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EDUCATION
Get started with a free account. FREE access to all BigML functionality for small datasets or educational purposes. Your tasks may be queued depending on the overall LEARN AND PRACTICE MACHINE LEARNING WITH BIGML BigML is committed to play its part in supporting the mission of democratizing Machine Learning. To meaningfully contribute on this matter, the BigML Team holds Machine Learning crash courses throughout the year, ideal for advanced undergraduates as well as graduate students and industry practitioners seeking a quick, practical, andhands-on
WHIZZML | BIGML.COM
WhizzML is a new domain-specific language for automating Machine Learning workflows, implementing high-level Machine Learning algorithms, and easily sharing them with others. WhizzML offers out-of-the-box scalability, abstracts away the complexity of underlying infrastructure, and helps analysts, developers, and scientists reduce the burden of repetitive and time-consuminganalytics tasks.
GRADING OUR 2021 OSCARS MACHINE LEARNING PREDICTIONS Grading our 2021 Oscars Machine Learning Predictions. After a difficult year in moviemaking and show business in general, the rescheduled 2021 Oscars were presented on Sunday evening marking the end of this year’s award season. Ultimately, history will be a better judge of the winning performances and productions as well asthe hostless
HOW CAN I INTERPRET THE EVALUATION RESULTS FOR MY For classification models, the evaluation results are based on the following metrics or measures: Accuracy: is the number of correct predictions over the number of total instances that have been evaluated.; Precision: the higher this number is, the more you were able to pinpoint all positives correctly.If this is a low score, you predicted a lot of positives where there were none. HOW CAN I CHANGE THE OBJECTIVE FIELD CHOSEN BY DEFAULT In the dataset view, click the configure option menu and select the objective field you need. Another option to change the objective field is, with your dataset open, by clicking the edit icon that appears when mousing over the field you want to change. Then click the highlighted icon and type the name, label and description you like. RIGHT HEART CATHETERIZATION The dataset originally used in Connors et al. (1996) pertains to day 1 of hospitalization, i.e., the "treatment" variable Right Heart Catheterization is whether or not a patient received a RHC (also called the Swan-Ganz catheter) on the first day. The results provided by Connors et al. showed evidence that patients receiving RHC had decreased survival time. USING A CUSTOMIZED COST FUNCTION TO DEAL WITH UNBALANCED The model-options argument is a map that can contain any configuration option we want to set when we create the model. The first attempt creates the model by using default settings, so model-options is just an empty map. This gives us the baseline for the behavior of models with raw unbalanced data. Then we evaluate how our models performusing
WHAT IS THE DIFFERENCE BETWEEN LIFT AND LEVERAGE? The only difference is that lift computes the ratio of both factors (support (A→C)/ (coverage (A)*coverage (C))) and leverage computes the difference (support (A→C)- (coverage (A)*coverage (C))). The implications are that lift may find very strong associations for less frequent items, while leverage tends to prioritize items with higher PREDICTING AIR POLLUTION IN MADRID BIGML.COMGETTING STARTEDPRICINGFEATURESRELEASESWHAT'S NEWBIGML FOREDUCATION
Get started with a free account. FREE access to all BigML functionality for small datasets or educational purposes. Your tasks may be queued depending on the overall LEARN AND PRACTICE MACHINE LEARNING WITH BIGML BigML is committed to play its part in supporting the mission of democratizing Machine Learning. To meaningfully contribute on this matter, the BigML Team holds Machine Learning crash courses throughout the year, ideal for advanced undergraduates as well as graduate students and industry practitioners seeking a quick, practical, andhands-on
WHIZZML | BIGML.COM
WhizzML is a new domain-specific language for automating Machine Learning workflows, implementing high-level Machine Learning algorithms, and easily sharing them with others. WhizzML offers out-of-the-box scalability, abstracts away the complexity of underlying infrastructure, and helps analysts, developers, and scientists reduce the burden of repetitive and time-consuminganalytics tasks.
GRADING OUR 2021 OSCARS MACHINE LEARNING PREDICTIONS Grading our 2021 Oscars Machine Learning Predictions. After a difficult year in moviemaking and show business in general, the rescheduled 2021 Oscars were presented on Sunday evening marking the end of this year’s award season. Ultimately, history will be a better judge of the winning performances and productions as well asthe hostless
HOW CAN I INTERPRET THE EVALUATION RESULTS FOR MY For classification models, the evaluation results are based on the following metrics or measures: Accuracy: is the number of correct predictions over the number of total instances that have been evaluated.; Precision: the higher this number is, the more you were able to pinpoint all positives correctly.If this is a low score, you predicted a lot of positives where there were none. HOW CAN I CHANGE THE OBJECTIVE FIELD CHOSEN BY DEFAULT In the dataset view, click the configure option menu and select the objective field you need. Another option to change the objective field is, with your dataset open, by clicking the edit icon that appears when mousing over the field you want to change. Then click the highlighted icon and type the name, label and description you like. RIGHT HEART CATHETERIZATION The dataset originally used in Connors et al. (1996) pertains to day 1 of hospitalization, i.e., the "treatment" variable Right Heart Catheterization is whether or not a patient received a RHC (also called the Swan-Ganz catheter) on the first day. The results provided by Connors et al. showed evidence that patients receiving RHC had decreased survival time. USING A CUSTOMIZED COST FUNCTION TO DEAL WITH UNBALANCED The model-options argument is a map that can contain any configuration option we want to set when we create the model. The first attempt creates the model by using default settings, so model-options is just an empty map. This gives us the baseline for the behavior of models with raw unbalanced data. Then we evaluate how our models performusing
WHAT IS THE DIFFERENCE BETWEEN LIFT AND LEVERAGE? The only difference is that lift computes the ratio of both factors (support (A→C)/ (coverage (A)*coverage (C))) and leverage computes the difference (support (A→C)- (coverage (A)*coverage (C))). The implications are that lift may find very strong associations for less frequent items, while leverage tends to prioritize items with higher PREDICTING AIR POLLUTION IN MADRID LEARN AND PRACTICE MACHINE LEARNING WITH BIGML BigML is committed to play its part in supporting the mission of democratizing Machine Learning. To meaningfully contribute on this matter, the BigML Team holds Machine Learning crash courses throughout the year, ideal for advanced undergraduates as well as graduate students and industry practitioners seeking a quick, practical, andhands-on
MACHINE LEARNING 101 Machine Learning for Everyone. by Vasily Zubarev. A simple introduction for those who want to understand Machine Learning, whether you are a programmer or a manager. Only real-world problems, practical solutions, simple language, and no high-level theorems. OCT31 2016.
CERTIFICATIONS
The breadth of intelligent applications the BigML platform can support spawn many new opportunities for BigML partners to get involved in delivering Machine Learning-based solutions. Our certifications are perfect for software developers, system integrators, technology consulting, and strategic consulting firms to rapidly get up to speed with Machine Learning and the BigML platform as they BIGMLER - THE COMMAND LINE TOOL FOR MACHINE LEARNING BigMLer is a free and open source tool that will help you perform sophisticated Machine Learning workflows by typing just one line in your command prompt. Simple and effective, BigMLer can satisfy requirements for hands-on development and also for perfectionist modeltuning.
EDUCATION VIDEOS
BigML.com is a consumable, programmable, and scalable Machine Learning platform that makes it easy to solve and automate Classification, Regression, Time Series Forecasting, Cluster Analysis, Anomaly Detection, Association Discovery, Topic Modeling, and Principal Component Analysis tasks. BigML is helping thousands of analysts, software developers, and scientists around the world seamlessly DATASET GALLERY: CONSUMER & RETAIL Dataset prepared for Association Discovery between items (products) 3,346,083 orders. from 206,209 different users. 33,819,106 products bought (49,685 different products) Dataset structure: order_id: Order ID. user_id: User ID. order_number: Order number for a EDGEML: MACHINE LEARNING ON LOW-POWER DEVICES Internet of Things (IoT) and Machine Learning are two transformative technological waves that not only improve the efficiency of existing processes or deliver cost savings but, together, they hold the potential to bring a whole new way of operating lean businesses.To boot, this sea change has the power to spark product and service innovations delivering new revenue streams.ANOMALY DETECTION
Anomaly Detection helps identify outliers in your data. The BigML platform provides one of the most effective, state-of-the-art methods to detect unusual patterns that may point out fraud or data quality issues without the need for labeled data. This unsupervised learning technique assigns a score to each instance of your dataset between 0% and 100%, where a score of 60% or above usually DATASETS | BIGML.COM API A dataset is a structured version of a source where each field has been processed and serialized according to its type. The possible field types are numeric, categorical, text , date-time, or items . For each field, you can also get the number of errors that were encountered processing it. DATASET GALLERY: ENERGY, OIL & GAS 1. (global_active_power*1000/60 - sub_metering_1 - sub_metering_2 - sub_metering_3) represents the active energy consumed every minute (in watt hour) in the household by electrical equipment not measured in sub-meterings 1, 2 and 3. 2.The dataset contains some missing values in the measurements (nearly 1,25% of the rows). BigML uses cookies to understand how you use our service and to improve your experience. Learn moreI accept✕
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