Apply data preprocessing to remove wrong values and outliers, and remove wrongly labeled examples to achieve uniform data points, helping the model learn patterns and improve accuracy.

Set up a Python machine learning environment with Anaconda and the Jupiter notebook, run notebooks locally via localhost, and organize code into runnable cells for modular experiments.

Master NumPy matrix operations, including addition, multiplication and transpose, and create zero, one, and complex matrices, while exploring shapes, dtypes, and dot products.

Learn to iterate through a numpy array with nested for loops in Python, printing each element line by line, and upgrade from classical code to the standard library.

Learn how to load datasets with built-in functions, read Excel sheets and data frames, parse filenames, and access Kaggle and open-source datasets for analysis.

Create and inspect pandas data frames, use head, tail, and describe for statistics, and index with brackets to select rows and columns via the columns attribute.

Explore linear regression as a supervised learning algorithm, illustrating simple and multiple regression, fitting a line of best fit to predict unknown values by minimizing error.

Understand gradient descent optimizer that minimizes the mean squared error cost function by updating weights beta with learning rate alpha and gradients to align predictions with actual values.

Train a linear regression model on X and Y using 30 data points with an 80/20 train-test split, reshape data to two-dimensional form, and predict against observed values.

Understand how the sigmoid function in logistic regression maps input to 0–1 and saturates at extreme values. See why its bounded output suits binary classification.

Explore how support vector machines separate two classes by maximizing the margin with kernels, choosing linear boundaries or nonlinear options like polynomial based on the data.