Fundamentals of Machine Learning through Python

Explore Python essentials by using variables to store numbers and strings, perform arithmetic with operators, compute a rectangle's area, and create personalized greetings through string concatenation.

Master control structures in Python by using if statements to decide paths and for and while loops to repeat tasks, with examples like voting eligibility, grading, and the Fibonacci series.

Explore modular programming and functions in Python to create organized, reusable code. Define functions with def, parameters, and proper indentation to perform operations like addition, subtraction, and compound interest.

Discover Python modules as pre-built collections of functions, classes, and variables, from built-in options like the random module to future tools such as NumPy and pandas, and learn import basics.

Create a Celsius temperature list, convert to Fahrenheit with the 9/5 plus 32, and print results. Use dictionaries for key-based access, and preview pandas for structured data in machine learning.

Learn to clean imperfect data by handling missing values, detecting and addressing outliers with IQR and cap techniques, and transforming categorical features with hot one encoding for robust machine learning.

Explore data visualization with matplotlib and seaborn to analyze the Titanic dataset, mastering histograms, scatter plots, and correlation heatmaps for feature selection.

Explore linear regression to predict house prices from features like area and rooms, using hot one encoding, train-test split, and plots with mean absolute error, mean squared error, and r-squared.

Use logistic regression for classification, translating inputs into probabilities with the sigmoid function. Practice on the heart disease risk dataset with features like age and smoking, using Python and scikit-learn.

Evaluate model performance using mean absolute error, mean squared error, and R-squared for regression. For classification, apply accuracy, precision, recall, F1, and interpret confusion matrices to reflect problem-specific priorities.

Learn how cross-validation strengthens model evaluation by repeatedly splitting data into folds, training on training folds and testing on held-out folds to estimate generalization and MSE across splits.

Learn to build models with Psychic Learn by importing the model class, instantiating it, fitting on data, making predictions, and evaluating with accuracy and mean square error.

Explore ensemble methods in machine learning by building random forest and gradient boosting models, tuning hyperparameters, and evaluating predictions on a house prices dataset.

Explore the k-nearest neighbors algorithm, classifying by majority among the k closest points or predicting by averaging their values, with emphasis on distance measures, k selection, and preprocessing.

Explore support vector machines for classification and regression, using the kernel trick to separate the classes while minimizing misclassifications, and apply a SVM regressor to the house prices dataset.

Conclude by reflecting on python foundations, syntax, and data structures, then cover data processing, machine learning, algorithms, and metrics, and share your project with the ai community.