CF

Machine Learning with Spark ML

2h 7m 29s
English
Free
Updated September 2026

Machine Learning with Spark ML is a 24-lesson 2 hours 7 minutes self-paced course by Zero To Mastery. Getting a machine learning model into a system that actually scales is a different problem than getting it to work in a notebook.

Course facts

Lessons
24
Duration
2 hours 7 minutes
Level
All levels
Language
English
Updated
2026-09-11
Instructor
Zero To Mastery
Price
Free

Getting a machine learning model into a system that actually scales is a different problem than getting it to work in a notebook. Machine Learning with Spark ML, free from Zero To Mastery, is built around that transition.

Core Techniques

  • Regression and classification methods suited to different data types
  • Feature engineering to improve model accuracy
  • Model evaluation and hyperparameter tuning for more robust results
  • Integrating deep learning components into a Spark workflow

Across 24 lessons, the course is aimed at data scientists, ML engineers, and anyone ready to move from experimentation to building models that hold up in real, scalable systems using Apache Spark.

Additional

https://github.com/mushketyk/ztm-data-engineering/tree/main/05-ml-with-spark

Who teaches Machine Learning with Spark ML? Zero To Mastery

Zero To Mastery thumbnail

Zero To Mastery (ZTM) is a Toronto-based online coding academy founded by Andrei Neagoie, originally a senior developer at large Canadian tech firms before turning to teaching full-time. The academy's signature is the cohort-based bootcamp track combined with a deep self-paced course library, all aimed at career-changers and self-taught developers preparing to land software-engineering roles at top companies.

The instructor roster has grown well beyond Andrei to include other senior practitioners: Daniel Bourke (machine learning), Aleksa Tešić (DevOps), Jacinto Wong, and others. Courses cover the full software-engineering career path: web development with React and Next.js, Python, machine learning and deep learning, DevOps and cloud, system design, mobile, and the algorithm / data-structure interview prep that gates engineering jobs.

The CourseFlix listing under this source carries over 120 ZTM courses spanning that full range. Material is paid; ZTM itself runs on a monthly / annual membership model. The teaching style favours long-form, project-based courses where students build complete portfolio-quality applications rather than disconnected feature tutorials.

What lessons are included in Machine Learning with Spark ML?

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0:00 0:00
#Lesson TitleDuration
1Introduction: Machine Learning with SparkML 07:46
2What Is Machine Learning? 06:06
3[Optional] What Is a Virtualenv? 06:37
4Regression Algorithms 05:38
5Building a Regression Model 05:04
6Training a Model 09:46
7Model Evaluation 07:26
8Testing a Regression Model 03:57
9Model Lifecycle 02:12
10Feature Engineering 08:44
11Improving a Regression Model 07:34
12Machine Learning Pipelines 03:56
13Creating a Pipeline 02:41
14[Exercise] House Price Estimation 01:59
15[Exercise] House Price Estimation - Solution 03:12
16Classification 07:37
17Classifiers Evaluation 04:27
18Training a Classifier 08:31
19Hyperparameters 08:06
20Optimizing a Model 03:02
21[Exercise] Loan Approval 02:34
22[Exercise] Loan Approval - Solution 02:33
23Deep Learning 06:56
24Let's Keep Learning Together! 01:05

What courses are similar to Machine Learning with Spark ML?

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Frequently asked questions

What prerequisites should I have before taking this course?
Before enrolling, students should have a basic understanding of machine learning concepts and be comfortable programming in Python. Familiarity with data analysis and working with libraries like Pandas and NumPy will be beneficial. Knowledge of Apache Spark is recommended but not essential, as the course provides foundational insights into using Spark ML.
What kind of projects will I build in this course?
The course includes practical exercises such as the 'House Price Estimation' and 'Loan Approval' projects. These exercises will help you apply concepts of regression, classification, and feature engineering to real-world scenarios, allowing you to build and evaluate machine learning models using Spark ML.
How does this course compare to other machine learning courses?
This course is distinct in its focus on deploying machine learning models in scalable systems using Spark ML. Unlike courses that only cover theoretical aspects, this course emphasizes practical application, including model evaluation, hyperparameter tuning, and integrating deep learning into Spark workflows.
What tools and platforms will I use throughout the course?
Throughout the course, you will primarily use Apache Spark ML. The course covers building and optimizing machine learning models using this library. Additionally, Python will be the main programming language used for exercises and projects.
What topics are not covered in this course?
The course does not cover topics outside the scope of machine learning with Spark ML, such as data visualization, detailed statistics, or non-Spark ML frameworks like TensorFlow or scikit-learn. It focuses specifically on deploying machine learning models in scalable systems using Spark.
How much time should I expect to commit to this course?
The course comprises 24 lessons, each focusing on different aspects of machine learning with Spark ML. While the total runtime is not specified, students should allocate additional time for completing practical exercises and projects, which are integral for gaining hands-on experience.
What skills will I gain that are transferable to other careers or courses?
By completing this course, you will gain skills in building scalable machine learning models, feature engineering, and model evaluation, which are valuable in various data science and engineering roles. The knowledge of integrating deep learning with Spark ML can also be applied to other advanced data processing and analytics courses.