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Certified Machine Learning Associate Certification Video Training Course
The complete solution to prepare for for your exam with Certified Machine Learning Associate certification video training course. The Certified Machine Learning Associate certification video training course contains a complete set of videos that will provide you with thorough knowledge to understand the key concepts. Top notch prep including Databricks Certified Machine Learning Associate exam dumps, study guide & practice test questions and answers.
99 Students Enrolled
15:38:44 Hours
Certified Machine Learning Associate Certification Video Training Course Exam Curriculum
1
Getting started with Databricks Machine Learning
6 Lectures
Time 00:35:55
2
Databricks Runtime for Machine
3 Lectures
Time 00:17:54
3
AutoML (Classification, Regression, Forecasting)
12 Lectures
Time 01:35:18
4
Feature store
2 Lectures
Time 00:23:01
5
Managed MLflow
12 Lectures
Time 01:46:23
6
Exploratory Data Analysis & Feature Engineering
14 Lectures
Time 01:53:41
7
Hyperparameter Tuning with Hyperopt
17 Lectures
Time 02:38:09
8
Spark ML Modeling APIs - Binary Classification
7 Lectures
Time 01:11:35
9
Spark ML Modeling APIs - Regression with GBT & MLib Pipelines
5 Lectures
Time 00:47:42
10
Spark ML Modeling APIs - Decision Trees SFO Airport Survey
7 Lectures
Time 00:51:26
11
Pandas on Databricks & Accessing Data ADLS
10 Lectures
Time 01:15:49
12
Pandas API on Spark
13 Lectures
Time 01:26:46
13
Pandas Function APIs
4 Lectures
Time 00:20:50
14
Pandas User Defined Functions
5 Lectures
Time 00:32:52
15
Thank You
1 Lectures
Time 00:01:23
Getting started with Databricks Machine Learning
-
6:27
1. Introduction to Databricks Machine Learning
-
6:26
2. Lab: Databricks Workspace with Community Edition
-
8:37
3. Lab: Databricks Workspace with Azure Cloud
-
8:55
4. Databricks User Interface Overview
-
3:06
5. Azure Databricks Architecture Overview
-
2:24
6. Resources Created by Azure Databricks Workspace
Databricks Runtime for Machine
-
6:21
1. Introduction to Databricks Runtime for Machine Learning
-
6:29
2. Lab: Creating Databricks ML Cluster
-
5:04
3. Explore Cluster Features from UI
AutoML (Classification, Regression, Forecasting)
-
8:02
1. Introduction to AutoML
-
10:44
2. AutoML Regression Databricks UI Part - 1
-
11:25
3. AutoML Regression Databricks UI Part - 2
-
12:15
4. AutoML Regression Databricks UI Part - 3
-
9:24
5. AutoML Regression Databricks Python API Part - 1
-
4:46
6. AutoML Regression Databricks Python API Part - 2
-
10:06
7. AutoML Classification Part - 1
-
7:09
8. AutoML Classification Part - 2
-
8:20
9. AutoML Forecasting Databricks UI Part - 1
-
2:46
10. AutoML Forecasting Databricks UI Part - 2
-
6:11
11. AutoML Forecasting Databricks Python API Part - 1
-
4:10
12. AutoML Forecasting Databricks Python API Part - 2
Feature store
-
11:05
1. Databricks Feature store Part - 1
-
11:56
2. Databricks Feature store Part - 2
Managed MLflow
-
8:56
1. Introduction to Mlflow
-
10:25
2. Lab : Mlflow Logging API Part - 1
-
6:44
3. Lab : Mlflow Logging API Part - 2
-
5:47
4. Lab : Mlflow Logging API Part - 3
-
10:53
5. Lab: ML End-to-End Example Part - 1
-
11:27
6. Lab: ML End-to-End Example Part - 2
-
10:28
7. Lab: ML End-to-End Example Part - 3
-
7:54
8. Lab: ML End-to-End Example Part - 4
-
7:26
9. Lab: ML End-to-End Example Part - 5
-
10:22
10. MLFlow Model Registry Part - 1
-
5:50
11. MLFlow Model Registry Part - 2
-
10:11
12. MLFlow Model Registry Part - 3
Exploratory Data Analysis & Feature Engineering
-
4:34
1. Introduction to Exploratory Data Analysis
-
13:13
2. Exploratory Data Analysis: Explore the Data Part 1
-
9:39
3. Exploratory Data Analysis: Explore the Data Part 2
-
9:14
4. Exploratory Data Analysis: Explore the Data Part 3
-
11:18
5. Exploratory Data Analysis: Data Visualization
-
12:29
6. Exploratory Data Analysis: Pandas Profiling
-
8:32
7. Feature engineering: Missing Value Imputation
-
7:58
8. Feature engineering: Outlier Removal
-
7:43
9. Feature engineering: Feature Creation
-
6:44
10. Feature engineering: Feature Scaling
-
6:00
11. Feature engineering: One-Hot-Encoding
-
6:19
12. Feature engineering: Feature Selection
-
4:44
13. Feature engineering: Feature Transformation
-
5:14
14. Feature engineering: Dimensionality Reduction
Hyperparameter Tuning with Hyperopt
-
6:29
1. Hyperparameter Basics
-
2:15
2. Introduction to Hyperparameter tuning with Hyperopt
-
6:55
3. Hyperparameter Parallelization: Loading the Dataset
-
8:55
4. Hyperparameter Parallelization: Single-Machine Hyperopt Workflow
-
11:05
5. Hyperparameter Parallelization: Distributed tuning using Apache Spark and MLflow
-
5:40
6. Model Selection with Hyperopt & MLflow Part 1
-
5:49
7. Model Selection with Hyperopt & MLflow Part 2
-
15:15
8. Model Selection with Hyperopt & MLflow Part 3
-
11:27
9. Tuning Distributed Training Algorithms (Hyperopt & Apache Spark MLlib) Part 1
-
12:17
10. Tuning Distributed Training Algorithms (Hyperopt & Apache Spark MLlib) Part 2
-
3:44
11. Tuning Distributed Training Algorithms (Hyperopt & Apache Spark MLlib) Part 3
-
13:45
12. Tuning Distributed Training Algorithms (Hyperopt & Apache Spark MLlib) Part 4
-
6:05
13. Tuning Distributed Training Algorithms (Hyperopt & Apache Spark MLlib) Part 5
-
10:21
14. Automated MLflow Tracking & Cross-Validation Part 1
-
11:47
15. Automated MLflow Tracking & Cross-Validation Part 2
-
7:30
16. Automated MLflow Tracking & Cross-Validation Part 3
-
18:50
17. Automated MLflow Tracking & Cross-Validation Part 4
Spark ML Modeling APIs - Binary Classification
-
11:59
1. Binary Classification - Loading Dataset
-
9:55
2. Binary Classification - Data Preprocessing & Feature Engineering Part 1
-
10:56
3. Binary Classification - Data Preprocessing & Feature Engineering Part 2
-
12:32
4. Binary Classification - Logistic Regression Part 1
-
11:45
5. Binary Classification - Logistic Regression Part 2
-
9:35
6. Binary Classification - Random Forest
-
4:53
7. Binary Classification - Making Predictions
Spark ML Modeling APIs - Regression with GBT & MLib Pipelines
-
13:00
1. Regression with GBT & MLlib Pipelines - Data Preprocessing Part 1
-
7:56
2. Regression with GBT & MLlib Pipelines - Data Preprocessing Part 2
-
9:36
3. Regression with GBT & MLlib Pipelines - Train ML Pipeline Part 1
-
8:26
4. Regression with GBT & MLlib Pipelines - Train ML Pipeline Part 2
-
8:44
5. Regression with GBT & MLlib Pipelines - Predicting and Evaluating ML Model
Spark ML Modeling APIs - Decision Trees SFO Airport Survey
-
3:17
1. Decision Trees SFO Airport Survey - Business Problem
-
2:51
2. Decision Trees SFO Airport Survey - Loading Dataset
-
7:32
3. Decision Trees SFO Airport Survey - Understanding Dataset
-
10:47
4. Decision Trees SFO Airport Survey - Creating Model Part 1
-
5:44
5. Decision Trees SFO Airport Survey - Creating Model Part 2
-
7:26
6. Decision Trees SFO Airport Survey - Evaluating the Model
-
13:49
7. Decision Trees SFO Airport Survey - Feature Importance
Pandas on Databricks & Accessing Data ADLS
-
1:15
1. Introduction to Pandas on Databricks
-
7:07
2. Store & Load Data with Pandas
-
7:08
3. Working with Files on Databricks
-
10:46
4. Accessing Data via Access Key
-
3:37
5. Accessing Data via SAS Token
-
10:49
6. Mounting ADLS to DBFS Part 1
-
8:20
7. Mounting ADLS to DBFS Part 2
-
9:02
8. Mount Storage Container Using f-strings
-
6:48
9. Multi-hop Architecture (Medallion Architecture) Part 1
-
10:57
10. Multi-hop Architecture (Medallion Architecture) Part 2
Pandas API on Spark
-
9:50
1. Object Creation - Series
-
7:01
2. Object Creation - Dataframe
-
7:57
3. Object Creation - View Data
-
9:49
4. Object Creation - Data Selection
-
10:45
5. Applying Python Function with Pandas-on-Spark Object
-
3:00
6. Grouping Data
-
8:40
7. Plotting Data
-
5:57
8. Type Conversion and Native Support for Pandas Objects
-
6:09
9. Distributed Execution for Pandas Functions
-
3:24
10. Using SQL in Pandas API on Spark
-
5:29
11. Conversion from and to Pyspark Dataframe
-
5:01
12. Checking Spark Execution Plans
-
3:44
13. Caching Dataframes
Pandas Function APIs
-
1:41
1. Introduction to Pandas Function APIs
-
7:59
2. Pandas Function API - Grouped Map
-
5:00
3. Pandas Function API - Map
-
6:10
4. Pandas Function API - Cogrouped Map
Pandas User Defined Functions
-
5:04
1. Introduction: Pandas User Defined Functions
-
6:40
2. Series to Series UDF
-
8:44
3. Iterator of Series to Iterator of Series UDF
-
6:10
4. Iterator of Multiple Series to Iterator of Series UDF
-
6:14
5. Series to Scalar UDF
Thank You
-
1:23
1. Congratulations & way forward
About Certified Machine Learning Associate Certification Video Training Course
Certified Machine Learning Associate certification video training course by prepaway along with practice test questions and answers, study guide and exam dumps provides the ultimate training package to help you pass.
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Certified Machine Learning Associate Premium Bundle
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- Premium File 74 Questions & Answers. Last update: Oct 15, 2024
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