
Core Python			Python Library	Includes all the core fucntionalities of python
DTs	Decision Trees	✔️	ML Algorithm	Classification and regression
Eli5		✔️	ML Framework	Inspect ML classifiers and explain their predictions
Hierarchical Clustering		✔️	ML Algorithm	Method to analyze social network data
Keras		✔️	ML Framework	Creating deep models which can be productized on smartphones & distributed training of deep learning models. Used by companies such as Netflix
K-Mean Clustering		✔️	ML Algorithm	Find groups which have not been explicitly labeled in the data
KNN	K-Nearest Neighbors	✔️	ML Algorithm	Classification (most commonly) and regression
LightGBM	Light Gradient Boosting Machine	✔️		Ranking
Linear Regression		✔️	ML Algorithm	To predict the value of a variable based on the value of another variable.
Logistic Regression		✔️	ML Algorithm	Understand the relationship between the dependent variable and one or more independent variables by estimating probabilities using a logistic regression equation.
Matplotlib		✔️	ML Library	Data visualization and graphical plotting library for Python and its numerical extension NumPy
Naive Bayes		✔️	ML Algorithm	Predict the probability of different class based on various attributes.
Numpy	Numerical Python	✔️	Math Calculation	Math Calculation
Pandas	Python Data Analysis Library	✔️	Edit/View Data	Data analysis and associated manipulation of tabular data in Dataframes
Plotly		✔️	Edit/View Data	Create beautiful interactive web-based visualizations on Jupyter Notebook.
PyCaret		✔️	Machine Learning	Automates machine learning workflows
PyTorch		✔️	ML Framework	Applications of research
Random Forest		✔️	Decision tree Algorithms	Classification and regression problems
RegEx	Regular Expression		Python Library	To check if a string contains the specified search pattern.
Sklearn	Scikit-learn	✔️	ML Library	Contains a lot of efficient tools for machine learning and statistical modeling including classification
SciPy	Scientific Python	✔️	Math Calculation	Utility functions for optimization
Scrapy			Data Mining/Scraping	Scrapy is a Python framework for large scale web scraping
Seaborn			Edit/View Data	Making statistical graphics in Python
SQL	Structured Query Language		Edit/View Data	Managing or querying data stored in a relational database management system (RDBMS).
SVM	Support Vector Machine	✔️	ML Algorithm	Supervised and linear Machine Learning algorithm. Solving classification problems
TensorFlow		✔️	Python Library	Fast numerical computing created and released by Google.
Theano			Python Library	Fast numerical computation that can be run on the CPU or GPU.
XGBoost	Extreme Gradient Boosting		Python Library	Provides a wrapper class to allow models to be treated like classifiers or regressors in the scikit-learn framework.