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Datasist A Python library for easy data analysis, visualization, exploration and modeling
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Machine Learning Workflow With PythonThis is a comprehensive ML techniques with python: Define the Problem- Specify Inputs & Outputs- Data Collection- Exploratory data analysis -Data Preprocessing- Model Design- Training- Evaluation
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Amazing Feature EngineeringFeature 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.
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AutofeatLinear Prediction Model with Automated Feature Engineering and Selection Capabilities
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Open source demosA collection of demos showcasing automated feature engineering and machine learning in diverse use cases
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Drugs Recommendation Using ReviewsAnalyzing the Drugs Descriptions, conditions, reviews and then recommending it using Deep Learning Models, for each Health Condition of a Patient.
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Kaggle CompetitionsThere are plenty of courses and tutorials that can help you learn machine learning from scratch but here in GitHub, I want to solve some Kaggle competitions as a comprehensive workflow with python packages. After reading, you can use this workflow to solve other real problems and use it as a template.
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FeatexpFeature exploration for supervised learning
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NlpythonThis repository contains the code related to Natural Language Processing using python scripting language. All the codes are related to my book entitled "Python Natural Language Processing"
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Tf2 courseNotebooks for my "Deep Learning with TensorFlow 2 and Keras" course
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PycroscopyScientific analysis of nanoscale materials imaging data
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MainCS579: Online Social Network Analysis at the Illinois Institute of Technology
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Cs231nhomework for CS231n 2017
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Visualizing cnnsUsing Keras and cats to visualize layers from CNNs
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Ipython NotebooksInformal IPython experiments and tutorials. TensorFlow, machine learning/deep learning/RL, NLP applications.
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Pytorch tutorialA set of jupyter notebooks on pytorch functions with examples
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Scipy con 2019Tutorial Sessions for SciPy Con 2019
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Diy AlexaCommand recognition research
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NbashotsNBA shot charts using matplotlib, seaborn, and bokeh.
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GpA tutorial about Gaussian process regression
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Mlmodelsmlmodels : Machine Learning and Deep Learning Model ZOO for Pytorch, Tensorflow, Keras, Gluon models...
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RlossRegularized Losses (rloss) for Weakly-supervised CNN Segmentation
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AnimlReproduction of "Model-Agnostic Meta-Learning" (MAML) and "Reptile".
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Dlfs codeCode for the book Deep Learning From Scratch, from O'Reilly September 2019
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SelfconsistencyCode for the paper: Fighting Fake News: Image Splice Detection via Learned Self-Consistency
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Faster Rcnn tensorflowThis is a tensorflow re-implementation of Faster R-CNN: Towards Real-Time ObjectDetection with Region Proposal Networks.
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Multihead Siamese NetsImplementation of Siamese Neural Networks built upon multihead attention mechanism for text semantic similarity task.
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GatorConda environment and package management extension from within Jupyter
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UnetU-Net Biomedical Image Segmentation
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Deep and machine learning projectsThis Repository contains the list of various Machine and Deep Learning related projects. Related code and data files are available inside this folder. One can go through these projects to implement them in real life for specific use cases.
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Vmls CompanionsThese are companion notebooks written in Julia and Python for: "Introduction to Applied Linear Algebra" by Boyd and Vandenberghe.
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Google2csvGoogle2Csv a simple google scraper that saves the results on a csv/xlsx/jsonl file
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