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Machine Learning Essentials

  • Course Code: Data Science - Machine Learning Essentials
  • Course Dates: Contact us to schedule.
  • Course Category: Big Data & Data Science Duration: 3 Days Audience: Data Scientists and Software Engineers

This course introduces popular Machine Learning techniques. 

This course is intended for data scientists and software engineers.  
We assume no previous knowledge of Machine Learning. 
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 Math / Stats is behind Machine Learning. 

This course is taught using one the following environments 

  1. Python 
  2. Spark & Python 

Duration : 3 days 

Audience : Data Scientists and Software Engineers 

Prerequisites : 

  • Working knowledge of either R, Python or Apache Spark 
  • Programming background 
  • No previous machine learning knowledge is assumed 

Objectives : 

  • 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 

Lab environment: 

Lab environment will be provided for students.  Students would only need an SSH client and a browse. 

Zero Install : There is no need to install software on students’ machines. 

Course Outline: 

Section 1: Machine Learning (ML) Overview 

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

Section 2: Machine Learning Environment 

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

Section 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) 
  • Lab: Basic stats 

Section 4: Feature Engineering (FE) 

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

Section 5: Linear regression 

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

Section 6: Logistic Regression 

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

Section 7: Classification : SVM (Supervised Vector Machines) 

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

Section 8: Classification : Decision Trees & Random Forests 

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

Section 9: Classification : Naive Bayes 

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

Section 10: Clustering (K-Means) 

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

Section 11: Principal Component Analysis (PCA) 

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

Section 12: Recommendation (Collaborative filtering) 

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

Section 13: Final workshop (time permitting) 

Students will analyze a couple of datasets and run ML algorithms. 
This is done as a group exercise.  Each group will present their findings to the class. 

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