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learn-something-every-day

📝 A compilation of everything that I learn; Computer Science, Software Development, Engineering, Math, and Coding in General.

learndatascience

Open Content for self-directed learning in data science

learning-data-mining-with-python

Code repo for Learning Data Mining with Python, published by Packt Publishing

learning-devops

⚡ The process of learning DevOps engineering

lectures-labs

Slides and Jupyter notebooks for the Deep Learning lectures at M2 Data Science Université Paris Saclay

lending-club-loan-data

DATAQUEST October's Monthly Challenge

libefp

Parallel implementation of the Effective Fragment Potential Method

loan-defaulter-prediction-machine-learning

Prediction of loan defaulter based on more than 5L records using Python, Numpy, Pandas and XGBoost

log-synth

Generates more or less realistic log data for testing simple aggregation queries.

logprocessing

Log processing system using Flume and Cassandra

looker_ecommerce_demo

LookML for ecommerce product demonstration

lookerexample

lookml-syntax-highlighter

VS Code syntax highlighting for Looker Model files (.lkml)

lookml_training_ecommerce_events

lstm-stock-prices

This project goal is to demonstrate how to use LSTM networks and apply in some real data.

lynda.com_training

My code for various Lynda.com courses.

m2ifilrouge

m2m-traffic-corridor

Realtime M2M Traffic corridor creation for emergency service

machine-learning

machine-learning-cheat-sheet

Classical equations and diagrams in machine learning

machine-learning-open-source

Monthly Series - Machine Learning Top 10 Open Source Projects

machine-learning-with-python

machine_learning_projects

Python & R Data Science Projects

machine_learning_security

Source code about machine learning and security.

magellan

Geo Spatial Data Analytics on Spark

mammomasses-project

Predict whether a mammogram mass is benign or malignant We'll be using the "mammographic masses" public dataset from the UCI repository (source: https://archive.ics.uci.edu/ml/datasets/Mammographic+Mass) This data contains 961 instances of masses detected in mammograms, and contains the following attributes: 1. BI-RADS assessment: 1 to 5 (ordinal) 2. Age: patient's age in years (integer) 3. Shape: mass shape: round=1 oval=2 lobular=3 irregular=4 (nominal) 4. Margin: mass margin: circumscribed=1 microlobulated=2 obscured=3 ill-defined=4 spiculated=5 (nominal) 5. Density: mass density high=1 iso=2 low=3 fat-containing=4 (ordinal) 6. Severity: benign=0 or malignant=1 (binominal) BI-RADS is an assesment of how confident the severity classification is; it is not a "predictive" attribute and so we will discard it. The age, shape, margin, and density attributes are the features that we will build our model with, and "severity" is the classification we will attempt to predict based on those attributes. Although "shape" and "margin" are nominal data types, which sklearn typically doesn't deal with well, they are close enough to ordinal that we shouldn't just discard them. The "shape" for example is ordered increasingly from round to irregular. A lot of unnecessary anguish and surgery arises from false positives arising from mammogram results. If we can build a better way to interpret them through supervised machine learning, it could improve a lot of lives. we will apply several different supervised machine learning techniques to this data set, and see which one yields the highest accuracy as measured with K-Fold cross validation (K=10). we will apply: * Decision tree * Random forest * KNN * Naive Bayes * SVM * Logistic Regression * And, as a bonus challenge, a neural network using Keras.

market-segmentation-for-bicycle-manufacturers

Applied unsupervised learning technique to group customers that exhibit similar behaviors for further marketing campaigns. Develop a hypothesis for purchasing trends, analyzed purchasing behavior of each segment of to understand true buying habits

mastering-apache-spark-book

Mastering Apache Spark 2

mastering-data-mining-with-python

From the book of the sample title, implementing the solution based on my understanding rather than just copying them 1-to-1

mastering-kafka-streams-and-ksqldb

Code repository for the Mastering Kafka Streams and ksqlDB book