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amazon-sagemaker-examples

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

awesome-ai-ml-dl

Awesome Artificial Intelligence, Machine Learning and Deep Learning as we learn it. Study notes and a curated list of awesome resources of such topics.

awesome-deep-learning

A curated list of awesome Deep Learning tutorials, projects and communities.

concrete_nlp_tutorial

An NLP workshop about concrete solutions to real problems

deep-learning-books

Books for machine learning, deep learning, math, NLP, CV, RL, etc

ensemble-technique

Using the collected from existing customers, build a model that will help the marketing team identify potential customers who are relatively more likely to subscribe term deposit and thus increase their hit ratio. Resources AvailableThe historical data for this project is available in filehttps://archive.ics.uci.edu/ml/datasets/Bank+MarketingDeliverable –1(Exploratory data quality report reflecting the following)1.Univariate analysisa.Univariate analysis –data types and description of the independent attributes which should include (name, meaning, range of values observed, central values (mean and median), standard deviation and quartiles, analysis of the body of distributions / tails, missing values, outliers.2.Multivariate analysisa.Bi-variate analysis between the predictor variables and target column. Comment on your findings in terms of their relationship and degree of relation if any. Presence of leverage points. Visualize the analysis using boxplots and pair plots, histograms or density curves. Select the most appropriate attributes. 3.Strategies to address the different data challenges such as data pollution, outliers and missing values. Deliverable –2(Prepare the data for analytics)1.Load the data into a data-frame. The data-frame should have data and column description.2.Ensure the attribute types are correct. If not, take appropriate actions.3.Transform the data i.e. scale / normalize if required4.Create the training set and test set in ration of 70:30Deliverable –3(create the ensemble model)1.Write python code using scikitlearn, pandas, numpy and othersin Jupyter notebook to train and test the ensemble model.2.First create a model using standard classification algorithm. Note the model performance.3.Use appropriate algorithms and explain why that algorithm in the comment lines.4.Evaluate the model. Use confusion matrix to evaluate class level metrics i.e..Precision and recall. Also reflect the overall score of the model.5.Advantages and disadvantages of the algorithm.6.Build the ensemble models and compare the results with the base model. Note: Random forest can be used only with Decision trees.

ensemble_amazon

Code to share different ensemble techniques with focus on meta-stacking , using data from Amazon.com - Employee Access Challenge kaggle competition

face_detection

handson-ml

A series of Jupyter notebooks that walk you through the fundamentals of Machine Learning and Deep Learning in python using Scikit-Learn and TensorFlow.

kaggle-past-solutions

A searchable compilation of Kaggle past solutions

kaggle-understanding-clouds

Code for 1st place solution in Understanding Clouds from Satellite Images Challenge.

mlquestions

Machine Learning and Computer Vision Engineer - Technical Interview Questions

ocr-with-tesseract

A comprehensive tutorial for OCR in python using Tesseract-OCR and OpenCV

pytesseract

A Python wrapper for Google Tesseract

python

All Algorithms implemented in Python

pythondatasciencehandbook

Python Data Science Handbook: full text in Jupyter Notebooks

racing-barchart

solution-for-machine-learning-competition-on-hackerearth

stats

Python ML Algorithms

thiru221991

Config files for my GitHub profile.

wtfpython

A collection of surprising Python snippets and lesser-known features.