Machine Learning with Apache Spark 3.0 using Scala
Why take this course?
Machine Learning with Apache Spark 3.0 using Scala with Examples and 4 Projects
🌟 Course Headline: Master Big Data & Machine Learning with Apache Spark 3.0 using Scala - Hands-On Project Experience!
Course Description:
Why This Course?
In this comprehensive course, you won't just be scratching the surface of Apache Spark and Machine Learning concepts. You'll dive deep with hands-on projects that will have you executing real-world applications on Databricks cloud computing services (Free Service). This course is designed to take you from a beginner to an advanced user in machine learning using Spark, with a focus on Scala – a modern, concise, and type-safe language that runs on the Java Virtual Machine.
What You'll Learn:
- Overview: Understand the capabilities of Apache Spark and its role in big data analytics.
- Spark ML Introduction: Get to know the machine learning library within Spark.
- Machine Learning Types: Explore different types of machine learning, including classification, regression, clustering, etc.
- Machine Learning Program Steps: Learn the steps involved in creating a machine learning program with Spark.
- Basic Statistics: Brush up on your stats to understand and implement machine learning algorithms effectively.
- Data Sources: Discover how to work with various data sources that are commonly used in machine learning projects.
- Pipelines: Understand the concept of pipelines for streamlining the machine learning process.
- Feature Transformation: Master the art of extracting, transforming, and selecting features that will significantly impact your model's performance.
- Classification & Regression: Implement classification and regression techniques to predict outcomes.
- Clustering: Learn clustering algorithms that can segment data into meaningful groups without predefined labels.
Hands-On Projects:
- Will it Rain Tomorrow in Australia? - Predict weather conditions using time-series data.
- Railway Train Arrival Delay Prediction - Analyze historical train arrival and departure times to predict delays.
- Iris Flower Classification - Categorize flowers based on their attributes using machine learning classification techniques.
- Mall Customer Segmentation (K-means) Cluster - Discover customer segments within shopping mall transaction data.
Getting Started:
To embark on this machine learning journey with Apache Spark 3.0 and Scala, you'll need a web browser (Google Chrome or Firefox, or Safari, or Microsoft Edge - the latest version) on Windows, Linux, or macOS desktop to access the online environment. This course is designed for hands-on learning within the Databricks environment.
Ready to dive into the realm of Apache Spark and Machine Learning with Scala? Enroll now and transform your data into actionable insights! 🚀
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