Machine Learning with Spark ML

2h 7m 29s
English
Paid

Course description

Learn to use Spark ML to create scalable machine learning solutions. Practice with regression, classification, feature engineering, model evaluation, hyperparameter tuning, and integrating deep learning with Apache Spark.

Machine learning is not just theory, but also the ability to work in real, scalable systems. In this course, you will learn how to bring ML models to production level using the Spark ML library.

You will practically study methods of regression and classification, learn to effectively create and transform features, conduct model evaluations, and adjust their parameters to achieve the best results. You will also discover how to integrate deep learning elements into Spark workflows.

If you are ready to move from experimentation to building real solutions, this course is for you.

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#1: Introduction: Machine Learning with SparkML

All Course Lessons (24)

#Lesson TitleDurationAccess
1
Introduction: Machine Learning with SparkML Demo
07:46
2
What Is Machine Learning?
06:06
3
[Optional] What Is a Virtualenv?
06:37
4
Regression Algorithms
05:38
5
Building a Regression Model
05:04
6
Training a Model
09:46
7
Model Evaluation
07:26
8
Testing a Regression Model
03:57
9
Model Lifecycle
02:12
10
Feature Engineering
08:44
11
Improving a Regression Model
07:34
12
Machine Learning Pipelines
03:56
13
Creating a Pipeline
02:41
14
[Exercise] House Price Estimation
01:59
15
[Exercise] House Price Estimation - Solution
03:12
16
Classification
07:37
17
Classifiers Evaluation
04:27
18
Training a Classifier
08:31
19
Hyperparameters
08:06
20
Optimizing a Model
03:02
21
[Exercise] Loan Approval
02:34
22
[Exercise] Loan Approval - Solution
02:33
23
Deep Learning
06:56
24
Let's Keep Learning Together!
01:05

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