TuriCreate library in Python Last Updated : 16 Jul, 2020 Comments Improve Suggest changes Like Article Like Report TuriCreate(Machine Learning Python Library): Custom Machine Learning models can be made using Turi create very easily. You don't have to be an expert in Machine Learning to use Turi create and it helps in building an add recommendations, object detection, image classification, image similarity or activity classification systems easily. Advantages of TuriCreate: It is easy to use. Flexible to use. Ready to deploy model to ios, MacOs, watchOs and tvOs apps. It one of the crazy features is that it can read the .csv(Comma Separated Values) and use the Machine Learning Model. Supported Platform for TuriCreate: macOS 10.12+ Linux (with glibc 2.12+) Windows 10 (via WSL)=It is little tricky to install it on windows but you can install via WSL(Windows Subsystem Linux) and if does have the same error then you have to upgrade pip by using this command: pip3 install --upgrade pip System Requirements Python 2.7, 3.5 or above (ex. 3.7, 3.8) Python 3.7 macOS only+ x86_64 architecture Using a Conda Environment or virtualenv is highly recommended so that we can have different Python Interpreter on the same Machine. Turicreate uses SFrames for data handling: SFrames means scalable data frame. It is a tabular, column-mutable data frame object that can scale to big data. It's one of the advantages is that is mutable. Code: How to use SFrame for Loading the data: python3 import turicreate as tc from turicreate import SFrame # Let's declare a variable for loading the data data = tc.SFrame("data.csv") # Here data.csv is a file stored in your system # with name = data Data Structure of TuriCreate: SFrame SGraph SArray Comment A abhisheksrivastaviot18 Follow Improve A abhisheksrivastaviot18 Follow Improve Article Tags : Machine Learning python Explore Machine Learning BasicsIntroduction to Machine Learning8 min readTypes of Machine Learning13 min readWhat is Machine Learning Pipeline?7 min readApplications of Machine Learning3 min readPython for Machine LearningMachine Learning with Python Tutorial5 min readNumPy Tutorial - Python Library3 min readPandas Tutorial6 min readData Preprocessing in Python4 min readEDA - Exploratory Data Analysis in Python6 min readFeature EngineeringWhat is Feature Engineering?5 min readIntroduction to Dimensionality Reduction4 min readFeature Selection Techniques in Machine Learning6 min readSupervised LearningSupervised Machine Learning7 min readLinear Regression in Machine learning15+ min readLogistic Regression in Machine Learning11 min readDecision Tree in Machine Learning9 min readRandom Forest Algorithm in Machine Learning5 min readK-Nearest Neighbor(KNN) Algorithm8 min readSupport Vector Machine (SVM) Algorithm9 min readNaive Bayes Classifiers7 min readUnsupervised LearningWhat is Unsupervised Learning5 min readK means Clustering â Introduction6 min readHierarchical Clustering in Machine Learning6 min readDBSCAN Clustering in ML - Density based clustering6 min readApriori Algorithm6 min readFrequent Pattern Growth Algorithm5 min readECLAT Algorithm - ML5 min readPrincipal Component Analysis(PCA)7 min readModel Evaluation and TuningEvaluation Metrics in Machine Learning9 min readRegularization in Machine Learning5 min readCross Validation in Machine Learning5 min readHyperparameter Tuning7 min readML | Underfitting and Overfitting5 min readBias and Variance in Machine Learning10 min readAdvanced TechniquesReinforcement Learning8 min readSemi-Supervised Learning in ML5 min readSelf-Supervised Learning (SSL)6 min readEnsemble Learning8 min readMachine Learning PracticeMachine Learning Interview Questions and Answers15+ min read100+ Machine Learning Projects with Source Code [2025]6 min read Like