Machine Learning Python Programming Syllabus

Python Programming syllabus — full chapter and topic list

Expand any chapter to see its topics and sub-topics. This is the whole examinable outline for Python Programming in Machine Learning, not a summary of it.

1. 1

   Introduction to Python

   5 topics

   • Python Basics
     • Syntax and Semantics
     • Variables and Data Types
     • Operators and Expressions
     • Control Flow Statements
     • Functions and Modules
   • Data Structures
     • Lists
     • Tuples
     • Dictionaries
     • Sets
   • File Handling
     • Reading from Files
     • Writing to Files
     • Working with CSV Files
   • Error and Exception Handling
     • Try and Except Blocks
     • Finally Clause
     • Custom Exceptions
   • Object-Oriented Programming
     • Classes and Objects
     • Inheritance
     • Polymorphism
     • Encapsulation
2. 2

   Python for Data Analysis

   4 topics

   • NumPy
     • Arrays and Matrices
     • Array Operations
     • Statistical Functions
   • Pandas
     • Series and DataFrames
     • Data Cleaning
     • Data Manipulation
     • Merging and Joining DataFrames
   • Matplotlib
     • Basic Plotting
     • Customizing Plots
     • Subplots
     • 3D Plotting
   • Seaborn
     • Statistical Plots
     • Categorical Plots
     • Matrix Plots
3. 3

   Machine Learning with Python

   5 topics

   • Scikit-Learn
     • Data Preprocessing
     • Supervised Learning
     • Unsupervised Learning
     • Model Evaluation
     • Hyperparameter Tuning
   • Regression
     • Linear Regression
     • Polynomial Regression
     • Ridge and Lasso Regression
   • Classification
     • Logistic Regression
     • K-Nearest Neighbors
     • Support Vector Machines
     • Decision Trees and Random Forests
   • Clustering
     • K-Means Clustering
     • Hierarchical Clustering
     • DBSCAN
   • Dimensionality Reduction
     • Principal Component Analysis (PCA)
     • Linear Discriminant Analysis (LDA)
4. 4

   Deep Learning with Python

   3 topics

   • Introduction to Neural Networks
     • Perceptrons
     • Activation Functions
     • Forward and Backward Propagation
   • TensorFlow
     • Tensors and Operations
     • Building Neural Networks
     • Training and Evaluation
   • Keras
     • Sequential and Functional API
     • Building and Compiling Models
     • Callbacks and Checkpoints

Python Programming flashcards for Machine Learning

25 of 60 cards from the Python Programming deck — real questions with worked answers.

1. In Python, what is the difference between a list and a tuple?

   A list is mutable and defined with square brackets, e.g. [1, 2, 3], so its elements can be changed, added, or removed. A tuple is immutable and defined with parentheses, e.g. (1, 2, 3), so it cannot be modified after creation, making it usable as a dictionary key.

2. What are Python's four built-in numeric/sequence-related core data structures and their key property?

   List (ordered, mutable), Tuple (ordered, immutable), Set (unordered, unique, mutable), and Dictionary (key-value pairs, unordered before 3.7 / insertion-ordered from 3.7+, mutable).

3. What does the Python expression range(start, stop, step) produce?

   A lazy sequence of integers beginning at start, incrementing by step, and stopping before stop (stop is exclusive). For example range(0, 10, 2) yields 0, 2, 4, 6, 8.

4. In Python, what is the difference between == and is?

   == tests value equality (do the objects have equal content), while is tests identity (do two references point to the exact same object in memory).

5. What is a list comprehension in Python, with an example that squares numbers 0 to 4?

   A concise syntax to build a list from an iterable. Example: [x**2 for x in range(5)] produces [0, 1, 4, 9, 16].

6. What is the difference between mutable and immutable objects in Python? Give one example of each.

   Mutable objects can be changed in place (e.g. list, dict, set); immutable objects cannot be changed after creation (e.g. int, str, tuple, frozenset). Rebinding an immutable variable creates a new object.

7. In Python file handling, what is the difference between opening a file in mode 'r', 'w', and 'a'?

   'r' opens for reading (error if the file does not exist); 'w' opens for writing and truncates/creates the file (overwriting existing content); 'a' opens for appending, writing to the end without truncating, creating the file if needed.

8. Why is the with statement preferred for file handling in Python?

   with open(...) as f: creates a context manager that automatically closes the file when the block exits, even if an exception occurs, preventing resource leaks and unclosed file handles.

9. In Python, what is the difference between read(), readline(), and readlines()?

   read() returns the entire file contents as one string; readline() returns the next single line including its newline; readlines() returns a list of all lines, each ending with its newline character.

10. What is the structure of a Python try / except / else / finally block?

    try contains code that may raise an exception; except handles a matching exception; else runs only if no exception occurred in try; finally always runs (cleanup) whether or not an exception occurred.

11. What is the difference between raise and assert in Python error handling?

    raise explicitly throws a specified exception (e.g. raise ValueError('bad')); assert checks a condition and raises AssertionError if it is False, used mainly for debugging and internal sanity checks.

12. In Python exception handling, what is the base class of most built-in exceptions, and which should you generally catch?

    Exception is the base class for most catchable errors. You should catch specific exceptions (e.g. ValueError, KeyError) rather than a bare except or except Exception, to avoid masking unexpected bugs.

13. What are the four pillars of Object-Oriented Programming?

    Encapsulation (bundling data and methods, hiding internal state), Abstraction (exposing only essential features), Inheritance (deriving classes from existing ones), and Polymorphism (the same interface behaving differently across types).

14. In Python OOP, what is the purpose of the __init__ method and the self parameter?

    __init__ is the constructor that initializes a new instance's attributes when the object is created. self is the reference to the current instance, through which attributes and methods are accessed.

15. What is the difference between an instance method, a class method, and a static method in Python?

    An instance method takes self and acts on a specific object; a class method uses @classmethod and takes cls, acting on the class itself; a static method uses @staticmethod, takes neither self nor cls, and is just a namespaced function.

16. In Python inheritance, what does super() do?

    super() returns a proxy to the parent (superclass), letting a subclass call the parent's methods—most commonly super().__init__(...) to run the parent constructor and reuse its initialization logic.

17. What is method overriding versus method overloading in OOP?

    Overriding redefines an inherited method in a subclass with the same signature to change behavior. Overloading defines multiple methods with the same name but different parameters; Python does not support true overloading and instead uses default or variadic arguments.

18. In NumPy, what is an ndarray and how does it differ from a Python list?

    An ndarray is NumPy's N-dimensional homogeneous array storing elements of a single fixed dtype in contiguous memory, enabling fast vectorized operations. Unlike a Python list, it is fixed-type, supports element-wise math, and is far more memory- and compute-efficient.

19. What does NumPy broadcasting do?

    Broadcasting lets NumPy perform element-wise operations on arrays of different but compatible shapes by virtually stretching the smaller array along dimensions of size 1, without copying data. Dimensions are compatible when they are equal or one of them is 1.

20. How do you compute the mean and standard deviation of a NumPy array a?

    np.mean(a) returns the arithmetic mean $\bar{x} = \frac{1}{n}\sum_{i=1}^{n} x_i$, and np.std(a) returns the standard deviation $\sigma = \sqrt{\frac{1}{n}\sum_{i=1}^{n}(x_i - \bar{x})^2}$.

21. In NumPy, what is the difference between a.reshape(2,3) and a.T?

    reshape(2,3) returns a view of the same data arranged into a 2x3 shape (total element count must match), while a.T returns the transpose, swapping axes so that element $(i,j)$ becomes $(j,i)$.

22. In Pandas, what is the difference between a Series and a DataFrame?

    A Series is a one-dimensional labeled array (a single column with an index). A DataFrame is a two-dimensional labeled table of rows and columns, where each column is a Series sharing a common row index.

23. In Pandas, what is the difference between .loc and .iloc?

    .loc selects by label/name of rows and columns (inclusive of the end label in slices), while .iloc selects by integer position (end-exclusive like normal Python slicing).

24. How do you handle missing values in a Pandas DataFrame df?

    df.isna() detects missing (NaN) values; df.dropna() removes rows/columns containing them; df.fillna(value) replaces them with a constant, mean, forward-fill (method='ffill'), or other strategy.

25. What does the Pandas groupby operation follow as its conceptual model?

    The split-apply-combine model: split the data into groups by key(s), apply an aggregation/transformation/filter to each group, then combine the results into a new Series or DataFrame, e.g. df.groupby('col')['x'].mean().

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