The breadth of intelligent applications the BigML platform can support spawn many new opportunities for a wide range of professionals that wish to get the most out of their data by getting involved in delivering Machine Learning-based solutions. As such, BigML offers three types of certification programs designed to fit the needs of different professionals: Analysts, Engineers, and Architects. Please see below the differences among the three courses and choose the one that works best for you!
This certification course prepares analysts and business professionals to become BigML Certified Analysts. No prior experience in Machine Learning is required to enroll in this course. By joining this program you will learn how to read your data in order to understand when and how to apply Machine Learning to help your organization. You will also learn how to train your own Machine Learning models from scratch and make predictions with them with no code involved, simply using our intuitive Dashboard. The course consists of 6 online classes of 1.5 hours each. Evaluation will be based on solving a set of theoretical questions and exercises presented during the course.CERTIFICATIONS CALENDAR
In order to be eligible to enroll in the BigML Certified Engineer course you must be familiar with general Machine Learning concepts, the BigML Dashboard and its resources. Also, some programming skills are mandatory in this certification, as you will be asked to understand and generate code in Python and the languages available in the platform: Flatline and WhizzML. You can use our documentation and tutorials as a head start: ML 101, Tutorials, API documentation, and WhizzML. This course is ideal 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.CERTIFICATIONS CALENDAR
Decision Trees: Node threshold, Weights, Statistical Pruning, Modeling Missing Values.
Ensemble Classifiers: Bagging (Sample Rates, Number of Models), Random Decision Forests (Random Candidates), Boosting.
Linear Regression: Field Encodings.
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.
OptiML: How to optimize the process for model selection and parametrization to automatically find the best model for a given dataset.
Fusion: Combination of models, ensembles, linear regressions, logistic regressions, and deepnets to balance out the individual weaknesses of single models.
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.
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 and inside BigML: SQL-style queries, Denormalizing, Aggregating, Pivoting, Time windows, Updates, Streaming Data, Images.
Principal Component Analysis (PCA): Dataset transformation and dimensionality reduction.
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/formulas, 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.
Premature optimization is the root of all evil in Machine Learning as well.
Automating the automatable.