one note at a time
Values and types, variable naming, expressions and operators, f-strings, and input/output.
Making decisions with if/elif/else, and repeating work with for and while loops.
Strings and slicing, lists, tuples, dictionaries and sets — methods and mutability traps.
Arrays and dtypes, indexing and views, broadcasting, and aggregations along an axis.
Series and DataFrames — reading, inspecting, cleaning, selecting and grouping tabular data.
Centre and spread, measuring relationships between variables, and the EDA workflow.
Choosing the right chart, then drawing it with Matplotlib, Seaborn or Plotly.
Class Test 1 revision packages and practice sets, covering NumPy arrays, collections, Pandas, exploratory data analysis, and Matplotlib.
What machine learning is and why it works — the types of learning, and the Python toolkit behind them.
Missing values, categorical encoding, feature scaling, outliers, and the train-test split.
Least-squares by hand and in scikit-learn, the matrix formulation, and what the model assumes.
Loss functions, gradient descent, the learning rate, and how many rows each update sees.
Descriptive statistics, covariance, and correlation.
Fundamental probability and the addition/multiplication rules.
Probability distributions and Bayes’ theorem.
Theoretical frameworks for sampling, probability, and metrics.