machine learning


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.


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.


Virtual Classroom Live Outline

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


Before attending this course, you should have:

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

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