PDFs and exam guides are not so efficient, right? Prepare for your Databricks examination with our training course. The Certified Machine Learning Associate course contains a complete batch of videos that will provide you with profound and thorough knowledge related to Databricks certification exam. Pass the Databricks Certified Machine Learning Associate test with flying colors.
Curriculum for Certified Machine Learning Associate Certification Video Course
Name of Video | Time |
---|---|
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 |
Name of Video | Time |
---|---|
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 |
Name of Video | Time |
---|---|
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 |
Name of Video | Time |
---|---|
1. Databricks Feature store Part - 1 |
11:05 |
2. Databricks Feature store Part - 2 |
11:56 |
Name of Video | Time |
---|---|
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 |
Name of Video | Time |
---|---|
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 |
Name of Video | Time |
---|---|
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 |
Name of Video | Time |
---|---|
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 |
Name of Video | Time |
---|---|
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 |
Name of Video | Time |
---|---|
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 |
Name of Video | Time |
---|---|
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 |
Name of Video | Time |
---|---|
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 |
Name of Video | Time |
---|---|
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 |
Name of Video | Time |
---|---|
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 |
Name of Video | Time |
---|---|
1. Congratulations & way forward |
1:23 |
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File Name | Size | Votes |
---|---|---|
File Name databricks.real-exams.certified machine learning associate.v2024-10-18.by.abdulrahman.7q.vce |
Size 17.58 KB |
Votes 1 |
Databricks Certified Machine Learning Associate Training Course
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