Machine Learning Essentials with Scala

Course Objectives

• Machine Learning (ML) Overview
• Machine Learning Environment
• Machine Learning Concepts
• Feature Engineering (FE)
• Linear regression
• Logistic Regression
• Classification : SVM (Supervised Vector Machines)
• Classification : Decision Trees & Random Forests
• Classification : Naive Bayes
• Clustering (K-Means)
• Principal Component Analysis (PCA)
• Recommendation (Collaborative filtering)

Need different skills or topics?  If your team requires different topics or tools, additional skills or custom approach, this course may be easily adjusted to accommodate.  We offer additional related Machine Learning, AI, Deep Learning, data science, programming (Python, R, Java, Scala etc.) and development courses which may be blended with this course for a track that best suits your learning objectives. Our team will collaborate with you to understand your needs and will target the course to focus on your specific learning objectives and goals.

Course Prerequisites

This in an introductory-level course is geared for experienced Scala developers intending to start using learning about and working with basic machine learning algorithms and concepts.

Pre-Requisites:  Students should have

• Basic Scala programming skills.  Attendees without Scala programming background may view labs as follow along exercises or team with others to complete them. (NOTE: This course is also offered in Python or R – please inquire for details)
• Good foundational mathematics skills in Linear Algebra and Probability, to start learning about and using basic machine learning algorithms and concepts
• Basic Linux skills, including familiarity with command-line options such as ls, cd, cp, and su

Please see the Related Courses tab for specific Pre-Requisite courses, Related Courses that offer similar skills or topics, and next-step Learning Path recommendations.

Course Agenda

Machine Learning (ML) Overview

• Machine Learning landscape
• Machine Learning applications
• Understanding ML algorithms & models (supervised and unsupervised)

Machine Learning Environment

• Introduction to Jupyter notebooks / R-Studio
• Exercise: Getting familiar with ML environment

Machine Learning Concepts

• Statistics Primer
• Covariance, Correlation, Covariance Matrix
• Errors, Residuals
• Overfitting / Underfitting
• Cross validation, bootstrapping
• Confusion Matrix
• ROC curve, Area Under Curve (AUC)
• Exercise: Working with Basic Statistics

Feature Engineering (FE)

• Preparing data for ML
• Extracting features, enhancing data
• Data cleanup
• Visualizing Data
• Exercise: data cleanup
• Exercise: visualizing data

Linear regression

• Simple Linear Regression
• Multiple Linear Regression
• Running LR
• Evaluating LR model performance
• Exercise / Use case: House price estimates

Logistic Regression

• Understanding Logistic Regression
• Calculating Logistic Regression
• Evaluating model performance
• Use case: credit card application, college admissions

Classification : SVM (Supervised Vector Machines)

• SVM concepts and theory
• SVM with kernel
• Use case: Customer churn data

Classification : Decision Trees & Random Forests

• Theory behind trees
• Classification and Regression Trees (CART)
• Random Forest concepts
• Exercise / Use case: predicting loan defaults, estimating election contributions

Classification : Naive Bayes

• Theory behind Naive Bayes
• Running NB algorithm
• Evaluating NB model
• Exercise / Use case: spam filtering

Clustering (K-Means)

• Theory behind K-Means
• Running K-Means algorithm
• Estimating the performance
• Exercise / Use case: grouping cars data, grouping shopping data

Principal Component Analysis (PCA)

• Understanding PCA concepts
• PCA applications
• Running a PCA algorithm
• Evaluating results
• Exercise / Use case: analyzing retail shopping data

Recommendation (Collaborative filtering)

• Recommender systems overview
• Collaborative Filtering concepts
• Use case: movie recommendations, music recommendations

Time Permitting: Capstone Project

• Hands-on guided workshop utilizing skills learned throughout the course

Course Materials

Each student will receive a Student Guide with course notes, code samples, setp-by-step written lab instructions, software tutorials, diagrams and related reference materials and links (as applicable). Students will also receive related (as applicable) project files, code files, data sets and solutions required for any hands-on work.

Lab Setup Made Simple.   All course labs and solutions, data sets, software, detailed courseware, lab guides and resources (as applicable) are provided for attendees in our easy access, no installation required, remote lab environment. Our tech team will help set up, test and verify lab access for each attendee prior to the course start date, ensuring a smooth start to class and successful hands-on course experience for all participants.