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Course Skill Level:


Course Duration:

3 day/s

  • Course Delivery Format:

    Live, instructor-led.

  • Course Category:

    Big Data & Data Science

  • Course Code:


Who should attend & recommended skills:

Data Scientists and Software Engineers with basic programming, machine learning, and R, Python or Apache Spark skills

Who should attend & recommended skills

  • Data Scientists and Software Engineers
  • R, Python or Apache Spark: Basic (1-2 years’ experience)
  • Programming: Basic (1-2 years’ experience)
  • Machine Learning: Basic (1-2 years’ experience)

About this course

This course introduces popular Machine Learning techniques. We teach popular Machine Learning algorithms from scratch.
For each machine learning concept, we first discuss the foundations, its applicability and limitations. Then we explain the implementation and use, and specific use cases. This is achieved through a combination of about 50% lecture, 50% lab work. Please note that this course does not cover in-depth coverage of the Math / Stats behind Machine Learning.

Skills acquired & topics covered

  • Learn popular machine learning algorithms, their applicability and limitations
  • Practice the application of these methods in a machine learning environment
  • Learn practical use cases and limitations of algorithms

Course breakdown / modules

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

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

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

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

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

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

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

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

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

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

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

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

  • Group exercise: Students will analyze a couple of datasets and run ML algorithms.
  • Each group will present their findings to the class.