Databricks Certified Machine Learning Associate Exam Guide

سرفصل های دوره

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

01 - Introduction

001 Introduction

002 Important - Udemy Tips & Review Update

003 Course FAQs.html

004 Course Materials.html

004 Databricks-Machine-Learning-HTML-Files.zip

004 bank-data.csv

004 databricks-machine-learning.zip

004 housing.csv

004 windfarm-data.csv

004 winequality-red.csv

004 winequality-white.csv

02 - Getting started with Databricks Machine Learning

001 Introduction to Databricks Machine Learning

002 Lab Databricks Workspace with Community Edition

003 Lab Databricks Workspace with Azure Cloud

004 Databricks User Interface Overview

005 Azure Databricks Architecture Overview

006 Resources Created by Azure Databricks Workspace

03 - Databricks Runtime for Machine Learning

001 Introduction to Databricks Runtime for Machine Learning

002 Lab Creating Databricks ML Cluster

003 Explore Cluster Features from UI

04 - AutoML (Classification, Regression, Forecasting)

001 Introduction to AutoML

002 AutoML Regression Databricks UI Part - 1

003 AutoML Regression Databricks UI Part - 2

004 AutoML Regression Databricks UI Part - 3

005 AutoML Regression Databricks Python API Part - 1

006 AutoML Regression Databricks Python API Part - 2

007 AutoML Classification Part - 1

008 AutoML Classification Part - 2

009 AutoML Forecasting Databricks UI Part - 1

010 AutoML Forecasting Databricks UI Part - 2

011 AutoML Forecasting Databricks Python API Part - 1

012 AutoML Forecasting Databricks Python API Part - 2

05 - Feature store

001 Databricks Feature store Part -1

002 Databricks Feature store Part -2

06 - Managed MLflow

001 Introduction to Mlflow

002 Lab Mlflow Logging API Part - 1

003 Lab Mlflow Logging API Part - 2

004 Lab Mlflow Logging API Part - 3

005 Lab ML End-to-End Example Part - 1

006 Lab ML End-to-End Example Part - 2

007 Lab ML End-to-End Example Part - 3

008 Lab ML End-to-End Example Part - 4

009 Lab ML End-to-End Example Part - 5

010 MLFlow Model Registry Part - 1

011 MLFlow Model Registry Part - 2

012 MLFlow Model Registry Part - 3

07 - Exploratory Data Analysis & Feature Engineering

001 Introduction to Exploratory Data Analysis

002 Exploratory Data Analysis Explore the Data Part 1

003 Exploratory Data Analysis Explore the Data Part 2

004 Exploratory Data Analysis Explore the Data Part 3

005 Exploratory Data Analysis Data Visualization

006 Exploratory Data Analysis Pandas Profiling

007 Feature engineering Missing Value Imputation

008 Feature engineering Outlier Removal

009 Feature engineering Feature Creation

010 Feature engineering Feature Scaling

011 Feature engineering One-Hot-Encoding

012 Feature engineering Feature Selection

013 Feature engineering Feature Transformation

014 Feature engineering Dimensionality Reduction

08 - Hyperparameter Tuning with Hyperopt

001 Hyperparameter Basics

002 Introduction to Hyperparameter tuning with Hyperopt

003 Hyperparameter Parallelization Loading the Dataset

004 Hyperparameter Parallelization Single-Machine Hyperopt Workflow

005 Hyperparameter Parallelization Distributed tuning using Apache Spark and MLflow

006 Model Selection with Hyperopt & MLflow Part 1

007 Model Selection with Hyperopt & MLflow Part 2

008 Model Selection with Hyperopt & MLflow Part 3

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

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

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

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

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

014 Automated MLflow Tracking & Cross-Validation Part 1

015 Automated MLflow Tracking & Cross-Validation Part 2

016 Automated MLflow Tracking & Cross-Validation Part 3

017 Automated MLflow Tracking & Cross-Validation Part 4

09 - Spark ML Modeling APIs - Binary Classification

001 Binary Classification - Loading Dataset

002 Binary Classification - Data Preprocessing & Feature Engineering Part 1

003 Binary Classification - Data Preprocessing & Feature Engineering Part 2

004 Binary Classification - Logistic Regression Part 1

005 Binary Classification - Logistic Regression Part 2

006 Binary Classification - Decision Trees

007 Binary Classification - Random Forest

008 Binary Classification - Making Predictions

10 - Spark ML Modeling APIs - Regression with GBT & MLlib Pipelines

001 Regression with GBT & MLlib Pipelines - Data Preprocessing Part 1

002 Regression with GBT & MLlib Pipelines - Data Preprocessing Part 2

003 Regression with GBT & MLlib Pipelines - Train ML Pipeline Part 1

004 Regression with GBT & MLlib Pipelines - Train ML Pipeline Part 2

005 Regression with GBT & MLlib Pipelines - Predicting and Evaluating ML Model

11 - Spark ML Modeling APIs - Decision Trees SFO Airport Survey

001 Decision Trees SFO Airport Survey - Business Problem

002 Decision Trees SFO Airport Survey - Loading Dataset

003 Decision Trees SFO Airport Survey - Understanding Dataset

004 Decision Trees SFO Airport Survey - Creating Model Part 1

005 Decision Trees SFO Airport Survey - Creating Model Part 2

006 Decision Trees SFO Airport Survey - Evaluating the Model

007 Decision Trees SFO Airport Survey - Feature Importance

12 - Pandas on Databricks & Accessing Data ADLS

001 Introduction to Pandas on Databricks

002 Store & Load Data with Pandas

003 Working with Files on Databricks

004 Accessing Data via Access Key

005 Accessing Data via SAS Token

006 Mounting ADLS to DBFS Part 1

007 Mounting ADLS to DBFS Part 2

008 Mount Storage Container Using f-strings

009 Multi-hop Architecture (Medallion Architecture) Part 1

010 Multi-hop Architecture (Medallion Architecture) Part 2

13 - Pandas API on Spark

001 Object Creation - Series

002 Object Creation - Dataframe

003 Object Creation - View Data

004 Object Creation - Data Selection

005 Applying Python Function with Pandas-on-Spark Object

006 Grouping Data

007 Plotting Data

008 Type Conversion and Native Support for Pandas Objects

009 Distributed Execution for Pandas Functions

010 Using SQL in Pandas API on Spark

011 Conversion from and to Pyspark Dataframe

012 Checking Spark Execution Plans

013 Caching Dataframes

14 - Pandas Function APIs

001 Introduction to Pandas Function APIs

002 Pandas Function API - Grouped Map

003 Pandas Function API - Map

004 Pandas Function API - Cogrouped Map

15 - Pandas User Defined Functions

001 Introduction Pandas User Defined Functions

002 Series to Series UDF

003 Iterator of Series to Iterator of Series UDF

004 Iterator of Multiple Series to Iterator of Series UDF

005 Series to Scalar UDF

16 - Thank You

001 Congratulations & way forward

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تولید کننده: Udemy