Databricks Certified Machine Learning Associate Exam Guide

Databricks Certified Machine Learning Associate Exam Guide - Printable Version

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Databricks Certified Machine Learning Associate Exam Guide - BaDshaH - 09-20-2023

[Image: aa136509e8efb8759299280702d9a478.jpg]

Last updated 9/2023
Duration: 16h5m | Video: .MP4, 1280x720 30 fps | Audio: AAC, 48 kHz, 2ch | Size: 6.13 GB
Genre: eLearning | Language: English

Pass Databricks Certified Machine Learning Associate Certification with 10+ Hours of HD Quality Video & Lots of Hands-on

What you'll learn
Apply Databricks AutoML to different ML Problem like Regression, Classification
Use MLFlow to Track Complete ML Lifecycle inside Data bricks environment
Register model & Deploy to Production with MLFlow & Databricks
Store Model Features inside Feature Store

Requirements
Basic Machine Learning knowledge
Credit or Debit card for Azure Account

Description
Welcome to our comprehensive course on
Databricks Certified Machine Learning Engineer Associate certification.
This course is designed to help you master the skills required to become a certified Databricks ML engineer associate.
Databricks is a cloud-based data analytics platform that offers a unified approach to data processing, machine learning, and analytics. With the growing demand for data engineers, Databricks has become one of the most sought-after skills in the industry.
The minimally qualified candidate should be able to
Use Databricks Machine Learning and its capabilities within machine learning workflows, including
Databricks Machine Learning (clusters, Repos, Jobs)
Databricks Runtime for Machine Learning (basics, libraries)
AutoML (classification, regression, forecasting)
Feature Store (basics)
MLflow (Tracking, Models, Model Registry)
Implement correct decisions in machine learning workflows, including
Exploratory data analysis (summary statistics, outlier removal)
Feature engineering (missing value imputation, one-hot-encoding)
Tuning (hyperparameter basics, hyperparameter parallelization)
Evaluation and selection (cross-validation, evaluation metrics)
Implement machine learning solutions at scale using Spark ML and other tools, including
Distributed ML Concepts
Spark ML Modeling APIs (data splitting, training, evaluation, estimators vs. transformers, pipelines)
Hyperopt
Pandas API on Spark
Pandas UDFs and Pandas Function APIs
Understand advanced scaling characteristics of classical machine learning models, including
Distributed Linear Regression
Distributed Decision Trees
Ensembling Methods (bagging, boosting)

Who this course is for
Anyone wants to Pass Databricks Certified Machine Learning Associate Exam

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