Curriculum For This Course

Getting started with Databricks Machine Learning

6Lectures

Time 00:35:55

Databricks Runtime for Machine

3Lectures

Time 00:17:54

AutoML (Classification, Regression, Forecasting)

12Lectures

Time 01:35:18

Feature store

2Lectures

Time 00:23:01

Managed MLflow

12Lectures

Time 01:46:23

Exploratory Data Analysis & Feature Engineering

14Lectures

Time 01:53:41

Hyperparameter Tuning with Hyperopt

17Lectures

Time 02:38:09

Spark ML Modeling APIs - Binary Classification

7Lectures

Time 01:11:35

Spark ML Modeling APIs - Regression with GBT & MLib Pipelines

5Lectures

Time 00:47:42

Spark ML Modeling APIs - Decision Trees SFO Airport Survey

7Lectures

Time 00:51:26

Pandas on Databricks & Accessing Data ADLS

10Lectures

Time 01:15:49

Pandas API on Spark

13Lectures

Time 01:26:46

Pandas Function APIs

4Lectures

Time 00:20:50

Pandas User Defined Functions

5Lectures

Time 00:32:52

Thank You

1Lectures

Time 00:01:23

Getting started with Databricks Machine Learning
	1. Introduction to Databricks Machine Learning	6:27
	2. Lab: Databricks Workspace with Community Edition	6:26
	3. Lab: Databricks Workspace with Azure Cloud	8:37
	4. Databricks User Interface Overview	8:55
	5. Azure Databricks Architecture Overview	3:06
	6. Resources Created by Azure Databricks Workspace	2:24

Databricks Runtime for Machine
	1. Introduction to Databricks Runtime for Machine Learning	6:21
	2. Lab: Creating Databricks ML Cluster	6:29
	3. Explore Cluster Features from UI	5:04

AutoML (Classification, Regression, Forecasting)
	1. Introduction to AutoML	8:02
	2. AutoML Regression Databricks UI Part - 1	10:44
	3. AutoML Regression Databricks UI Part - 2	11:25
	4. AutoML Regression Databricks UI Part - 3	12:15
	5. AutoML Regression Databricks Python API Part - 1	9:24
	6. AutoML Regression Databricks Python API Part - 2	4:46
	7. AutoML Classification Part - 1	10:06
	8. AutoML Classification Part - 2	7:09
	9. AutoML Forecasting Databricks UI Part - 1	8:20
	10. AutoML Forecasting Databricks UI Part - 2	2:46
	11. AutoML Forecasting Databricks Python API Part - 1	6:11
	12. AutoML Forecasting Databricks Python API Part - 2	4:10

Feature store
	1. Databricks Feature store Part - 1	11:05
	2. Databricks Feature store Part - 2	11:56

Managed MLflow
	1. Introduction to Mlflow	8:56
	2. Lab : Mlflow Logging API Part - 1	10:25
	3. Lab : Mlflow Logging API Part - 2	6:44
	4. Lab : Mlflow Logging API Part - 3	5:47
	5. Lab: ML End-to-End Example Part - 1	10:53
	6. Lab: ML End-to-End Example Part - 2	11:27
	7. Lab: ML End-to-End Example Part - 3	10:28
	8. Lab: ML End-to-End Example Part - 4	7:54
	9. Lab: ML End-to-End Example Part - 5	7:26
	10. MLFlow Model Registry Part - 1	10:22
	11. MLFlow Model Registry Part - 2	5:50
	12. MLFlow Model Registry Part - 3	10:11

Exploratory Data Analysis & Feature Engineering
	1. Introduction to Exploratory Data Analysis	4:34
	2. Exploratory Data Analysis: Explore the Data Part 1	13:13
	3. Exploratory Data Analysis: Explore the Data Part 2	9:39
	4. Exploratory Data Analysis: Explore the Data Part 3	9:14
	5. Exploratory Data Analysis: Data Visualization	11:18
	6. Exploratory Data Analysis: Pandas Profiling	12:29
	7. Feature engineering: Missing Value Imputation	8:32
	8. Feature engineering: Outlier Removal	7:58
	9. Feature engineering: Feature Creation	7:43
	10. Feature engineering: Feature Scaling	6:44
	11. Feature engineering: One-Hot-Encoding	6:00
	12. Feature engineering: Feature Selection	6:19
	13. Feature engineering: Feature Transformation	4:44
	14. Feature engineering: Dimensionality Reduction	5:14

Hyperparameter Tuning with Hyperopt
	1. Hyperparameter Basics	6:29
	2. Introduction to Hyperparameter tuning with Hyperopt	2:15
	3. Hyperparameter Parallelization: Loading the Dataset	6:55
	4. Hyperparameter Parallelization: Single-Machine Hyperopt Workflow	8:55
	5. Hyperparameter Parallelization: Distributed tuning using Apache Spark and MLflow	11:05
	6. Model Selection with Hyperopt & MLflow Part 1	5:40
	7. Model Selection with Hyperopt & MLflow Part 2	5:49
	8. Model Selection with Hyperopt & MLflow Part 3	15:15
	9. Tuning Distributed Training Algorithms (Hyperopt & Apache Spark MLlib) Part 1	11:27
	10. Tuning Distributed Training Algorithms (Hyperopt & Apache Spark MLlib) Part 2	12:17
	11. Tuning Distributed Training Algorithms (Hyperopt & Apache Spark MLlib) Part 3	3:44
	12. Tuning Distributed Training Algorithms (Hyperopt & Apache Spark MLlib) Part 4	13:45
	13. Tuning Distributed Training Algorithms (Hyperopt & Apache Spark MLlib) Part 5	6:05
	14. Automated MLflow Tracking & Cross-Validation Part 1	10:21
	15. Automated MLflow Tracking & Cross-Validation Part 2	11:47
	16. Automated MLflow Tracking & Cross-Validation Part 3	7:30
	17. Automated MLflow Tracking & Cross-Validation Part 4	18:50

Spark ML Modeling APIs - Binary Classification
	1. Binary Classification - Loading Dataset	11:59
	2. Binary Classification - Data Preprocessing & Feature Engineering Part 1	9:55
	3. Binary Classification - Data Preprocessing & Feature Engineering Part 2	10:56
	4. Binary Classification - Logistic Regression Part 1	12:32
	5. Binary Classification - Logistic Regression Part 2	11:45
	6. Binary Classification - Random Forest	9:35
	7. Binary Classification - Making Predictions	4:53

Spark ML Modeling APIs - Regression with GBT & MLib Pipelines
	1. Regression with GBT & MLlib Pipelines - Data Preprocessing Part 1	13:00
	2. Regression with GBT & MLlib Pipelines - Data Preprocessing Part 2	7:56
	3. Regression with GBT & MLlib Pipelines - Train ML Pipeline Part 1	9:36
	4. Regression with GBT & MLlib Pipelines - Train ML Pipeline Part 2	8:26
	5. Regression with GBT & MLlib Pipelines - Predicting and Evaluating ML Model	8:44

Spark ML Modeling APIs - Decision Trees SFO Airport Survey
	1. Decision Trees SFO Airport Survey - Business Problem	3:17
	2. Decision Trees SFO Airport Survey - Loading Dataset	2:51
	3. Decision Trees SFO Airport Survey - Understanding Dataset	7:32
	4. Decision Trees SFO Airport Survey - Creating Model Part 1	10:47
	5. Decision Trees SFO Airport Survey - Creating Model Part 2	5:44
	6. Decision Trees SFO Airport Survey - Evaluating the Model	7:26
	7. Decision Trees SFO Airport Survey - Feature Importance	13:49

Pandas on Databricks & Accessing Data ADLS
	1. Introduction to Pandas on Databricks	1:15
	2. Store & Load Data with Pandas	7:07
	3. Working with Files on Databricks	7:08
	4. Accessing Data via Access Key	10:46
	5. Accessing Data via SAS Token	3:37
	6. Mounting ADLS to DBFS Part 1	10:49
	7. Mounting ADLS to DBFS Part 2	8:20
	8. Mount Storage Container Using f-strings	9:02
	9. Multi-hop Architecture (Medallion Architecture) Part 1	6:48
	10. Multi-hop Architecture (Medallion Architecture) Part 2	10:57

Pandas API on Spark
	1. Object Creation - Series	9:50
	2. Object Creation - Dataframe	7:01
	3. Object Creation - View Data	7:57
	4. Object Creation - Data Selection	9:49
	5. Applying Python Function with Pandas-on-Spark Object	10:45
	6. Grouping Data	3:00
	7. Plotting Data	8:40
	8. Type Conversion and Native Support for Pandas Objects	5:57
	9. Distributed Execution for Pandas Functions	6:09
	10. Using SQL in Pandas API on Spark	3:24
	11. Conversion from and to Pyspark Dataframe	5:29
	12. Checking Spark Execution Plans	5:01
	13. Caching Dataframes	3:44

Pandas Function APIs
	1. Introduction to Pandas Function APIs	1:41
	2. Pandas Function API - Grouped Map	7:59
	3. Pandas Function API - Map	5:00
	4. Pandas Function API - Cogrouped Map	6:10

Pandas User Defined Functions
	1. Introduction: Pandas User Defined Functions	5:04
	2. Series to Series UDF	6:40
	3. Iterator of Series to Iterator of Series UDF	8:44
	4. Iterator of Multiple Series to Iterator of Series UDF	6:10
	5. Series to Scalar UDF	6:14

Thank You
	1. Congratulations & way forward	1:23

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