Practical Machine Learning

Once the purview of programmers and statisticians, machine learning has expanded across applications and disciplines into virtually every industry. And to keep pace with an increasingly data-driven marketplace, professionals of all stripes have set their sights on mastering its fundamentals.

UC Berkeley Extension’s Practical Machine Learning course offers a hands-on introduction to machine learning with R-programming that includes real-world datasets that let you solve problems in a variety of industries. Whether it’s business leaders aiming to improve their understanding of data science and machine learning to coordinate better with teams on tactical data-based initiatives, or aspiring data scientists seeking to master the practical aspects of problem framing and model deployment, the course addresses topics, tools, and techniques to help professionals from any background or industry enhance their machine learning skills.

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Duration and Course Fee

  • Starts TBD
  • 3 Months
  • 6 – 8 hours per week
  • Course Fees USD 1,400

 

Faculty

Christopher Brown Christopher Brown
Adjunct Professor in UC Berkeley’s Department of Computer Science and a Founding Partner of Decision Patterns
UC Berkeley Extension
Kristen Kehrer Kristen Kehrer
Course Instructor, EMERITUS
UC Berkeley Extension

Course Highlights

  • 116Video Lectures
  • 6Exercises / Assessments
  • 18R-Studio Demos
  • 5Assignments
  • 11Quizzes

SYllabus

  • Machine Learning – The What, The Why, and The How?
  • Machine Learning – Models and Functions
  • Model Training
  • Model Scoring
  • Classification of Machine Learning Algorithms
  • Three Features of Supervised ML Algorithms
  • Summary and Key Takeaways
  • Overview of Machine Learning Process
  • Business Problem Framing
  • Define the Final Delivery
  • Frame the Problem: App Store Example
  • Collect and Shape Data: Reading Data in R
  • Collect and Shape Data: Shaping Data in R
  • Exploratory Data Analysis
  • EDA in R
  • Fitting a simple model
  • Fitting a simple model in R
  • Linear Model Training in R
  • Limitations of a Linear Model
  • Linear Model Training in R (One Variable)
  • Linear Model Training in R (Multiple Variable)
  • Stepwise Regression
  • Stepwise Regression in R
  • Summary and Key Takeaways
  • Introduction to Classification (Categorical Modeling)
  • Building from Linear Regression
  • Loss Function
  • Unconstrained Errors
  • Logistic Function
  • Use of the logistic
  • Probability, Odds, and Log-odds
  • Training a Logistic Regression Mode
  • Stepwise Regression
  • Variable Importance
  • Multinomial Logistic Regression
  • Summary and key Takeaways
  • Introduction to Model Evaluation
  • Class Separation Plot
  • Cutoff
  • Confusion Matrix
  • Evaluating Models Using Confusion Matrix
  • Nomenclature of Binomial Metrics
  • ROC Curves
  • Default ROC Curve
  • Generalizing ROC Curve
  • Value matrix and curve
  • Class Imbalance
  • Cohen’s Kappa
  • Summary and Key Takeaways
  • Introduction to Decision Trees
  • Recursive Partitioning
  • Partitioning Scoring
  • Splitting Continuous Variables
  • Splitting Categorical Variables
  • How to Evaluate Best Split?
  • Measures if Homogeneity
  • Evaluating Splits Using Model Performance
  • Finding the Best Split
  • Stopping Criteria
  • Rules for Partitioning
  • Handling Missing Data
  • Tree Method: Advantages and Disadvantages
  • RP Classification & Regression Examples – coding example
  • Wrap-up
  • Intro to Resampling
  • Variance-bias Tradeoff
  • Training and Testing Model Performance
  • Resampling
  • Resampling Methods
  • Resampling Process and Practice
  • Tuning Parameter Optimization
  • Caret Package in R
  • Model Validation
  • Tidyverse Coding Example
  • Wrap-up
  • Introduction to Model Improvements
  • Model Ensembles
  • Bagging Models
  • Random Forests
  • Using Random Forest – Coding Example
  • Generalized Ensembles and Model Stacking
  • Boosting
  • Simple Boosting
  • Comparison with CART
  • Gradient Boosting
  • Weak Learners
  • Local Minimums vs Global Minimums
  • Stochastic Gradient Boosting Machines
  • Stochastic GBMs – Coding Example
  • Wrap-up
  • Introduction to Neural Networks
  • Analogy to Brain Function
  • From Neurons to Neural Networks
  • Single-Layer Feed Forward Networks
  • Solutions to Overfitting
  • MNIST Handwriting Recognition
  • MNIST Coding Example
  • Back Propagation
  • Deep Learning
  • Conclusion
  • Introduction to Unsupervised Learning
  • Principal Components Analysis (PCA)
  • PCA Coding Example
  • Clustering
  • K-means
  • K-means Coding Example
  • Hierarchical Clustering (HC)
  • HC Coding Example
  • Clustering Code Example
  • Association Rules
  • Association Rules Coding Example
  • Semi-Supervised Learning
  • Wrap-up
  • Introduction to Model Deployment
  • Model Development: Roles and Success Factors
  • Data Science Project Life Cycle
  • Creating a Problem Statement
  • Deployment Assets
  • Deployment Patterns
  • Agile Process for Deployment
  • Managing Environments and Assets
  • Wrap-up
  • Introduction to Problem Framing
  • Recommender Systems 1: Problem Framing
  • Recommender Systems 2: Debrief and Approaches
  • Recommender Systems 3: Walkthrough with Coding hints
  • Customer Lifetime Value 1: Problem Framing
  • Customer Lifetime Value 2: Debrief and Approaches
  • Customer Lifetime Value 3: Walkthrough with Coding hints
  • Deployment and Performance Considerations

PREREQUISITES: The course requires an undergraduate knowledge of statistics (descriptive statistics, regression, sampling distributions, hypothesis testing, interval estimation etc.) and probability. Familiarity with R (importing a data set, assigning variables, working with a variety of data structures like. numeric, character, factor etc., creating and adding columns to data frames) is required.

Assignments /application projects which require programming will be done using the R programming language.

Application Projects

Price Predictions Using App Store Data

Price Predictions Using App Store Data

Participants will perform an exploratory data analysis (EDA) and build a univariate or multivariate linear regression model using data from Apple’s app store.

Predicting Probability of Credit Default

Predicting Probability of Credit Default

Participants will apply logistic regression to a dataset including features on credit card users and develop a model predicting the probability of default payments based upon previous payment history, bill amount, and customer demographics.

Predicting Probability of Employee Attrition

Predicting Probability of Employee Attrition

Participants will examine classification problems and apply what they have learned to an employee attrition data set in order to make predictions about the probability of an employee leaving his/her company.

Neural Nets: Determining Standard vs. Premium Service Levels

Neural Nets: Determining Standard vs. Premium Service Levels

Participants will get an introduction to neural networks and make predictions based upon a dataset with information on office supply purchases.

Customer Segmentation with Clustering

Customer Segmentation with Clustering

Participants will get an introduction to unsupervised learning algorithmic techniques, such as K-means and hierarchical clustering. and employ clustering techniques to develop segments from customer data.

Problem Framing: Recommendation Engines and Customer Lifetime Value

Problem Framing: Recommendation Engines and Customer Lifetime Value

Participants will walk through practical examples of problem framing and identify approaches to modeling customer lifetime value and develop recommendation engines.

BENEFITS TO THE LEARNER

Intellectual Capital

Intellectual Capital

  • Global Business Education
  • Rigorous and experiential curriculum
  • World-renowned faculty
  • Globally Connected Classroom: Peer to Peer Learning Circles
  • Action Learning: Learning by Doing

Brand-Capital

Brand Capital

  • Certificate from EMERITUS in collaboration with UC Berkeley Extension

Social-Capital

Social Capital

  • Build new networks through peer interaction
  • Benefit from diverse class profiles

Career-Capital

Career Capital

  • Professional Acceleration through our enriched leadership toolkit
  • Learn while you earn
  • Get noticed. Get ahead.

Duration and Course Fee

  • Starts TBD
  • 3 Months
  • 6 – 8 hours per week
  • Course Fees USD 1,400

 

Faculty

Christopher Brown Christopher Brown
Adjunct Professor in UC Berkeley’s Department of Computer Science and a Founding Partner of Decision PatternsUC Berkeley Extension

 

Kristen Kehrer Kristen Kehrer
Course Instructor, EMERITUSUC Berkeley Extension

GET PROGRAM INFO

Take the first step to a Global Education

Your details will not be shared with third parties. Privacy Policy

Duration and Course Fee

  • Starts TBD
  • 3 Months
  • 6 – 8 hours per week
  • Course Fees USD 1,400

 

Faculty

Christopher Brown Christopher Brown
Adjunct Professor in UC Berkeley’s Department of Computer Science and a Founding Partner of Decision Patterns
UC Berkeley Extension
Kristen Kehrer Kristen Kehrer
Course Instructor, EMERITUS
UC Berkeley Extension

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