Data for Machine Learning
- Offered byCoursera
Data for Machine Learning at Coursera Overview
Duration | 12 hours |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Data for Machine Learning at Coursera Highlights
- This Course Plus the Full Specialization.
- Shareable Certificates.
- Graded Programming Assignments.
Data for Machine Learning at Coursera Course details
- 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.
Data for Machine Learning at Coursera Curriculum
What Does Good Data look like?
Introduction to the Course
Business Understanding and Problem Discovery
No Free Lunch Theorem
Exploring the process of problem definition
Data Acquisition and Understanding
Metadata Matters
Dealing with Multimodal Data
Features and transformations of raw data
Identifying Data from Problem
Case Study: Problem from Data
Weekly Summary What does good data look like?
Machine Learning Process Lifecycle Review
Match Data to the needs of the learning Algorithm
Business Understanding and Problem Discovery (BUPD) Review
Data Acquisition and Understanding Review
Module 1 Quiz
Preparing your Data for Machine Learning Success
Data Warehousing
Converting to Useful Forms
Data Quality
How Much Data Do I Need?
Everything has to be Numbers
Types of Data
Aligning Similar Data
Imputing Missing Values
Data Transformations
Weekly Summary: Preparing your Data for Machine Learning Success
Data Cleaning: Everybody's favourite task
Data Warehousing Review
Everything has to be Numbers Review
Types of Data Review
Module 2 Quiz
Feature Engineering for MORE Fun & Profit
What are the simplest Features to try
Useful/Useless Features
How Many Features?
What is Unsupervised Learning
Feature Selection
Feature Extraction
Transfer Learning
Weekly Summary: Feature Engineering for MORE Fun & Profit
Possibilities for Text Features
Word Embeddings
Understanding Features
Building Good Features
Understanding Transfer Learning
Bad Data
Imbalanced Data
Generalization and how machines actually learn
Bias in Data Sources
Bias and variance tradeoff
Outliers
Skewed Distributions
Badness Multipliers
Live Data Danger
Weekly Summary: Bad Data
Mistakes Computers Make
Data: Skewed Distributions
Live Data Dangers
Module 4 Quiz
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