BigML 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 acquire and grow their customer base.
In order to be eligible to enroll into the BigML Certified courses you must show certain level of proficiency in Machine Learning, BigML Dashboard, BigML API, and WhizzML. The following getting started assets will get you up and running in no time: ML 101, Tutorials, API documentation, and WhizzML.
Certified Engineer
This certification track prepares analysts, scientists, and software developers to become BigML Certified Engineers.
The certification process consists of 8 online classes of 1.5 hours each. Evaluation will be based on solving a set of theoretical questions and exercises presented during the course. The courses listed below will consist of 2 sessions each to complete the 8 online classes.
Courses
Advanced Modeling
Objective
- Understand how to parameterize supervised and unsupervised methods to achieve better performance.
- Learn how to compose multiple methods together to better solve modeling problems.
Pre-requisites
- BigML sources and datasets
- Supervised (Model, Ensemble, Logistic Regression) and Unsupervised (Cluster, Anomaly Detection, Association Discovery, and Topic Modeling) Methods.
- 1-click Model, 1-click Ensemble, 1-click Logistic Regression, 1-click Cluster, 1-click Anomaly, 1-click Association, 1-click Topic Model.
- Simple evaluations and metrics.
Syllabus
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Modeling vs. Prediction
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Supervised Learning
Decision Trees: Node threshold, Weights, Statistical Pruning, Modeling Missing Values.
Ensemble Classifiers: Bagging (Sample Rates, Number of Models), Random Decision Forests (Random Candidates), Boosting.
Logistic Regression: L1 Normalization, L2 Normalization , Field Encodings, Scales.
Evaluation: How to Properly Evaluate a Predictive Model, Cross-Validation , ROC Spaces and Curves.
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Unsupervised Learning
Clustering: Number of Clusters, Dealing with Missing Values, Modeling Clusters, Scaling Fields, Weights, Summary Fields, K-means vs. G-means.
Association Discovery: Measures (Support, Confidence, Leverage, Significance Level, Lift), Search Strategies (Confidence, Coverage, Leverage, Lift, Support), Missing Items, Discretization.
Topic Modeling: Topics, Terms, Text analysis.
Anomaly Detection: Forest Size, Constraints, ID Fields.
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Combination and Automation
Stacking.
Advanced API
Objective
- Proficiency in using BigML's API and client-side tools to create ML resources.
- Integration and automation of the workflows needed put a ML solution in production.
Pre-requisites
- Basic knowledge of BigML and its resources (UI-level familiarity is enough).
- Basic programming skills (some examples are in Python, so knowledge of the language will be a plus).
- Familiarity with REST APIs.
Syllabus
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API description
Domains (bigml.io vs. Private Deployments).
Authentication.
Inputs and outputs.
Resources: Common information, Specifics, Listing and filtering.
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First level wrappers
Bindings.
Methods mapping.
Field management.
Local resources.
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Second level wrappers
BigMLer.
Resource management.
Field management.
Workflow automation.
Automated feature engineering.
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Modeling strategies
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Predicting strategies
Advanced Data Transformations
Objective
- Data is typically: scattered, unclean, and imperfect. How to make it ML-Ready.
- Once data is ML-Ready, why/how to make better features.
- Not all features are good. How to choose and what to watch out for.
Pre-requisites
- Advanced Modeling Class.
- Familiarity with: SQL, Python / Pandas, CSV formatting.
Syllabus
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ML-Ready Data
What is it?
Formats.
Structures for ML tasks.
Automating Labeling.
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Data Transformations
Cleansing Missing Data, Cleaning Data, Better Data.
Transformations outside BigML: Denormalizing, Aggregating, Pivoting, Time windows, Updates, Streaming Data, Images, Transformations inside BigML.
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Feature Engineering
Auto Transformations: Date-time parsing, LR/cluster missing, LR/cluster auto-scaling, Bag-of-words (Language, Tokenization, etc).
Manual - Flatline: DSL for feature engineering, Basics (s-expressions, Literals, Counters, Field Values / Properties, Strings, Regex, Operators), Limitations.
Numerics: Discretization, Normalization, Z-score, Built-in math functions, Type-casting, Random, Shocks, Moving averages.
Date-times: UI timestamp, Epoch, Moon phase.
Text: JSON key/val, Topic distributions.
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Feature Selection
Correlations.
Leakage.
Field Importance (ensembles).
Advanced Selection: Best-First, Boruta.
Advanced WhizzML
Objective
- Proficiency in using BigML's DSL language, WhizzML, as a server-side tool to automate ML-workflows in a scalable, replicable and shareable way.
Pre-requisites
- Basic knowledge of BigML and its resources (UI-level familiarity is enough).
- Familiarity with ML-workflows.
- Basic programming skills (knowledge of some language of the LISP-family and/or WhizzML will be a plus).
Syllabus
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WhizzML directives
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Directives mappings
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Simple workflows in WhizzML
Batch Anomaly Score.
Evaluation.
Clustered dataset generation.
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Advanced workflows in WhizzML
Cross-validation.
Covariate shift.
Stacked generalization.
Certifications calendar
Certified Architect
This certification track prepares BigML Certified Engineers to become BigML Certified Architects. Once you've successfully passed the BigML Certified Engineer Exam, you are eligible to enroll into the BigML Certified Architect Courses.
The certification process consists of 8 online classes of 1.5 hours each. Evaluation will be based on solving a set of theoretical questions and exercises presented during the course. The courses listed below will consist of 2 sessions each to complete the 8 online classes.
Courses
Designing Large-Scale Machine Learning Solutions
Measuring the Impact of Machine Learning Solutions
Using Machine Learning to Solve Machine Learning Problems
Lessons Learned Implementing Machine Learning Solutions
