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基于SVM美女图像分类

-h1

A series of Jupyter notebooks showing how to load well log and petrophysical data in python.

03_python

The note of Python（Numpy/Pandas/Matplotlib...)

05pythonvisualization

Various Scripts for visualization in Python. Matplotlib, GGplot, Seaborn, Bokeh, ScikitImage. Unrelated: Numpy examples also included.

10-steps-to-become-a-data-scientist

📢 Ready to learn or review your knowledge! You will learn 10 skills as data scientist: 📚 Python, Machine Learning, Deep Learning, Data Cleaning, EDA, python packages such as Numpy, Pandas, Seaborn, Matplotlib, Plotly, Tensorfolw, Theano...., Linear Algebra, Big Data, Analysis Tools and solve some real problems such as predict house prices.

100-days-of-ml-code

100-Days-Of-ML-Code中文版

100days

100 days of algorithms

222

用来记一下重点

300days__machinelearningdeeplearning

I am sharing my Journey of 300DaysOfData in Machine Learning and Deep Learning.

50-ggplot-python

abc_svm

Clinical Health Analysis CA

aco

ACO: Ant colony optimization algorithm

ad_examples

A collection of anomaly detection methods (iid/point-based, graph and time series) including active learning for anomaly detection/discovery, bayesian rule-mining, description for diversity/explanation/interpretability. Analysis of incorporating label feedback with ensemble and tree-based detectors. Includes adversarial attacks with Graph Convolutional Network.

additional-topics-statistics-bonus-for-regression-lesson

Box-Cox Transformations, AIC, BIC, and Marginal Plots for Multiple Regression

ailearning

AiLearning: 机器学习 - MachineLearning - ML、深度学习 - DeepLearning - DL、自然语言处理 NLP

albumentations

Fast image augmentation library and an easy-to-use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about the library: https://www.mdpi.com/2078-2489/11/2/125

algorithms_in_ipython_notebooks

A repository with IPython notebooks of algorithms implemented in Python.

allstate_capstone

Allstate Kaggle Competition ML Capstone Project

amazing-feature-engineering

Feature engineering is the process of using domain knowledge to extract features from raw data via data mining techniques. These features can be used to improve the performance of machine learning algorithms. Feature engineering can be considered as applied machine learning itself.

amazon-sagemaker-examples

Example notebooks that show how to apply machine learning, deep learning and reinforcement learning in Amazon SageMaker

an-alternative-approach-to-forecast-the-volatility-of-multiscale-and-high-dimensional-market-data

Traditional methods for volatility forecast of multiscale and high-dimensional data like foreign-exchange and stock market volatility have both advantages and disadvantages which have been identified. In my project, I apply the Support Vector Machine (SVM) as a complimentary volatility method which is capable dealing of such type of data. SVM-based models may extract extra information of time series data and handle the long memory effect very well. Our Support Vector Machine for Regression (SVR) model has better result than the common GARCH (1, 1) model. The predictions are closer to the historical data and the error is lower. In addition, I test different kernels to see the performance difference. For my data, rbf kernel has an overall better performance than linear and polynomial kernels. I conclude that SVM-based model may be applied more frequently in the emerging field of high-frequency finance and in multivariate models for portfolio risk management.

anatomyofmatplotlib

Anatomy of Matplotlib -- tutorial developed for the SciPy conference

ann_cnn_rnn_lstm_optimization_hyperparameter

Machine learning, Deep learning, Data science master

apples-to-oranges

An AI Program that can tell the difference between pictures of apples and oranges using various algorithms

applied-ml

📚 Papers & tech blogs by companies sharing their work on data science & machine learning in production.

arima

Simple python example on how to use ARIMA models to analyze and predict time series.

arima-lstm-hybrid-corrcoef-predict

Applied an ARIMA-LSTM hybrid model to predict future price correlation coefficients of two assets

artificial-intelligence-deep-learning-machine-learning-tutorials

A comprehensive list of Deep Learning / Artificial Intelligence and Machine Learning tutorials - rapidly expanding into areas of AI/Deep Learning / Machine Vision / NLP and industry specific areas such as Climate / Energy, Automotives, Retail, Pharma, Medicine, Healthcare, Policy, Ethics and more.

asset_price_correlations

Visualising correlations between different ETFs using network analytics and Plotly

attack-and-anomaly-detection-in-iot-sensors-in-iot-sites-using-machine-learning-approaches

Attack and Anomaly detection in the Internet of Things (IoT) infrastructure is a rising concern in the domain of IoT. With the increased use of IoT infrastructure in every domain, threats and attacks in these infrastructures are also growing commensurately. Denial of Service, Data Type Probing, Malicious Control, Malicious Operation, Scan, Spying and Wrong Setup are such attacks and anomalies which can cause an IoT system failure. In this paper, performances of several machine learning models have been compared to predict attacks and anomalies on the IoT systems accurately. The machine learning (ML) algorithms that have been used here are Logistic Regression (LR), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), and Artificial Neural Network (ANN). The evaluation metrics used in the comparison of performance are accuracy, precision, recall, f1 score, and area under the Receiver Operating Characteristic Curve. The system obtained 99.4% test accuracy for Decision Tree, Random Forest, and ANN. Though these techniques have the same accuracy, other metrics prove that Random Forest performs comparatively better.