Machine Learning with Apache Spark 2: 2-in-1. With the help of this course you can Learn to implement and evaluate machine learning solutions with Apache Spark 2.
This course was created by Packt Publishing. It was rated 4.8 out of 5 by approx 1112 ratings. There are approx 75860 users enrolled with this course, so don’t wait to download yours now. This course also includes 4 hours on-demand video, 1 Supplemental Resource, Full lifetime access, Access on mobile and TV & Certificate of Completion.
What Will You Learn?
Perform advanced text processing and build classification models
Use Natural Language Processing (NLP) techniques to create a program that learns structure of the posts in a forum
Stream applications to provide real-time insights and predictions
Implement Word2Vect in Apache Spark
Delve into graph processing using GraphX library
Learn the best practices involved in building, evaluating, tuning, and deploying Spark pipelines
Apache Spark lets you apply machine learning techniques to data in real time, giving users immediate machine-learning based insights based on what’s happening right now. It’s used to create machine learning models and programs that are distributed and much faster compared to standard machine learning toolkits such as R or Python. If you’re a data professional who is familiar with machine learning and wants to use Apache Spark for developing efficient and fast machine learning systems, then this learning path is for you.
This comprehensive 2-in-1 course teaches you to build machine learning systems, perform analytics, and predictions with Apache Spark. You’ll learn through practical demonstrations of use cases, clear explanations, and interesting real-world applications. Each section briefly establishes theoretical basis for the topic under discussion and then cement your understanding with practical use cases.
This training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.
The first course, Spark for Machine Learning, starts off with explaining how to use Spark MLlib. You will then learn supervised and unsupervised machine learning algorithms. You will also learn to build classification models and extracting proper futures from text using Word2Vect to achieve this. Next, you will build logistic regression model with Spark. You will learn to find clusters and correlations in your data using K-Means clustering. Moving ahead, you will learn how to validate models using cross-validation and area under the ROC measurement. You will then build an effective recommendation model using distributed Spark algorithm. Finally, you will be glanced through graph processing using GraphX library.
The second course, Advanced Machine Learning with Spark 2.x, starts with an introduction to the key concepts and data types that are fundamental to understanding distributed data processing and machine learning with Spark. You will then be provided with practical recipes that demonstrate some of the most popular algorithms in Spark, leading to the creation of sophisticated machine learning pipelines and applications. Further you will be learning more advanced use cases for machine learning such as streaming, NLP, and deep learning.
By the end of the course, you’ll be able to focus on leveraging Apache Spark to create fast and efficient machine learning systems.
Meet Your Expert(s):
We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:
- Tomasz Lelek is a Software Engineer who programs mostly in Java and Scala. He is a fan of microservice architectures and functional programming. He dedicates considerable time and effort to being better every day. Recently, he’s been delving into big data technologies such as Apache Spark and Hadoop. He is passionate about nearly everything associated with software development. He thinks that we should always try to consider different solutions and approaches to solving a problem. Recently, he was a speaker at several conferences in Poland: Confitura and JDD (Java Developer’s Day) and also at Krakow Scala User Group.