Machine Learning Essentials with Python and Spark

Machine Learning Essentials with Python and Spark is a foundation-level, three-day hands-on course that teaches students core skills and concepts in modern ML at scale practices, leveraging Python and Spark. This course is geared for attendees new to machine learning who need introductory level coverage of these topics, rather than a deep dive of the math and statistics behind Machine Learning. Students will learn basic algorithms from scratch. For each machine learning concept, students will first learn about and discuss the foundations, its applicability and limitations, and then explore the implementation and use, reviewing and working with specific use cases.

Retail Price: $2,195.00

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Course Days: 3


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This program provides a structured, engaging learning experience for students to progressively attain the technical skills they require to engage in meaningful machine learning projects and activities, right after the training ends. Working in a hands-on learning environment, led by our Machine Learning expert instructor, students will learn about and explore:

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

 

Topics Covered: This is a high-level list of topics covered in this course. Please see the detailed Agenda below

  • Machine Learning (ML) Overview
  • Machine Learning in Python and Spark
  • 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)
  • Recommendations (Collaborative filtering)
  • Performance 
  • Time Permitting: Capstone Project

 

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.

 

Audience & Prerequisites

This course is geared for experienced, intermediate-skilled developers or others (with prior Python experience) intending to start using learning about and working with basic machine learning algorithms and concepts.  Attendees should be comfortable working with Python programming.  Students should also be able to navigate Linux command line, and who have basic knowledge of Linux editors (such as VI / nano) for editing code.

 

Pre-Requisites:  Students should have attended or have incoming skills equivalent to those in this course:

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

Outline 

Please note that this list of topics is based on our standard course offering, evolved from typical industry uses and trends. We’ll work with you to tune this course and level of coverage to target the skills you need most.

 

 

1.     Machine Learning (ML) Overview

·       Machine Learning landscape

·       Machine Learning applications

·       Understanding ML algorithms & models

 

2.     Machine Learning in Python and Spark

·       Spark ML Overview

·       Introduction to Jupyter notebooks

·       Working with Jupyter + Python + Spark

 

3.     Machine Learning Concepts

·       Statistics Primer

·       Covariance, Correlation, Covariance Matrix

·       Errors, Residuals

·       Overfitting / Underfitting

·       Cross-validation, bootstrapping

·       Confusion Matrix

·       ROC curve, Area Under Curve (AUC)

 

4.     Feature Engineering (FE)

·       Preparing data for ML

·       Extracting features, enhancing data

·       Data cleanup

·       Visualizing Data

·       Lab: data cleanup

·       Lab: visualizing data

 

 

5.     Linear regression

·       Simple Linear Regression

·       Multiple Linear Regression

·       Running LR

·       Evaluating LR model performance

·       Use case: House price estimates

 

6.     Logistic Regression

·       Understanding Logistic Regression

·       Calculating Logistic Regression

·       Evaluating model performance

·       Use case: credit card application, college admissions

 

7.     Classification: SVM (Supervised Vector Machines)

·       SVM concepts and theory

·       SVM with kernel

·       Use case: Customer churn data

 

8.     Classification: Decision Trees & Random Forests

·       Theory behind trees

·       Classification and Regression Trees (CART)

·       Random Forest concepts

·       Use case: predicting loan defaults, estimating election contributions

 

9.     Classification: Naive Bayes

·       Theory

·       Use case: spam filtering

 

10.   Clustering (K-Means)

·       Theory behind K-Means

·       Running K-Means algorithm

·       Estimating the performance

·       Use case: grouping cars data, grouping shopping data

 

11.   Principal Component Analysis (PCA)

·       Understanding PCA concepts

·       PCA applications

·       Running a PCA algorithm

·       Evaluating results

·       Use case: analyzing retail shopping data

 

12.   Recommendations (Collaborative filtering)

·       Recommender systems overview

·       Collaborative Filtering concepts

·       Use case: movie recommendations, music recommendations

 

13.   Performance 

·       Best practices for scaling and optimizing Apache Spark

·       Memory caching

·       Testing and validation

 

Time Permitting: Capstone Project

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

 



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