Podcast
Questions and Answers
What does the function factor([1, 2, 3], [4, 5, 6]) return?
What does the function factor([1, 2, 3], [4, 5, 6]) return?
What is the result of executing a = df['width'] > 5, assuming 'width' contains integers in a DataFrame?
What is the result of executing a = df['width'] > 5, assuming 'width' contains integers in a DataFrame?
In Python's object-oriented programming, what action is generally needed to make an object's print representation informative about its attributes?
In Python's object-oriented programming, what action is generally needed to make an object's print representation informative about its attributes?
What change is required for a function to calculate 2N when N is an integer and N > 1?
What change is required for a function to calculate 2N when N is an integer and N > 1?
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What is a key difference between a linked list and a standard Python list?
What is a key difference between a linked list and a standard Python list?
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Which scenario indicates that overfitting may be occurring in a predictive model?
Which scenario indicates that overfitting may be occurring in a predictive model?
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What is the main advantage of embeddings over one-hot encodings in natural language processing?
What is the main advantage of embeddings over one-hot encodings in natural language processing?
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Which best describes the concept of 'refactoring' code?
Which best describes the concept of 'refactoring' code?
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Which data structure is most similar to the structure of a JSON file?
Which data structure is most similar to the structure of a JSON file?
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What does it mean when an object attribute starts with an underscore character?
What does it mean when an object attribute starts with an underscore character?
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What common mistake could lead a recursive function to run indefinitely?
What common mistake could lead a recursive function to run indefinitely?
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What underlying data structure does a Python list use?
What underlying data structure does a Python list use?
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What is the best practice for training and evaluating a machine learning system?
What is the best practice for training and evaluating a machine learning system?
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In which machine learning algorithm is scaling numerical features most important before feeding them to the algorithm?
In which machine learning algorithm is scaling numerical features most important before feeding them to the algorithm?
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What should be the cosine value between two word embedding vectors to provide strong evidence that the meanings of the words are similar?
What should be the cosine value between two word embedding vectors to provide strong evidence that the meanings of the words are similar?
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Why is it important to have separate training, validation, and test sets in machine learning model evaluation?
Why is it important to have separate training, validation, and test sets in machine learning model evaluation?
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What step should follow training on the training set in a machine learning system?
What step should follow training on the training set in a machine learning system?
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Study Notes
Machine Learning Best Practices
- Best practice for training and evaluating a machine learning system is to randomly choose examples for training, validation, and test sets, and evaluate on the test set after optimizing on the validation set.
Feature Scaling
- In K-nearest Neighbor algorithm, it is most important to scale numerical features before feeding them to the algorithm.
Word Embeddings
- A cosine of the angle between two word embedding vectors close to 1 is strong evidence that the meanings of the words are similar.
Python Functions
- The
factorfunction returns a list of lists, where each sublist contains the factors of the corresponding number in the input list. - The
factorfunction example:factor([1, 2, 3], [4, 5, 6])returns[[1, 2], [1, 5], [1, 2, 3]].
Pandas DataFrames
- When executing
a = df['width'] > 5, the resultais a Series with as many entries asdfhad rows, where all the values are Booleans.
Object-Oriented Programming
- To make an object print in a way that is informative about its attributes, you generally need to override the
str()method.
Recursion
- A recursive function needs a base case, returning 1 if
N == 0, to calculate 2N when N > 1 and N is an integer.
Data Structures
- A linked list is different from a standard Python list in that the next element is not adjacent in memory, but a linked list must follow a reference to get to the next element.
- A dictionary is the data structure that most closely resembles the structure of a JSON file and is also the data type returned by
json.load().
Overfitting
- Significantly higher accuracy on the training set than on the test set is good evidence that overfitting is happening.
Regression Methods
- Nearest neighbors regression is the least well-suited to giving us insight about the importance of the different features to the predictive model.
Embeddings in NLP
- Embeddings are an improvement over one-hot encodings in natural language processing because they allow learning about one word to affect the system's behavior toward words with similar meanings, even if those words have been seen infrequently or not at all in training.
Code Refactoring
- Refactoring is rewriting the code to be stylistically cleaner, for example, breaking large chunks of code into smaller functions.
Object Attributes
- When an object attribute has a name that starts with an underscore character, it is highly discouraged, but not impossible, to access the attribute directly from outside the class, and it would be preferable to use a method to manipulate it.
Recursive Function Mistakes
- A recursive function can run forever if the base case is omitted or the recursive call's argument is accidentally the same as the function argument, so the function gets no closer to termination.
Studying That Suits You
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Description
Test your knowledge on Python functions and DataFrame operations with these two questions. Identify the result of a given function call and understand the outcome of a specific DataFrame operation.
