Databricks Certified Machine Learning Associate Exam Guide
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Vijay Gadhave,Ankit Mistry
16:04:58
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001 Introduction.mp4
03:07
002 Important - Udemy Tips & Review Update.mp4
01:58
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
001 Introduction to Databricks Machine Learning.mp4
06:28
002 Lab Databricks Workspace with Community Edition.mp4
06:41
003 Lab Databricks Workspace with Azure Cloud.mp4
08:54
004 Databricks User Interface Overview.mp4
08:56
005 Azure Databricks Architecture Overview.mp4
03:07
006 Resources Created by Azure Databricks Workspace.mp4
02:26
001 Introduction to Databricks Runtime for Machine Learning.mp4
06:23
002 Lab Creating Databricks ML Cluster.mp4
06:34
003 Explore Cluster Features from UI.mp4
05:05
001 Introduction to AutoML.mp4
08:03
002 AutoML Regression Databricks UI Part - 1.mp4
10:46
003 AutoML Regression Databricks UI Part - 2.mp4
11:33
004 AutoML Regression Databricks UI Part - 3.mp4
12:15
005 AutoML Regression Databricks Python API Part - 1.mp4
09:27
006 AutoML Regression Databricks Python API Part - 2.mp4
04:48
007 AutoML Classification Part - 1.mp4
10:08
008 AutoML Classification Part - 2.mp4
07:11
009 AutoML Forecasting Databricks UI Part - 1.mp4
08:22
010 AutoML Forecasting Databricks UI Part - 2.mp4
02:48
011 AutoML Forecasting Databricks Python API Part - 1.mp4
06:12
012 AutoML Forecasting Databricks Python API Part - 2.mp4
04:11
001 Databricks Feature store Part -1.mp4
11:05
002 Databricks Feature store Part -2.mp4
11:57
001 Introduction to Mlflow.mp4
08:58
002 Lab Mlflow Logging API Part - 1.mp4
10:26
003 Lab Mlflow Logging API Part - 2.mp4
06:45
004 Lab Mlflow Logging API Part - 3.mp4
05:49
005 Lab ML End-to-End Example Part - 1.mp4
10:54
006 Lab ML End-to-End Example Part - 2.mp4
11:28
007 Lab ML End-to-End Example Part - 3.mp4
10:29
008 Lab ML End-to-End Example Part - 4.mp4
07:55
009 Lab ML End-to-End Example Part - 5.mp4
07:27
010 MLFlow Model Registry Part - 1.mp4
10:23
011 MLFlow Model Registry Part - 2.mp4
05:51
012 MLFlow Model Registry Part - 3.mp4
10:11
001 Introduction to Exploratory Data Analysis.mp4
04:34
002 Exploratory Data Analysis Explore the Data Part 1.mp4
13:13
003 Exploratory Data Analysis Explore the Data Part 2.mp4
09:38
004 Exploratory Data Analysis Explore the Data Part 3.mp4
09:14
005 Exploratory Data Analysis Data Visualization.mp4
11:19
006 Exploratory Data Analysis Pandas Profiling.mp4
12:29
007 Feature engineering Missing Value Imputation.mp4
08:32
008 Feature engineering Outlier Removal.mp4
07:58
009 Feature engineering Feature Creation.mp4
07:44
010 Feature engineering Feature Scaling.mp4
06:45
011 Feature engineering One-Hot-Encoding.mp4
06:00
012 Feature engineering Feature Selection.mp4
06:20
013 Feature engineering Feature Transformation.mp4
04:44
014 Feature engineering Dimensionality Reduction.mp4
05:14
001 Hyperparameter Basics.mp4
06:30
002 Introduction to Hyperparameter tuning with Hyperopt.mp4
02:13
003 Hyperparameter Parallelization Loading the Dataset.mp4
06:56
004 Hyperparameter Parallelization Single-Machine Hyperopt Workflow.mp4
08:56
005 Hyperparameter Parallelization Distributed tuning using Apache Spark and MLflow.mp4
11:06
006 Model Selection with Hyperopt & MLflow Part 1.mp4
05:41
007 Model Selection with Hyperopt & MLflow Part 2.mp4
05:50
008 Model Selection with Hyperopt & MLflow Part 3.mp4
15:15
009 Tuning Distributed Training Algorithms (Hyperopt & Apache Spark MLlib) Part 1.mp4
11:28
010 Tuning Distributed Training Algorithms (Hyperopt & Apache Spark MLlib) Part 2.mp4
12:17
011 Tuning Distributed Training Algorithms (Hyperopt & Apache Spark MLlib) Part 3.mp4
03:45
012 Tuning Distributed Training Algorithms (Hyperopt & Apache Spark MLlib) Part 4.mp4
13:45
013 Tuning Distributed Training Algorithms (Hyperopt & Apache Spark MLlib) Part 5.mp4
06:01
014 Automated MLflow Tracking & Cross-Validation Part 1.mp4
10:21
015 Automated MLflow Tracking & Cross-Validation Part 2.mp4
11:47
016 Automated MLflow Tracking & Cross-Validation Part 3.mp4
07:31
017 Automated MLflow Tracking & Cross-Validation Part 4.mp4
18:51
001 Binary Classification - Loading Dataset.mp4
11:59
002 Binary Classification - Data Preprocessing & Feature Engineering Part 1.mp4
09:56
003 Binary Classification - Data Preprocessing & Feature Engineering Part 2.mp4
10:57
004 Binary Classification - Logistic Regression Part 1.mp4
12:33
005 Binary Classification - Logistic Regression Part 2.mp4
11:47
006 Binary Classification - Decision Trees.mp4
16:59
007 Binary Classification - Random Forest.mp4
09:35
008 Binary Classification - Making Predictions.mp4
04:54
001 Regression with GBT & MLlib Pipelines - Data Preprocessing Part 1.mp4
13:01
002 Regression with GBT & MLlib Pipelines - Data Preprocessing Part 2.mp4
07:56
003 Regression with GBT & MLlib Pipelines - Train ML Pipeline Part 1.mp4
09:37
004 Regression with GBT & MLlib Pipelines - Train ML Pipeline Part 2.mp4
08:26
005 Regression with GBT & MLlib Pipelines - Predicting and Evaluating ML Model.mp4
08:45
001 Decision Trees SFO Airport Survey - Business Problem.mp4
03:18
002 Decision Trees SFO Airport Survey - Loading Dataset.mp4
02:52
003 Decision Trees SFO Airport Survey - Understanding Dataset.mp4
07:33
004 Decision Trees SFO Airport Survey - Creating Model Part 1.mp4
10:47
005 Decision Trees SFO Airport Survey - Creating Model Part 2.mp4
05:44
006 Decision Trees SFO Airport Survey - Evaluating the Model.mp4
07:27
007 Decision Trees SFO Airport Survey - Feature Importance.mp4
13:49
001 Introduction to Pandas on Databricks.mp4
01:16
002 Store & Load Data with Pandas.mp4
07:08
003 Working with Files on Databricks.mp4
07:08
004 Accessing Data via Access Key.mp4
10:46
005 Accessing Data via SAS Token.mp4
03:38
006 Mounting ADLS to DBFS Part 1.mp4
10:49
007 Mounting ADLS to DBFS Part 2.mp4
08:20
008 Mount Storage Container Using f-strings.mp4
09:03
009 Multi-hop Architecture (Medallion Architecture) Part 1.mp4
06:49
010 Multi-hop Architecture (Medallion Architecture) Part 2.mp4
10:57
001 Object Creation - Series.mp4
09:51
002 Object Creation - Dataframe.mp4
07:02
003 Object Creation - View Data.mp4
07:58
004 Object Creation - Data Selection.mp4
09:50
005 Applying Python Function with Pandas-on-Spark Object.mp4
10:45
006 Grouping Data.mp4
03:00
007 Plotting Data.mp4
08:40
008 Type Conversion and Native Support for Pandas Objects.mp4
05:58
009 Distributed Execution for Pandas Functions.mp4
06:10
010 Using SQL in Pandas API on Spark.mp4
03:25
011 Conversion from and to Pyspark Dataframe.mp4
05:29
012 Checking Spark Execution Plans.mp4
05:00
013 Caching Dataframes.mp4
03:44
001 Introduction to Pandas Function APIs.mp4
01:42
002 Pandas Function API - Grouped Map.mp4
08:00
003 Pandas Function API - Map.mp4
05:01
004 Pandas Function API - Cogrouped Map.mp4
06:11
001 Introduction Pandas User Defined Functions.mp4
05:04
002 Series to Series UDF.mp4
06:41
003 Iterator of Series to Iterator of Series UDF.mp4
08:45
004 Iterator of Multiple Series to Iterator of Series UDF.mp4
06:10
005 Series to Scalar UDF.mp4
06:15
001 Congratulations & way forward.mp4
03:30
Description
Pass Databricks Certified Machine Learning Associate Certification with 10+ Hours of HD Quality Video & Lots of Hands-on
What You'll Learn?
- Apply Databricks AutoML to different ML Problem like Regression, Classification
- Use MLFlow to Track Complete ML Lifecycle inside Data bricks environment
- Register model & Deploy to Production with MLFlow & Databricks
- Store Model Features inside Feature Store
Who is this for?
What You Need to Know?
More details
DescriptionWelcome to our comprehensive course on Databricks Certified Machine Learning Engineer Associate certification. This course is designed to help you master the skills required to become a certified Databricks ML engineer associate.
Databricks is a cloud-based data analytics platform that offers a unified approach to data processing, machine learning, and analytics. With the growing demand for data engineers, Databricks has become one of the most sought-after skills in the industry.
The minimally qualified candidate should be able to:
Use Databricks Machine Learning and its capabilities within machine learning workflows, including:
Databricks Machine Learning (clusters, Repos, Jobs)
Databricks Runtime for Machine Learning (basics, libraries)
AutoML (classification, regression, forecasting)
Feature Store (basics)
MLflow (Tracking, Models, Model Registry)
Implement correct decisions in machine learning workflows, including:
Exploratory data analysis (summary statistics, outlier removal)
Feature engineering (missing value imputation, one-hot-encoding)
Tuning (hyperparameter basics, hyperparameter parallelization)
Evaluation and selection (cross-validation, evaluation metrics)
Implement machine learning solutions at scale using Spark ML and other tools, including:
Distributed ML Concepts
Spark ML Modeling APIs (data splitting, training, evaluation, estimators vs. transformers, pipelines)
Hyperopt
Pandas API on Spark
Pandas UDFs and Pandas Function APIs
Understand advanced scaling characteristics of classical machine learning models, including:
Distributed Linear Regression
Distributed Decision Trees
Ensembling Methods (bagging, boosting)
Who this course is for:
- Anyone wants to Pass Databricks Certified Machine Learning Associate Exam
Welcome to our comprehensive course on Databricks Certified Machine Learning Engineer Associate certification. This course is designed to help you master the skills required to become a certified Databricks ML engineer associate.
Databricks is a cloud-based data analytics platform that offers a unified approach to data processing, machine learning, and analytics. With the growing demand for data engineers, Databricks has become one of the most sought-after skills in the industry.
The minimally qualified candidate should be able to:
Use Databricks Machine Learning and its capabilities within machine learning workflows, including:
Databricks Machine Learning (clusters, Repos, Jobs)
Databricks Runtime for Machine Learning (basics, libraries)
AutoML (classification, regression, forecasting)
Feature Store (basics)
MLflow (Tracking, Models, Model Registry)
Implement correct decisions in machine learning workflows, including:
Exploratory data analysis (summary statistics, outlier removal)
Feature engineering (missing value imputation, one-hot-encoding)
Tuning (hyperparameter basics, hyperparameter parallelization)
Evaluation and selection (cross-validation, evaluation metrics)
Implement machine learning solutions at scale using Spark ML and other tools, including:
Distributed ML Concepts
Spark ML Modeling APIs (data splitting, training, evaluation, estimators vs. transformers, pipelines)
Hyperopt
Pandas API on Spark
Pandas UDFs and Pandas Function APIs
Understand advanced scaling characteristics of classical machine learning models, including:
Distributed Linear Regression
Distributed Decision Trees
Ensembling Methods (bagging, boosting)
Who this course is for:
- Anyone wants to Pass Databricks Certified Machine Learning Associate Exam
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Vijay Gadhave
Instructor's CoursesHello Data Lover,I am glad that you are reading this!I am Vijay Gadhave and I have 10+ years of experience in the IT Industry. I am passionate about Cloud Computing and Machine Learning.I teach in areas of Cloud Computing, Machine Learning, Python, Data Science, and Data analysis.I hope you will enjoy my course and it will help you to grow in your career.
Ankit Mistry
Instructor's CoursesI am Ankit Mistry, completed my master from IIT Kharagpur in area of machine learning, Artificial intelligence. Now working as Software Developer, Big Data Engineer in one of leading private investment bank with 8+ years of experience in software industry. Over the time I developed interest related to data discipline and learned about data analysis, machine learning model development, Cloud Computing.Created course in area of Cloud Computing, Google Cloud, Python, Data Science, Data analysis, Machine Learning.I am so excited to be on Udemy online learning platform and want to make big impact on your software career.I hope you will like my course offering.
Udemy
View courses UdemyStudents take courses primarily to improve job-related skills.Some courses generate credit toward technical certification. Udemy has made a special effort to attract corporate trainers seeking to create coursework for employees of their company.
- language english
- Training sessions 121
- duration 16:04:58
- English subtitles has
- Release Date 2023/11/14
