What Is Programming? And How To Get Started
January 28, 2025
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This course is part of Machine Learning: Algorithms in the Real World Specialization
Instructor: Anna Koop
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(97 reviews)
(97 reviews)
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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.
We all know that data is important for machine learning success, but what does it really look like? What steps do you need to take to get from scattered, unprocessed data to nice clean learning data? This week takes an overarching view to describe how your problem and data needs interact, and what processes need to be in place for successful data preparation.
11 videos2 readings3 assignments
Now that you have your data sources identified, you need to bring it all together. This week describes what you need to prepare data overall.
11 videos4 assignments
Data is particular to a problem. This week we'll discuss how to turn generic data into successful fuel for specific machine learning projects.
8 videos2 readings3 assignments1 programming assignment1 ungraded lab
There are so many ways data can go wrong! This week discussed some of the pitfalls in data identification and processing.
9 videos4 assignments
We asked all learners to give feedback on our instructors based on the quality of their teaching style.
The Alberta Machine Intelligence Institute (Amii) is home to some of the world’s top talent in machine intelligence. We’re an Alberta-based research institute that pushes the bounds of academic knowledge and guides business understanding of artificial intelligence and machine learning.
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Reviewed on Oct 11, 2020
Some bugs in the assignment, but overall excellent discussion of how to avoid common pitfalls when using data for ML.
Reviewed on Jul 4, 2020
Good course, if you follow the previous ones and if you know some python (Pandas).
Reviewed on Dec 29, 2020
Excellent depth in coverage. Lab, although only one, was instructive to enable learning while also being exhaustive and intensive to drive learnings home.
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