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Machine Learning with Python Big Data University

About This Course

This Machine Learning with Python course dives into the basics of machine learning using an approachable, and well-known, programming language. You'll learn about Supervised vs Unsupervised Learning, look into how Statistical Modeling relates to Machine Learning, and do a comparison of each.
Look at real-life examples of Machine learning and how it affects society in ways you may not have guessed!
Explore many algorithms and models:
  • Popular algorithms: Classification, Regression, Clustering, and Dimensional Reduction.
  • Popular models: Train/Test Split, Root Mean Squared Error, and Random Forests.
Get ready to do more learning than your machine!

Course Syllabus

Module 1 - Machine Learning vs Statistical Modeling & Supervised vs Unsupervised Learning
  • Machine Learning Languages, Types, and Examples
  • Machine Learning vs Statistical Modelling
  • Supervised vs Unsupervised Learning
  • Supervised Learning Classification
  • Unsupervised Learning
Module 2 - Supervised Learning I
  • K-Nearest Neighbors
  • Decision Trees
  • Random Forests
  • Reliability of Random Forests
  • Advantages & Disadvantages of Decision Trees
Module 3 - Supervised Learning II
  • Regression Algorithms
  • Model Evaluation
  • Model Evaluation: Overfitting & Underfitting
  • Understanding Different Evaluation Models
Module 4 - Unsupervised Learning
  • K-Means Clustering plus Advantages & Disadvantages
  • Hierarchical Clustering plus Advantages & Disadvantages
  • Measuring the Distances Between Clusters - Single Linkage Clustering
  • Measuring the Distances Between Clusters - Algorithms for Hierarchy Clustering
  • Density-Based Clustering
Module 5 - Dimensionality Reduction & Collaborative Filtering
  • Dimensionality Reduction: Feature Extraction & Selection
  • Collaborative Filtering & Its Challenges

Recommended skills prior to taking this course

  • None

Grading scheme

  • The minimum passing mark for the course is 60%, where the review questions are worth 40% and the final exam is worth 60% of the course mark.
  • You have 1 attempt to take the exam with multiple attempts per question.

Requirements

None.

Course Staff

Kevin Wong

Kevin Wong

Kevin Wong is a Technical Curriculum Developer. He enjoys developing courses that focuses on the education in the Big Data field. Kevin updates courses to be compatible with the newest software releases, recreates courses on the new cloud environment, and develops new courses such as Introduction to Machine Learning. In addition to contributing to the transition of BDU, he has worked with various components that deal with Big Data, including Hadoop, Pig, Hive, Phoenix, HBase, MapReduce & YARN, Sqoop and Oozie. Kevin is from the University of Alberta, where he has completed his third year of Computer Engineering Co-op.

Daniel Tran

Daniel Tran

Daniel Tran is an IBM Technical Curriculum Developer in Toronto, Ontario. He develops courses to improve the education of customers who seek knowledge in the Big Data field. He has also reworked previously developed courses, updating them to be compatible with the newest software releases, as well as work at the forefront of recreating courses on a newly developed cloud environment. He has worked with various components that deal with Big Data, including Hadoop, Pig, Hive, HBase, MapReduce & YARN, Sqoop, Oozie, and Apache Phoenix. He has also worked on separate courses involving Machine Learning. Daniel is from the University of Alberta, where he has completed his third year of traditional Computer Engineering Co-op.

  1. Course Number

    ML0101EN
  2. Classes Start

    Any Time, Self-Paced
  3. Estimated Effort

    3 hours
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