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Data for Machine Learning
  • Level Professional
  • Duration 12 hours
  • Course by Alberta Machine Intelligence Institute
  • Offered by

About

This course is all about data and how it is critical to the success of your applied machine learning model. Completing this course will give learners the skills to: Understand the critical elements of data in the learning, training and operation phases Understand biases and sources of data Implement techniques to improve the generality of your model Explain the consequences of overfitting and identify mitigation measures Implement appropriate test and validation measures. Demonstrate how the accuracy of your model can be improved with thoughtful feature engineering. Explore the impact of the algorithm parameters on model strength To be successful in this course, you should have at least beginner-level background in Python programming (e.g., be able to read and code trace existing code, be comfortable with conditionals, loops, variables, lists, dictionaries and arrays). You should have a basic understanding of linear algebra (vector notation) and statistics (probability distributions and mean/median/mode). This is the third course of the Applied Machine Learning Specialization brought to you by Coursera and the Alberta Machine Intelligence Institute.

Modules

Know Your Problem

    • 4 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Business Understanding and Problem Discovery (BUPD) Review

4 Videos

  • Introduction to the Course
  • Business Understanding and Problem Discovery
  • No Free Lunch Theorem
  • Exploring the process of problem definition

1 Readings

  • Machine Learning Process Lifecycle Review

Know Your Data

    • 4 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Data Acquisition and Understanding Review

4 Videos

  • Data Acquisition and Understanding
  • Metadata Matters
  • Dealing with Multimodal Data
  • Features and transformations of raw data

Matchmaking

    • 3 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Module 1 Quiz

3 Videos

  • Identifying Data from Problem
  • Case Study: Problem from Data
  • Weekly Summary What does good data look like?

1 Readings

  • Match Data to the needs of the learning Algorithm

Consolidate Sources

    • 4 Videos
    • 1 Assignment
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1 Assignment

  • Data Warehousing Review

4 Videos

  • Data Warehousing
  • Converting to Useful Forms
  • Data Quality
  • How Much Data Do I Need?

Coordinate

    • 3 Videos
    • 2 Assignment
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2 Assignment

  • Everything has to be Numbers Review
  • Types of Data Review

3 Videos

  • Everything has to be Numbers
  • Types of Data
  • Aligning Similar Data

Clean & Complete

    • 4 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Module 2 Quiz

4 Videos

  • Imputing Missing Values
  • Data Transformations
  • Weekly Summary: Preparing your Data for Machine Learning Success
  • Data Cleaning: Everybody's favourite task

Understanding Features

    • 3 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Understanding Features

3 Videos

  • What are the simplest Features to try
  • Useful/Useless Features
  • How Many Features?

Building Good Features

    • 3 Videos
    • 1 Readings
    • 1 Assignment
Show info about module content

1 Assignment

  • Building Good Features

3 Videos

  • What is Unsupervised Learning
  • Feature Selection
  • Feature Extraction

1 Readings

  • Possibilities for Text Features

Transfer Learning

    • 2 Videos
    • 1 Readings
    • 1 Lab
    • 1 Programming
    • 1 Assignment
Show info about module content

1 Programming

  • Preparing Data: Grader

1 Assignment

  • Understanding Transfer Learning

1 Labs

  • Preparing Data

2 Videos

  • Transfer Learning
  • Weekly Summary: Feature Engineering for MORE Fun & Profit

1 Readings

  • Word Embeddings

Accept Limitations

    • 4 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Mistakes Computers Make

4 Videos

  • Imbalanced Data
  • Generalization and how machines actually learn
  • Bias in Data Sources
  • Bias and variance tradeoff

Statistical Nuance

    • 2 Videos
    • 1 Assignment
Show info about module content

1 Assignment

  • Data: Skewed Distributions

2 Videos

  • Outliers
  • Skewed Distributions

Consequences of Bad Data

    • 3 Videos
    • 2 Assignment
Show info about module content

2 Assignment

  • Live Data Dangers
  • Module 4 Quiz

3 Videos

  • Badness Multipliers
  • Live Data Danger
  • Weekly Summary: Bad Data

Auto Summary

Unlock the power of data with the "Data for Machine Learning" course, an in-depth exploration of how crucial data is to the success of applied machine learning models. Designed for those with a beginner-level grasp of Python, linear algebra, and statistics, this course will elevate your understanding and skills in data science and AI. Guided by the expertise of the Coursera and the Alberta Machine Intelligence Institute, learners will delve into: - The pivotal role of data throughout the learning, training, and operational phases of machine learning. - Identifying and mitigating biases and understanding diverse data sources. - Techniques to enhance the generality and accuracy of models through feature engineering and algorithm parameter tuning. - Addressing overfitting and implementing robust test and validation measures. Spanning 720 minutes, this professional-level course is part of the Applied Machine Learning Specialization, offering comprehensive insights to refine your machine learning prowess. Flexible subscription options, including Starter and Professional tiers, ensure you can learn at your own pace and convenience. Perfect for aspiring data scientists and AI enthusiasts ready to deepen their knowledge and application of machine learning.

Anna Koop
Instructor

Anna Koop