Machine Learning Essentials with Scala

This foundation-level hands-on course explores core skills and concepts in machine learning practices. You’ll learn machine learning concepts and algorithms from scratch. This includes the foundations, applicability and limitations, and an exploration of implementation and use.

    Nov 4 2020

    November 4 - 6, 2020 | 10:00 AM - 6:00 PM (EST) | Virtual Classroom Live

    Date: 11/04/2020 - 11/06/2020 (Wednesday - Friday) | 10:00 AM - 6:00 PM (EST)
    Location: ONLINE (Virtual Classroom Live)
    Delivery Format: VIRTUAL CLASSROOM LIVE Request Quote & Enroll

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    Machine Learning Essentials with Scala

    November 4 - 6, 2020 | 10:00 AM - 6:00 PM (EST) | Virtual Classroom Live


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    Join Our Email List?

    Dec 16 2020

    December 16 - 18, 2020 | 10:00 AM - 6:00 PM (EST) | Virtual Classroom Live

    Date: 12/16/2020 - 12/18/2020 (Wednesday - Friday) | 10:00 AM - 6:00 PM (EST)
    Location: ONLINE (Virtual Classroom Live)
    Delivery Format: VIRTUAL CLASSROOM LIVE Request Quote & Enroll

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    Machine Learning Essentials with Scala

    December 16 - 18, 2020 | 10:00 AM - 6:00 PM (EST) | Virtual Classroom Live


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    Join Our Email List?

Machine Learning (ML) Overview

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

Machine Learning Environment

  • Introduction to Jupyter notebooks/R-Studio

Machine Learning Concepts

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

Feature Engineering (FE)

  • Preparing data for ML
  • Extracting features and 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

Logistic Regression

  • Understanding Logistic Regression
  • Calculating Logistic Regression
  • Evaluating model performance

Classification: SVM (Supervised Vector Machines)

  • SVM concepts and theory
  • SVM with kernel

Classification: Decision Trees and Random Forests

  • Theory behind trees
  • Classification and Regression Trees (CART)
  • Random Forest concepts

Classification: Naive Bayes

  • Theory behind Naive Bayes
  • Running NB algorithm
  • Evaluating NB model

Clustering (K-Means)

  • Theory behind K-Means
  • Running K-Means algorithm
  • Estimating the performance

Principal Component Analysis (PCA)

  • Understanding PCA concepts
  • PCA applications
  • Running a PCA algorithm
  • Evaluating results

Recommendation (collaborative filtering)

  • Recommender systems overview
  • Collaborative Filtering concepts

Time Permitting: Capstone Project

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

Join an engaging hands-on learning environment, where you’ll learn:

  • Popular machine learning algorithms, their applicability and limitations
  • Practical application of these methods in a machine learning environment
  • Practical algorithm use cases and limitations

This course has a 50% hands-on labs to 50% lecture ratio with engaging instruction, demos, group discussions, labs, and project work.

Before attending this course, you should have:

  • Basic Scala programming skills
  • Good foundational mathematics in linear algebra and probability
  • Basic Linux skills
  • Familiarity with command line options such as ls, cd, cp, and su

This course is for intermediate skilled professional. This is not a basic class.

Experienced Developers, Data Analysts, and others interested in learning about machine learning algorithms and core concepts leveraging Scala.

This course is also offered in Python or R – please inquire for details.

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