Databricks Certified Machine Learning Associate Exam Guide

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 ConceptsSpark ML Modeling APIs (data splitting, training, evaluation, estimators vs. transformers, pipelines)HyperoptPandas API on SparkPandas UDFs and Pandas Function APIsUnderstand advanced scaling characteristics of classical machine learning models, including:Distributed Linear RegressionDistributed Decision TreesEnsembling Methods (bagging, boosting)

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Course FAQ's

Section 2: Getting started with Databricks Machine Learning

Lecture 3 Introduction to Databricks Machine Learning

Lecture 4 Lab: Databricks Workspace with Community Edition

Lecture 5 Lab: Databricks Workspace with Azure Cloud

Lecture 6 Databricks User Interface Overview

Lecture 7 Azure Databricks Architecture Overview

Lecture 8 Resources Created by Azure Databricks Workspace

Section 3: Databricks Runtime for Machine Learning

Lecture 9 Introduction to Databricks Runtime for Machine Learning

Lecture 10 Lab: Creating Databricks ML Cluster

Lecture 11 Explore Cluster Features from UI

Section 4: AutoML (Classification, Regression, Forecasting)

Lecture 12 Introduction to AutoML

Lecture 13 AutoML Regression Databricks UI Part - 1

Lecture 14 AutoML Regression Databricks UI Part - 2

Lecture 15 AutoML Regression Databricks UI Part - 3

Lecture 16 AutoML Regression Databricks Python API Part - 1

Lecture 17 AutoML Regression Databricks Python API Part - 2

Lecture 18 AutoML Classification Part - 1

Lecture 19 AutoML Classification Part - 2

Lecture 20 AutoML Forecasting Databricks UI Part - 1

Lecture 21 AutoML Forecasting Databricks UI Part - 2

Lecture 22 AutoML Forecasting Databricks Python API Part - 1

Lecture 23 AutoML Forecasting Databricks Python API Part - 2

Section 5: MLflow

Lecture 24 Introduction to Mlflow

Lecture 25 Lab : Mlflow Logging API Part - 1

Lecture 26 Lab : Mlflow Logging API Part - 2

Lecture 27 Lab : Mlflow Logging API Part - 3

Lecture 28 Lab: ML End-to-End Example Part - 1

Lecture 29 Lab: ML End-to-End Example Part - 2

Lecture 30 Lab: ML End-to-End Example Part - 3

Lecture 31 Lab: ML End-to-End Example Part - 4

Lecture 32 Lab: ML End-to-End Example Part - 5

Section 6: Exploratory Data Analysis & Feature Engineering

Lecture 33 Introduction to Exploratory Data Analysis

Lecture 34 Exploratory Data Analysis: Explore the Data Part 1

Lecture 35 Exploratory Data Analysis: Explore the Data Part 2

Lecture 36 Exploratory Data Analysis: Explore the Data Part 3

Lecture 37 Exploratory Data Analysis: Data Visualization

Lecture 38 Exploratory Data Analysis: Pandas Profiling

Lecture 39 Feature engineering: Missing Value Imputation

Lecture 40 Feature engineering: Outlier Removal

Lecture 41 Feature engineering: Feature Creation

Lecture 42 Feature engineering: Feature Scaling

Lecture 43 Feature engineering: One-Hot-Encoding

Lecture 44 Feature engineering: Feature Selection

Lecture 45 Feature engineering: Feature Transformation

Lecture 46 Feature engineering: Dimensionality Reduction

Section 7: Hyperparameter Tuning with Hyperopt

Lecture 47 Hyperparameter Basics

Lecture 48 Introduction to Hyperparameter tuning with Hyperopt

Lecture 49 Hyperparameter Parallelization: Loading the Dataset

Lecture 50 Hyperparameter Parallelization: Single-Machine Hyperopt Workflow

Lecture 51 Hyperparameter Parallelization: Distributed tuning using Apache Spark and MLflow

Lecture 52 Model Selection with Hyperopt & MLflow Part 1

Lecture 53 Model Selection with Hyperopt & MLflow Part 2

Lecture 54 Model Selection with Hyperopt & MLflow Part 3

Lecture 55 Tuning Distributed Training Algorithms (Hyperopt & Apache Spark MLlib) Part 1

Lecture 56 Tuning Distributed Training Algorithms (Hyperopt & Apache Spark MLlib) Part 2

Lecture 57 Tuning Distributed Training Algorithms (Hyperopt & Apache Spark MLlib) Part 3

Lecture 58 Tuning Distributed Training Algorithms (Hyperopt & Apache Spark MLlib) Part 4

Lecture 59 Tuning Distributed Training Algorithms (Hyperopt & Apache Spark MLlib) Part 5

Lecture 60 Automated MLflow Tracking & Cross-Validation Part 1

Lecture 61 Automated MLflow Tracking & Cross-Validation Part 2

Lecture 62 Automated MLflow Tracking & Cross-Validation Part 3

Lecture 63 Automated MLflow Tracking & Cross-Validation Part 4

Section 8: Spark ML Modeling APIs

Lecture 64 Binary Classification - Loading Dataset

Lecture 65 Binary Classification - Data Preprocessing & Feature Engineering Part 1

Lecture 66 Binary Classification - Data Preprocessing & Feature Engineering Part 2

Lecture 67 Binary Classification - Logistic Regression Part 1

Lecture 68 Binary Classification - Logistic Regression Part 2

Lecture 69 Binary Classification - Decision Trees

Lecture 70 Binary Classification - Random Forest

Lecture 71 Binary Classification - Making Predictions

Section 9: Thank You

Lecture 72 Congratulations & way forward

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