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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# Title Duration
1 Introduction: Machine Learning with SparkML 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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