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Certified Machine Learning Associate Certification Video Training Course

Looking for studying for Certified Machine Learning Associate certification training course. Our Certified Machine Learning Associate certification video training course training course is your number one assistant. Pass with our Certified Machine Learning Associate certification training course on the first try and become a certified professional in no time.

  • 112 Students Enrolled
  • 118 Lectures
  • 15 Hours
  • 5.0 Rating
$38.49
$34.99

Curriculum For This Course

  • Getting started with Databricks Machine Learning

    Video Name 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

  • Databricks Runtime for Machine

    Video Name 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

  • AutoML (Classification, Regression, Forecasting)

    Video Name 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

  • Feature store

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

  • Managed MLflow

    Video Name 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

  • Exploratory Data Analysis & Feature Engineering

    Video Name 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

  • Hyperparameter Tuning with Hyperopt

    Video Name 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

  • Spark ML Modeling APIs - Binary Classification

    Video Name 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

  • Spark ML Modeling APIs - Regression with GBT & MLib Pipelines

    Video Name 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

  • Spark ML Modeling APIs - Decision Trees SFO Airport Survey

    Video Name 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

  • Pandas on Databricks & Accessing Data ADLS

    Video Name 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

  • Pandas API on Spark

    Video Name 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

  • Pandas Function APIs

    Video Name 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

  • Pandas User Defined Functions

    Video Name 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

  • Thank You

    Video Name Time
    1. Congratulations & way forward 1:23

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