Machine Learning Essentials

Upon completion of the Machine Learning Essentials course, students will be able to:

• 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

• Data Scientists and Software Engineers

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

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)