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100days_of_ml_code

ML models

120-data-science-interview-questions

Answers to 120 commonly asked data science interview questions.

applied-deep-learning-with-keras-elearning

Solve complex real-life problems with the simplicity of Keras

bagging_confidence_interval

Applied the concept of bagging on boston housing price prediction dataset creating 30 bags, and trained 30 regression DTs, calculated their train MSE score, thentheir MSE score and oob MSE score on the population data, then calculated a 95% confidence interval (C.I.) on doing the previous task 35 times and storing the mse and oob mse scores and using that as a sample, then made 10 samples from this sample to get the population mean and std on 95% confidence interval using the central limit theorem.

cross_val

Implementing the RandomSearchCV(from scikit-learn) from scratch on KNN using synthetic dataset and also ploting it's decision boundary.

data-science--cheat-sheet

Cheat Sheets

data-science-for-marketing-analytics

Achieve your marketing goals with the data analytics power of Python

data-science-interview-questions-and-answers

Data science interview questions with answers. Not ideally (yet)

data-science-projects-with-python

A Case Study Approach to Successful Data Science Projects Using Python, Pandas, and Scikit-Learn

data_science_using_r

This repository contains all code and data used in Data Science Using R blog series

data_science_using_r-1

datawrangling

Data wrangling in python

deep-learning-for-natural-language-processing

Solve your natural language processing problems with smart deep neural networks

donor_classification

Applying Multinomial Naive Bayes on the 'Donors Choose' and using Bag of Word(BOW) and Term Frequency-Inverse Document Frequency(TFIDF) Vectorisation for text features. To create a programming challenge, displaying the top features as given by feature_log_prob_ parameter of MultinomialNB is coded from scratch.

implement-sgd-classifier-with-logloss-and-l2-regularization-using-sgd-without-using-sklearn

Implement SGD Classifier with Logloss and L2 regularization Using SGD without using sklearn

interactive-data-visualization-with-python

Present your data as an effective and compelling story

ml-from-scratch

Machine Learning From Scratch. Bare bones NumPy implementations of machine learning models and algorithms with a focus on accessibility. Aims to cover everything from data mining to deep learning.

multiplelinearregression

multipletest4spark

MT4S - Multiple Tests 4 Spark - a simple Junit/Scalatest testing framework for Apache Spark

naive-bayes-on-donorschoose-dataset

Applying Naive Bayes on Donors Choose

natural-language-processing-fundamentals

Use Python and NLTK to build out your own text classifiers and solve common NLP problems

online-courses-learning

Contains the online course about Data Science, Machine Learning, Programming Language, Operating System, Mechanial Engineering, Mathematics and Robotics provided by Coursera, Udacity, Linkedin Learning, Udemy and edX.

papers

Summaries of machine learning papers

performance_met

Compute performance metrics for the given Y and Y_score without sklearn.

performancemetrics

Computing Performance Metrics(Precision, Recall, F1score, Accuracy) without sklearn

python-machine-learning-book

The "Python Machine Learning (1st edition)" book code repository and info resource

python-machine-learning-book-2nd-edition

The "Python Machine Learning (2nd edition)" book code repository and info resource

python_tutorial_ques

Complex Python programming practice questions without Scikit_Learn or numpy to hone skills for data science.

random-forest---gbdt-on-decision-trees

Applying RF and GBDT on DonorsChoose Dataset

randomsearchcv-using-k-fold-knn

Implement RandomSearchCV with k fold cross validation on KNN