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Introduction to Machine Learning and Data Mining

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Starts Sep 25
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About This Course

Machine learning and data mining are at the center of a powerful movement driving the tech industry. Companies depend on practitioners of machine learning to create products that parse, reduce, simplify, and categorize data, and then extract actionable intelligence from that data. When you know machine learning, a key technology driving Big Data, you secure a competitive edge in exciting careers in the data sciences.

In this course, you will learn machine learning concepts, terms and methodology. You will gain an intuitive understanding of the mathematics underlying it by understanding how actual applications are built. You will understand how these algorithms drive real-world applications such as search engines, image analysis, biometrics, industrial automation, and market segmentation.

We will establish a basic understanding of supervised learning and Bayesian classifiers using the histogram as a starting point, before exploring the design and application of practical and useful classifiers such as linear machines and decision trees. You will learn concepts in unsupervised learning and clustering algorithms such as expectation maximization and k-means clustering. The course concludes with the application of neural networks in machine learning.

Using examples to guide you through foundational concepts and pseudocode, you will have the opportunity to employ live algorithms to facilitate visual understanding. You are also encouraged to use the pseudocode as a reference to create your own programs. In-class quizzes and group activities and discussion, will help you gauge learning. Homework assignments are designed for in-depth practice.

Topics include:

  • Histograms and Bayesian classifiers
  • Principal component analysis
  • Linear classifiers and regression
  • Classifier performance evaluation
  • Expectation maximization algorithm
  • K-Means algorithm
  • Hidden Markov models
  • Ensemble learning and Decision trees
  • Neural networks

Prerequisite Skills:

If you are new to programming and wish to learn Python by enrolling in a course, we suggest "Python Programming for Beginners" or "Python for Data Analysis".

For a free, self-study program that meets this course requirement, use the keywords "Microsoft edX Introduction to Python for Data Science" in a search engine.

Learning Outcomes

At the conclusion of the course, you should be able to:

  • Describe Use cases and translate problems into Machine Learning Framework
  • Understand ML and AI concepts, algorithms and their mathematical foundation,
  • Implement ML models in python
  • Train models, test accuracy/performance, communicate trade offs and solve real world problems

Skills You'll Gain

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