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Introduction to Machine Learning and Data Mining
No longer a computer science technology niche, machine learning has emerged as the algorithmic power behind online shopping recommendations, credit card fraud detection, relevant social media content delivery, rideshare trip pricing, and traffic navigation. Companies are increasingly hiring skilled machine learning engineers to create products that parse, reduce, simplify, and categorize data, and then extract actionable intelligence from that data.
The goal of this course is to prepare you to work with practical data-driven applications. In this course you gain applicable knowledge on essential machine learning concepts, tools, and methodology, such as classical and modern algorithms that drive real-world applications—search engines, image analysis, biometrics, industrial automation, and market segmentation.
Starting with an intuitive introduction to the mathematics underlying machine learning, we will emphasize the practical use of machine learning algorithms using free Python programming libraries that have become the industry standard for developing ML applications.
We will focus on the connection between abstract concepts and working Python code in every lesson. Course materials and starter code will be available.
- Machine learning defined: Simple ML problems and intuitive solutions for supervised learning and Bayesian classifiers
- Bayesian classifiers: Probability density and an intro to the multivariate normal model
- Principal component analysis: Build classifiers using hundreds of features
- Linear algebra: The foundation for machine learning algorithms
- Linear classifiers: Common straightforward classifiers to use when data is scarce
- Evaluating machine learning models: Coding exercises to demonstrate the use of k-fold cross-validation in data-poor situations
- Unsupervised Learning: Using accumulated buying histories from a customer database to evaluate the quality of clustering results
- Natural language processing: How computer algorithms glean meaning and sentiment from written text and respond intelligently
- Neural networks and deep learning: Without using complex mathematics, learn how neural networks are trained (Tensorflow and Keras)
- Build your portfolio through a final class project researching an interesting machine learning problem of your choice.
* Skills needed: Familiarity with Python programming strongly recommended. Suggested: "Python for Machine Learning and Artificial Intelligence, Essentials" or, search these terms in Google: "Microsoft edX Introduction to Python for Data Science," for a free, self-study program that meets this course requirement.
Sections Open for Enrollment:
|Date:||Start Time:||End Time:||Meeting Type:||Location:|
|Thu, 04-08-2021||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
|Thu, 04-15-2021||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
|Thu, 04-22-2021||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
|Thu, 04-29-2021||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
|Thu, 05-06-2021||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
|Thu, 05-13-2021||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
|Thu, 05-20-2021||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
|Thu, 05-27-2021||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
|Thu, 06-03-2021||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
|Thu, 06-10-2021||6:30 p.m.||9:30 p.m.||Live-Online||ONLINE|
Ask us any questions you may have about this course.