BECOME AN EXPERT IN MACHINE LEARNING

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What is the Applied Machine Learning course about?

This course teaches you a wide-ranging set of techniques of supervised and unsupervised machine learning approaches using Python as the programming language.

This course is appropriate for you, if you are looking to implementing or leading a machine learning project in your organization or developing custom ML functions to incorporate in your application.

Learning outcomes

At the end of this course, you will be able to

Explain the concepts behind many popular supervised and unsupervised machine learning functions
Select the most appropriate machine learning approach (supervised vs unsupervised) for a given set of data or a business context
Develop and optimize your own machine learning algorithms using Python programming language
Create a predictive algorithms to predict the price of a house, the survival chance of a voyager aboard the sinking Titanic. the chances of loan default and many more.

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Your Learning Journey

Faculty Video Lectures

Quizzes / Assignments

Q&A Sessions with Course Leaders

Moderated Discussion Boards

Application
Assignments

Live Online Teaching

Supervised Learning
Module 1: Regression
Maximum Likelihood, Least Squares, Regularization
Module 2: Bayesian Methods
Bayes Rule, MAP Inference, Active Learning
Module 3: Foundational Classification Algorithms
Nearest Neighbors, Perceptron, Logistic Regression
Module 4: Refinements to Classification
Kernel Methods, Gaussian Process
Module 5: Intermediate Classification Algorithms
SVM, Trees, Forests and Boosting

Unsupervised Learning
Module 6: Clustering Methods
K-Means Clustering, E-M, Gaussian Mixtures
Module 7: Recommendation Systems
Collaborative Filtering, Topic Modeling, PCA
Module 8: Sequential Data Models
Markov and Hidden Markov Models, Kalman Filters
Module 9: Association Analysis
Module 10: Model Selection
Model Comparisons, Analysis Considerations

PREREQUISITES:
● The course requires an undergraduate knowledge of statistics (descriptive statistics, regression, sampling distributions, hypothesis testing, interval estimation, etc.), calculus, linear algebra, and probability.

● All assignments/application projects will be done using the Python programming language. You should have an intermediate knowledge of Python or you should have completed the Emeritus Python for Data Science course prior to joining this course.

All assignments and application projects will be done using the Python programming language.

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Application Assignments

Movie Recommendation Engine

You will build a movie recommendation engine by applying collaborative filtering and topic modelling techniques. You use a dataset which contains 20 million viewer ratings of 27,000 movies.

House Price Prediction

You will write code to predict house prices based on several parameters available in the Ames City dataset compiled by Dean De Cock using least squares linear regression and Bayesian linear regression.

Human Activity Prediction

You will predict the human activity (walking, sitting, standing) that corresponds to the accelerometer and gyroscope measurements by applying the nearest neighbours technique.

Credit Card Fraud Detection

You will detect potential frauds using credit card transaction data. You will apply the random forest method to identify fraudulent transactions.

Marketing Segmentation

You will create market segments using the US Census dataset and by applying the k-means clustering method.

Note: All product and company names are trademarks™ or registered® trademarks of their respective holders. Use of them does not imply any affiliation with or endorsement by them.

Faculty

John W. Paisley

Columbia University Associate Professor, Electrical Engineering
Affiliated Member, Data Sciences Institute

John has a PhD from Duke and has been a postdoctoral researcher in the Computer Science departments at Princeton University and UC Berkeley. John Paisley’s research focuses on developing models for large-scale text and image processing applications. He is particularly interested in Bayesian models and posterior inference techniques that address the big data problem.

Applied Machine Learning

BECOME AN EXPERT IN MACHINE LEARNING

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Flexible payment available

What is the Applied Machine Learning course about?

Learning outcomes