What you'll get
- 4+ Hours
- 1 Courses
- Course Completion Certificates
- Self-paced Courses
- Technical Support
- Case Studies
- Download Curriculum
Synopsis
- Learn to organize and utilize large datasets effectively.
- Understand how to uncover patterns, trends, and relationships in data.
- Gain skills to cluster, classify, and categorize data for actionable insights.
- Explore Mahout's machine learning capabilities, including collaborative filtering, classification, and clustering.
- Work with Mahout Samsara for scalable, distributed machine learning operations on Spark.
- Learn to build intelligent applications using commercial-friendly ML algorithms.
- Apply data structures, linear algebra, and statistical operations to real-world datasets.
- Hands-on experience with algorithms for enhancing data organization and analysis.
Content
| Courses | No. of Hours | Certificates | Details |
|---|---|---|---|
| Apache Mahout | 3h 5m | ✔ | View Curriculum |
Description
Apache Mahout is a leading open-source platform for scalable machine learning, designed to efficiently process large datasets. Originally co-founded by Grant Ingersoll, Mahout provides a suite of algorithms for clustering, classification, and collaborative filtering, making it ideal for building intelligent applications that require automated recommendations or content organization. Mahout Samsara, the core of this ecosystem, supports advanced linear algebra, statistical operations, and custom algorithm development in distributed computing environments like Spark.
This course covers both foundational and advanced aspects of Mahout, including setting up the environment, implementing scalable machine learning algorithms, and integrating Mahout with Hadoop and other big data frameworks. Participants will explore practical applications, including document clustering, filtering, and real-time data analysis. The course emphasizes hands-on projects that help learners apply Mahout in real-world scenarios, enabling them to improve data utilization and generate actionable insights efficiently.
Sample Certificate
Requirements
- Strong coding and development skills.
- Analytical skills for data interpretation and algorithm application.
- Knowledge of Hadoop, Spark, Flink, Kafka, or other real-time processing tools.
- Understanding of machine learning concepts and algorithms.
- JDK installed along with a build tool such as Maven or Ant.
- Basic computer science knowledge and eagerness to learn.
Target Audience
- Developers, software engineers, and big data engineers.
- Data scientists, analysts, and consultants working on machine learning tasks.
- Web developers and IT professionals interested in intelligent applications.
- Students seeking hands-on experience with machine learning.
- Entrepreneurs or professionals planning to implement Mahout-based solutions.
- Anyone pursuing roles that require data-driven insights, such as in meteorology or geology.
