Machine Learning With Python

Machine Learning With Python - build predicti… | Awesome Repos

Features

Python Machine Learning Libraries - Provides a comprehensive collection of machine learning algorithms and data science tools implemented in Python.
Scikit-Learn Implementations - Provides a comprehensive collection of common supervised and unsupervised machine learning algorithms implemented via the Scikit-Learn API.
Clustering Algorithms - Implements various unsupervised grouping techniques, including k-means, to identify segments within datasets.
Predictive Machine Learning Analytics - Executes machine learning algorithms to generate predictions from historical data patterns.
Predictive Model Development - Implements machine learning algorithms in Python to design, train, and test predictive models.
Predictive Modeling - Provides a toolkit for applying mathematical algorithms to datasets to predict future outcomes or classify data.
Data Science Toolkits - Provides a collection of scripts using Pandas and NumPy for cleaning, preprocessing, and analyzing complex datasets.
Tabular Data Manipulations - Utilizes Pandas to structure raw datasets into tabular dataframes for efficient cleaning and preprocessing.
General Data Clustering - Groups similar data points using clustering techniques to identify hidden patterns and segments.
Vectorized Data Processing - Uses NumPy vectorized operations on contiguous memory arrays to ensure high computational efficiency for mathematical operations.
Exploratory Data Analysis - Provides tools for loading, cleaning, and visualizing datasets to understand their structure before modeling.
Hyperparameter Tuning - Implements systematic hyperparameter adjustment through repeated training cycles to refine model accuracy.
Statistical Analysis - Employs descriptive and inferential statistics, including Bayesian modeling, to interpret complex datasets.
Matplotlib - Generates static two-dimensional charts and graphs to represent data distributions and classifier boundaries using Matplotlib.
Bayesian Statistical Modeling - Applies Bayesian modeling and causality tests to extract insights and identify relationships in complex datasets.
Statistical Analysis Libraries - Ships a framework for applying Bayesian modeling and causality tests to extract insights from complex datasets.
Data Trend Visualizations - Creates graphical representations of datasets and classifiers to identify and interpret data trends.
Machine Learning - Jupyter notebooks for machine learning algorithms.

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susanli2016Machine-Learning-with-Python

Features

Open-source alternatives to Machine Learning With Python

rapidsai/cuml

mrdbourke/zero-to-mastery-ml

JWarmenhoven/ISLR-python

haifengl/smile

Star history

Open-source alternatives to Machine Learning With Python

rapidsai/cuml

mrdbourke/zero-to-mastery-ml

JWarmenhoven/ISLR-python

haifengl/smile