Machine Learning with Spark is a practical, hands-on training program designed to help data professionals and developers build and scale machine learning solutions using Apache Spark and its MLlib library. The course combines big data processing with machine learning techniques, enabling participants to work with large datasets and develop models that can be efficiently trained and applied in distributed computing environments.
Course Overview:
Machine Learning with Spark is an intermediate, hands-on training program designed to help data professionals develop and apply scalable machine learning solutions using Apache Spark and MLlib. The course combines the fundamentals of machine learning with Spark’s distributed data-processing capabilities, enabling participants to work with datasets that require efficient and scalable processing.
Participants will begin with a practical review of Apache Spark fundamentals, including reading and manipulating data with DataFrames and working with Spark’s machine learning libraries. They will then learn how to prepare data for machine learning by applying essential preprocessing techniques such as normalization, standardization, tokenization, and TF-IDF.
The course provides practical experience with key machine learning approaches, including clustering, classification, regression, and recommendation systems. Participants will build models using Spark MLlib algorithms such as K-Means, Naive Bayes, Decision Trees, Multilayer Perceptrons, Linear Regression, and Gradient-Boosted Trees.
A key focus of the training is building end-to-end machine learning pipelines with Spark. Learners will understand how different stages of data preparation, feature transformation, model training, and prediction can be combined into reusable workflows. Practical exercises will demonstrate how Spark can be applied to real-world problems such as text classification and recommendation systems.
Through guided demonstrations, coding exercises, and practical use cases, participants will gain experience in developing machine learning workflows that can handle larger datasets efficiently. The course emphasizes practical implementation rather than purely theoretical concepts, helping learners understand how Spark-based machine learning can support data analytics, predictive modeling, automation, and data-driven decision-making.
By the end of the training, participants will be able to prepare data, implement machine learning algorithms, develop Spark ML pipelines, and apply scalable machine learning techniques to real-world datasets using Apache Spark and MLlib.
Course Objectives:
- Overview of Apache Spark
- Clustering
- Regression
- Classification
- Recommendation
Pre-requisites:
Participants should have:
- Basic knowledge of Python programming
- Working familiarity with Apache Spark and DataFrames
- A basic understanding of machine learning concepts and terminology
- Basic knowledge of data analysis and data preparation
- No advanced mathematics or machine learning experience is required
Target Audience:
This intermediate-level course is suitable for professionals who work with data and want to develop practical skills in large-scale data processing and machine learning with Apache Spark, including:
- Data Scientists developing machine learning models and working with large datasets
- Data Analysts who want to expand their skills into predictive analytics and machine learning
- Big Data Analysts working with distributed data processing and large-scale datasets
- Machine Learning Engineers seeking practical experience with Spark MLlib
- Data Engineers interested in integrating machine learning into Spark-based data pipelines
- Business Intelligence and Analytics Professionals exploring scalable machine learning applications
- Software Developers and Technical Professionals working with Python and Apache Spark who want to apply machine learning techniques
- AI and Machine Learning Practitioners looking to strengthen their knowledge of distributed machine learning
Course Duration:
- 14 hours – 2 days
Course Content:
Module 1: Apache Spark Basics
- Recap of Apache Spark Basics
- Install Apache Spark on Local Computer
- Read CSV Data
- Manipulating Dataframe
- ML Libraries
Module 2: Preprocessing
- Normalizer
- Standardizer
- Tokenizer
- TF-IDF
Module 3: Clustering
- What is Clustering
- Clustering Algorithms
- KMeans Clustering
- Hierarchical Clustering
Module 4: Classification
- What is Classification
- Naives Bayes Clasiifier
- Decision Tree Classifer
- •Multi Layer Perception
Module 5: Regression
- What is Clustering
- Clustering Algorithms
- Linear Regression
- Decision Tree Regression
- Gradient Boosted Tree Regression
Module 6: ML Pipeline
- What is Pipeline
- Creating a Pipeline for Movie Review Classification
Module 7: Recommendation (Optional)
- Recommendation Systems
- Collaborative Filtering
- Summary and Closing Remarks
