Applied Machine Learning

Supervised Learning

• Module 1: Regression

  Perform supervised learning with probabilistic models, linear regression, and maximum likelihood, use matrix operations to code least squares, and implement Ordinary Least Squares.

• Module 2: Ridge Regression

  Analyze the bias-variance tradeoff through least squares and ridge regression, apply the Bayes rule to quantify uncertainty, and use regression analysis to solve a real-world problem.

• Module 3: Bayesian Methods

  Understand “active learning,” the Lagrange multipliers tool, and the sparse regression model, identify MAP solutions, estimate covariants, and apply Bayesian linear regression.

• Module 4: Classification Algorithms

  Deal with classification using nearest neighbors, K-nearest neighbors, and the Bayes classifier, predict outcomes with supervised learning classification, and use the perceptron algorithm.

• Module 5: Logistic Regression, Kernel Methods, and Gaussian Processes

  Analyze logistic regression and its algorithm, learn about a kernel, kernelized perceptron, the Nadaraya-Watson regression model, and Gaussian processes.

• Module 6: Support Vector Machines and Decision Trees

  Plot hyperplanes with the maximum margin method, modify SVM, code decision tree-based classifiers to make predictions about variables, and organize grid search hyperparameters.

• Module 7: Boosting and K-Means Clustering

  Understand a bootstrap dataset and decision stump, learn about bagging and boosting techniques, identify characteristics of K-means tools, and use a label encoder.

Unsupervised Learning

• Module 8: Clustering Methods

  Code functions in Python implementing K-means, soft K-means, and Gaussian mixture model clustering to assign points and recalculate clusters.

• Module 9: Recommendation Systems

  Employ recommendation systems to predict a user score, compare LDA vs. PMF vs. NMF, translate a mathematical algorithm into code, and use SVD to make item recommendations.

• Module 10: Principal Component Analysis and Markov Models

  Use PCA to code a recommender system in Python, implement PCA using sklearn, prepare and scale data in preparation for PCA, and implement Markov chains using quantecon.

• Module 11: Hidden Markov Models and Kalman Filtering

  Make predictions about variables using the Hidden Markov Model, differentiate between PCAs, Markov models, and Gaussian models, and use forward/backward algorithm to solve HMM.

• Module 12: Association Analysis and Model Selection

  Apply association analysis to a dataset, model future stock prices using sequential methods, apply one-hot encoding to a data frame, and use the library Mlxtend for association analysis.

The course familiarizes you with Machine learning algorithms and applications. It will also help you understand the approach to a business problem and provide you with the tool knowledge needed to transition to a Machine Learning or a Data Science role.

The course familiarizes you with Machine learning algorithms and applications and provides a solid foundation in statistics/mathematics and problem-solving skills to help you solve enterprise-level problems. The Applied Machine Learning course augments your existing knowledge of various tools and expands your skill set as a Data Science or Machine Learning professional.

The course familiarizes you with Machine learning algorithms and applications while providing a solid foundation in statistics/mathematics and enhancing your business acumen. It augments your existing programming knowledge and expands the technologies you are familiar with, helping you further develop your skill set as a Data Science or Machine Learning professional.

Absolutely! Knowledge of Data Science and Machine Learning (ML) has quickly become a requisite across industries, and all businesses will eventually need to use these techniques to thrive. While your current role may not require Machine learning knowledge, it is almost certain that ML skills will be in high demand in most every industry in the future.

The course is a blend of theory, tools, and case studies (datasets) that are easy to assimilate and implement. For instance, students work on application projects that require them to apply the Machine Learning concepts they’ve learned to datasets and derive inferences. These application projects are intentionally made to be challenging, and students are expected to spend substantial time and effort solving them. At the end of the course, students will be able to apply Machine Learning to solve many of the business problems they face in their workplace.

Columbia Engineering Executive Education is collaborating with online education provider Emeritus to offer a portfolio of high-impact online courses. These courses leverage Columbia’s thought leadership in management practice developed over years of research, teaching, and practice.

Recommended System Requirements

• Processors: 2.60 GHz

• RAM: 8 GB of RAM

• Disk space: 2 to 3 GB

• Operating systems: Windows 10, MacOS and Linux

• Python download link

• Compatible tools: Any text editor, Command prompt

Minimum System Requirements

• Processors: 1 GHz

• RAM: 1 GB of RAM

• Disk space: 1 GB

• Operating systems: Windows 7 or later, MacOS and Linux

• Python versions: 2.7.X, 3.6.X

• Compatible tools: Any text editor, Command prompt

For the program refund and deferral policy, please click the link here.

Emeritus collects all program payments, provides learner enrollment and program support, and manages learning platform services.

Program fees for Emeritus programs with Columbia Engineering Executive Education may not be paid for with (a) funds from the GI Bill, the Post-9/11 Educational Assistance Act of 2008, or similar types of military education funding benefits or (b) Title IV financial aid funds.