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.
This certification course prepares analysts, scientists, and software developers to become BigML Certified Engineers. It 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 modules listed below will consist of 2 sessions each to complete the 8 online classes.
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.
Deepnets: Topologies, Gradient Descent Algorithms, Automatic Network Discovery.
Time Series: Error, Trend, Damped, Seasonality.
Evaluation: How to Properly Evaluate a Predictive Model, Cross-Validation, ROC Spaces and Curves.
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 Modelingg: Topics, Terms, Text analysis.
Anomaly Detection: Forest Size, Constraints, ID Fields.
Domains (bigml.io vs. Private Deployments).
Inputs and outputs.
Resources: Common information, Specifics, Listing and filtering.
Automated feature engineering.
What is it?
Structures for ML tasks.
Cleansing Missing Data, Cleaning Data, Better Data.
Transformations outside BigML: Denormalizing, Aggregating, Pivoting, Time windows, Updates, Streaming Data, Images, Transformations inside BigML.
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.
Field Importance (ensembles).
Advanced Selection: Best-First, Boruta.
Batch Anomaly Score.
Clustered dataset generation.
This certification course prepares BigML Certified Engineers to become BigML Certified Architects. Once you have successfully become a BigML Certified Engineer, you are eligible to enroll into the BigML Certified Architect course. 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 modules listed below will consist of 2 sessions each to complete the 8 online classes.