BigML Certified Analyst | Data Preparation for Machine Learning

Duration: Hours

Training Mode: Online

Description

BigML Analyst : we believe that best way to add business value is by showing and not just telling what is possible i.e. Machine Learning techniques.

Objective

–  Understand what Machine Learning is, the many different Machine Learning applications across industries, and what tasks can be performed to solve a given business problem according to the data you have.

–  Learn how to train your own Machine Learning models and make predictions with them using the BigML Dashboard.

Pre-requisites

–  No prior experience in Machine Learning is required to enroll in this course.

1. Introduction to Machine Learning

a). Introduction to Machine Learning

b). Machine Learning use cases and real-world applications

c). BigML sources and datasets

d). Supervised learning models: Models, Ensembles, Linear Regressions, Logistic Regressions, Deepnets, Time Series, OptiML, and Fusions

e). Predictions

f). Evaluations: How to properly evaluate a predictive model

g). Unsupervised learning models: Clusters, Anomaly Detectors, Associations, and Topic Models

2. Data Preparation for Machine Learning with BigML Analyst

a). Data processing for Machine Learning

b). ML-ready data

c). Feature engineering

d). Feature selection

3. Automating Machine Learning in 1-Click of BigML

a). Machine Learning iterations

b). Real Machine Learning solutions requirements

c). 1-click automation: Scriptify

For more inputs on BigML Certified Analyst you can connect here.
Contact the L&D Specialist at Locus IT.

Locus Academy has more than a decade experience in delivering the training/staffing on BigML Certified Analyst for corporates across the globe. The participants for the training/staffing on BigML Certified Analyst are extremely satisfied and are able to implement the learnings in their on going projects.

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BigML is a consumable, programmable, and also scalable Machine Learning platform that makes it easy to solve and automate Classification, Regression, Time Series Forecasting, Cluster Analysis, Anomaly Detection and Topic Modeling tasks.