Master Machine Learning with Apache Spark 3.0 & Scala: 4 Real-World Projects

What you will learn:

Master Apache Spark 3.0 and Scala for machine learning.
Build and deploy scalable machine learning models.
Gain practical experience with four real-world projects.
Utilize Databricks cloud computing services.
Learn data preprocessing, model training, and evaluation.
Master core machine learning concepts (supervised, unsupervised).
Integrate machine learning workflows into big data pipelines.
Understand and apply feature engineering techniques.
Work with diverse data formats and sources.
Develop robust and deployable AI solutions.

Description

Unlock the power of large-scale machine learning with our comprehensive course.

Learn to leverage Apache Spark 3.0 and Scala to build and deploy sophisticated models on massive datasets. This course is designed for data scientists, big data professionals, and developers seeking to enhance their analytics capabilities and future-proof their careers. Demand for big data professionals skilled in Apache Spark is soaring, with companies like Amazon, eBay, and NASA leading the charge.

This isn't just theory – you'll work through four real-world projects using the industry-standard Databricks platform (free service), gaining hands-on experience with data preprocessing, model implementation, and evaluation. From predicting rainfall in Australia to segmenting mall customers, you'll tackle diverse problems and master critical techniques.

What you will master:

Core Machine Learning Concepts: Supervised, unsupervised, and recommendation algorithms with practical application.
Spark MLlib Expertise: Data preprocessing, model training, and optimization on large datasets.
Big Data Integration: Seamless integration of ML workflows into big data pipelines for optimal efficiency.
Scalable AI Solutions: Build robust, deployable AI solutions for real-world scenarios.
Databricks Proficiency: Utilize the power of Databricks cloud computing for efficient development and deployment.

Course Highlights:

Four comprehensive, real-world projects.
Detailed instruction on Spark MLlib and its core components.
Hands-on exercises and practical examples in Scala.
Step-by-step guidance throughout the entire learning process.
Access to the Databricks platform for practical experience.

Who Should Enroll?

Data scientists, machine learning engineers, big data professionals, and developers wanting to advance their skills and build a high-demand skill set. Don't miss out – enroll now!

Curriculum

Introduction

This introductory section lays the groundwork for the course. Lectures cover a general introduction to the course, an overview of the concepts, what Spark ML is, a dive into the fundamentals of machine learning, and finally, tips for a successful learning experience. These foundational videos set the stage for the more advanced topics to follow.

Apache Spark Basics (Optional)

This optional section provides a comprehensive introduction to Apache Spark, covering key concepts like RDDs and DataFrames with practical examples. It guides you through creating a free Databricks account, provisioning a Spark cluster, and understanding the basics of notebooks. Lectures also cover anonymous functions in Scala and offer supplemental material for a deeper understanding of Spark DataFrames and Datasets. This section is valuable for students new to Spark.

Apache Spark Machine Learning

The core of the course, this section delves into the world of machine learning using Apache Spark. You'll explore types of machine learning, the steps involved in building a program, and a deep dive into Spark MLlib. You'll learn about various data sources and how to work with them including CSV, JSON, LIBSVM, image, Arvo, and Parquet files. The section covers building data pipelines, extracting, transforming, and selecting features using techniques like TF-IDF, Word2Vec, CountVectorizer, etc. Practical projects like rainfall prediction, railway delay prediction, Iris flower classification, and mall customer segmentation using k-means clustering provide hands-on experience with classification and regression models (Decision Trees, Logistic Regression, Naive Bayes, Random Forest, Gradient-boosted Trees, Support Vector Machines), showcasing both supervised and unsupervised learning. Finally, extra lectures provide insights into model evaluation and additional techniques.

Download Resources

This final section contains downloadable resources to support your learning and bonus lectures offering extra value and insight beyond the core curriculum.

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