Learn machine learning the fun way, with Oracle and RedBull Racing

Related tags

Data Analysis redbull-analytics-hol
Overview

Red Bull Racing Analytics Hands-On Labs

License: UPL Quality gate

Introduction

Are you interested in learning machine learning (ML)? How about doing this in the context of the exciting world of F1 racing?! Get your ML skills bootstrapped here with Oracle and Red Bull Racing!

Red Bull F1 Race Car

This tutorial teaches ML analytics with a series of hands-on labs (HOLs) using the Data Science service in Oracle Cloud Infrastructure.

You'll learn how to get data from some public data sources, then how to analyze this data using some of the latest ML techniques. In the process you'll build ML models and test them out in a predictor app.

Getting Started

There is some infrastructure that must be deployed before you can enjoy this tutorial. See the Terraform documentation for more information.

After the OCI infrastructure is deployed, proceed with the beginner's tutorial to start through the ML labs.

Prerequisites

You must have an OCI account. Click here to create a new cloud account.

This solution is designed to work with several OCI services, allowing you to quickly be up-and-running:

There are required OCI resources (see the Terraform documentation for more information) that are needed for this tutorial.

Notes/Issues

None at this time.

URLs

Contributing

This project is open source. Please submit your contributions by forking this repository and submitting a pull request! Oracle appreciates any contributions that are made by the open source community.

License

Copyright (c) 2021 Oracle and/or its affiliates.

Licensed under the Universal Permissive License (UPL), Version 1.0.

See LICENSE for more details.

Comments
  • Refactored Terraform code

    Refactored Terraform code

    • Compatible with ORM, Cloud Shell and Terraform CLI
    • Updated README to include instructions for all three methods
    • Refactored, removing unnecessary resources (Vault, public Subnet, etc.).
    • Added a nerd knob so that it could use an existing Group (rather than create a new one)
    • Fixed ORM RegEx filters to allow dashes (-) and underscores (_), for the names
    opened by timclegg 2
  • Issue with hands on lab guide - launchapp.sh missing

    Issue with hands on lab guide - launchapp.sh missing

    https://github.com/oracle-devrel/redbull-analytics-hol/tree/main/beginners#beginners-hands-on-lab

    In Starting The Web Application it reads:

    cd /home/opc/redbull-analytics-hol/beginners/web ./launchapp.sh start

    However is launchapp.sh is missing, for example

    (redbullenv) cd /home/opc/redbull-analytics-hol/beginners/web (redbullenv) ./launchapp.sh start bash: ./launchapp.sh: No such file or directory

    opened by raekins 1
  • fix: Updating schema.yaml syntax

    fix: Updating schema.yaml syntax

    Making the variable notation follow what the doc syntax shows (https://docs.oracle.com/en-us/iaas/Content/ResourceManager/Concepts/terraformconfigresourcemanager_topic-schema.htm)

    opened by timclegg 1
  • Exploratory Data Analysis Merge Issue

    Exploratory Data Analysis Merge Issue

    Hello I have been encountering an issue while running the lab. The Jupyter notebook 03.f1_analysis_EDA.ipynb has the following issue on cell number 5:

    ValueError Traceback (most recent call last) in ----> 1 df1 = pd.merge(races,results,how='inner',on=['raceId']) 2 df2 = pd.merge(df1,quali,how='inner',on=['raceId','driverId','constructorId']) 3 df3 = pd.merge(df2,drivers,how='inner',on=['driverId']) 4 df4 = pd.merge(df3,constructors,how='inner',on=['constructorId']) 5 df5 = pd.merge(df4,circuit,how='inner',on=['circuitId'])

    ~/redbullenv/lib64/python3.6/site-packages/pandas/core/reshape/merge.py in merge(left, right, how, on, left_on, right_on, left_index, right_index, sort, suffixes, copy, indicator, validate) 85 copy=copy, 86 indicator=indicator, ---> 87 validate=validate, 88 ) 89 return op.get_result()

    ~/redbullenv/lib64/python3.6/site-packages/pandas/core/reshape/merge.py in init(self, left, right, how, on, left_on, right_on, axis, left_index, right_index, sort, suffixes, copy, indicator, validate) 654 # validate the merge keys dtypes. We may need to coerce 655 # to avoid incompatible dtypes --> 656 self._maybe_coerce_merge_keys() 657 658 # If argument passed to validate,

    ~/redbullenv/lib64/python3.6/site-packages/pandas/core/reshape/merge.py in _maybe_coerce_merge_keys(self) 1163 inferred_right in string_types and inferred_left not in string_types 1164 ): -> 1165 raise ValueError(msg) 1166 1167 # datetimelikes must match exactly

    ValueError: You are trying to merge on object and int64 columns. If you wish to proceed you should use pd.concat

    Iā€™m using an oracle automatic deployment provided by oracle as part of their environment. I do not have a lot of experience with Python but one possible ible solution is to read the numeric values form the csv file as integer or float but Iā€™m almost certain the solution might be a little more elaborated than that šŸ˜‰. Anyway thanks for your time. Iā€™m really excited to test your solution and finish the lab. Thanks again.

    opened by yankodavila 2
  • Has the PAR for the stack deploy image expired.

    Has the PAR for the stack deploy image expired.

    Cannot deploy stack as getting PAR expired message.

    2021/11/07 10:50:11[TERRAFORM_CONSOLE] [INFO] Error Message: work request did not succeed, workId: ocid1.coreservicesworkrequest.oc1.eu-amsterdam-1.abqw2ljrwz2n7qqj7ghdwtnlrqol355oumc7a6coushvgdrebskspaewh7ea, entity: image, action: CREATED. Message: Import image not found: PAR is invalid (maybe is expired or deleted), please check.

    PAR in stack file is https://objectstorage.eu-frankfurt-1.oraclecloud.com/p/khhPjc_IMuyBOMfZUcJajIzCpoZ5aC-D7VMCU__GVZRlIQueXLIIcaaqLOZIuT1a/n/emeasespainsandbox/b/publichol/o/redbullhol-20210809-1523

    opened by Mel-A-M 1
Releases(v0.1.8)
Owner
Oracle DevRel
Oracle DevRel
GitHub
Hidden Markov Models in Python, with scikit-learn like API

hmmlearn hmmlearn is a set of algorithms for unsupervised learning and inference of Hidden Markov Models. For supervised learning learning of HMMs and

null 2.7k Jan 3, 2023
Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Theano

PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning focusing on advanced Markov chain Monte Carlo (MCMC) an

PyMC 7.2k Dec 30, 2022
Mortgage-loan-prediction - Show how to perform advanced Analytics and Machine Learning in Python using a full complement of PyData utilities

Mortgage-loan-prediction - Show how to perform advanced Analytics and Machine Learning in Python using a full complement of PyData utilities. This is aimed at those looking to get into the field of Data Science or those who are already in the field and looking to solve a real-world project with python.

Joachim 1 Dec 26, 2021
Used for data processing in machine learning, and help us to construct ML model more easily from scratch

Used for data processing in machine learning, and help us to construct ML model more easily from scratch. Can be used in linear model, logistic regression model, and decision tree.

ShawnWang 0 Jul 5, 2022
Using Data Science with Machine Learning techniques (ETL pipeline and ML pipeline) to classify received messages after disasters.

Using Data Science with Machine Learning techniques (ETL pipeline and ML pipeline) to classify received messages after disasters.

null 1 Feb 11, 2022
Python scripts aim to use a Random Forest machine learning algorithm to predict the water affinity of Metal-Organic Frameworks

The following Python scripts aim to use a Random Forest machine learning algorithm to predict the water affinity of Metal-Organic Frameworks (MOFs). The training set is extracted from the Cambridge Structural Database and the CoRE_MOF 2019 dataset.

null 1 Jan 9, 2022
Additional tools for particle accelerator data analysis and machine information

PyLHC Tools This package is a collection of useful scripts and tools for the Optics Measurements and Corrections group (OMC) at CERN. Documentation Au

PyLHC 3 Apr 13, 2022
Single machine, multiple cards training; mix-precision training; DALI data loader.

Template Script Category Description Category script comparison script train.py, loader.py for single-machine-multiple-cards training train_DP.py, tra

null 2 Jun 27, 2022
TheMachineScraper šŸ±ā€šŸ‘¤ is an Information Grabber built for Machine Analysis

TheMachineScraper ??ā€?? is a tool made purely for analysing machine data for any reason.

doop 5 Dec 1, 2022
Python Library for learning (Structure and Parameter) and inference (Statistical and Causal) in Bayesian Networks.

pgmpy pgmpy is a python library for working with Probabilistic Graphical Models. Documentation and list of algorithms supported is at our official sit

pgmpy 2.2k Dec 25, 2022
A collection of learning outcomes data analysis using Python and SQL, from DQLab.

Data Analyst with PYTHON Data Analyst berperan dalam menghasilkan analisa data serta mempresentasikan insight untuk membantu proses pengambilan keputu

null 6 Oct 11, 2022
Karate Club: An API Oriented Open-source Python Framework for Unsupervised Learning on Graphs (CIKM 2020)

Karate Club is an unsupervised machine learning extension library for NetworkX. Please look at the Documentation, relevant Paper, Promo Video, and Ext

Benedek Rozemberczki 1.8k Jan 9, 2023
Using approximate bayesian posteriors in deep nets for active learning

Bayesian Active Learning (BaaL) BaaL is an active learning library developed at ElementAI. This repository contains techniques and reusable components

ElementAI 687 Dec 25, 2022
A probabilistic programming library for Bayesian deep learning, generative models, based on Tensorflow

ZhuSuan is a Python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and

Tsinghua Machine Learning Group 2.2k Dec 28, 2022
Active Learning demo using two small datasets

ActiveLearningDemo How to run step one put the dataset folder and use command below to split the dataset to the required structure run utils.py For ea

null 3 Nov 10, 2021
An ETL framework + Monitoring UI/API (experimental project for learning purposes)

Fastlane An ETL framework for building pipelines, and Flask based web API/UI for monitoring pipelines. Project structure fastlane |- fastlane: (ETL fr

Dan Katz 2 Jan 6, 2022
BigDL - Evaluate the performance of BigDL (Distributed Deep Learning on Apache Spark) in big data analysis problems

Evaluate the performance of BigDL (Distributed Deep Learning on Apache Spark) in big data analysis problems.

Vo Cong Thanh 1 Jan 6, 2022
PyTorch implementation for NCL (Neighborhood-enrighed Contrastive Learning)

NCL (Neighborhood-enrighed Contrastive Learning) This is the official PyTorch implementation for the paper: Zihan Lin*, Changxin Tian*, Yupeng Hou* Wa

RUCAIBox 73 Jan 3, 2023
[CVPR2022] This repository contains code for the paper "Nested Collaborative Learning for Long-Tailed Visual Recognition", published at CVPR 2022

Nested Collaborative Learning for Long-Tailed Visual Recognition This repository is the official PyTorch implementation of the paper in CVPR 2022: Nes

Jun Li 65 Dec 9, 2022