Machine Learning & Python Data Science for Business and AI

Explore data with exploratory data analysis (EDA) to identify relationships, detect missing values and outliers, understand data distribution, and guide preprocessing, feature engineering, and model selection.

Explore descriptive statistics: mean, median, mode, variance, and standard deviation—computed with Python libraries like NumPy and pandas for data science applications.

Explore data visualization with matplotlib and seaborn by creating line and scatter plots, visualizing monthly sales growth and a regression relationship between budget and sales.

Learn to handle missing data in Python by deleting rows with missing values and imputing gaps using mean and forward fill on a data frame with ages and salaries.

Learn to convert categorical data into numerical form using label encoding and one-hot encoding, with practical size and color examples and sklearn preprocessing.

Normalize data to 0–1 and standardize to zero mean and unit variance as feature scaling. Demonstrate with Python MinMaxScaler and StandardScaler on a two-feature dataset, for logistic regression and SVM.

Explore supervised, unsupervised, and reinforcement learning, detailing linear/logistic regression, decision trees, SVM, nearest neighbors, PCA, DBSCAN, Q-learning, deep Q-learning, policy gradient, CNN, RNN/LSTM, and generative models.

Learn key metrics for evaluating machine learning models, including accuracy, precision, recall, F1-score, and ROC-AUC, with hands-on examples using iris data, logistic regression, and train-test split.

Learn linear regression theory as a supervised method for predicting continuous values, understand the linear relationship between variables, and implement it in Python with numpy, evaluating with MSE and MAE.

Explore multiple linear regression with Python to predict house prices using features like square footage and bedrooms. Build models with train/test splits and evaluate with mean absolute error and R-squared.

Learn polynomial regression as an extension of linear regression to model non-linear salary trends from experience, using Python and polynomial features to fit, predict, and visualize results.

Explore the k-nearest neighbors (KNN) algorithm in Python for classification and regression, using a two-feature dataset with height and weight to classify healthy or overweight, and a house price regression.

Explore decision trees and random forests in Python for classification and regression, using iris data to train and test, and learn how ensemble learning with multiple trees boosts performance.

Explore k-means clustering theory and Python implementation using iris data to form three clusters, update centroids by proximity, and evaluate with the elbow method for optimal cluster count.

Explore hierarchical clustering in Python to build an agglomerative, unsupervised learning model that forms a dendrogram and reveals data structure with four centers.

DBSCAN uses density-based clustering to identify clusters and noise. It is an unsupervised algorithm that detects outliers without predefining cluster counts, using eps and min points in Python.

Apply feature engineering to create, transform, and select relevant features from raw data, using log transformation, scaling, normalization, and principal component analysis to improve accuracy and reduce overfitting.

Master feature engineering by extracting meaningful features from raw data and creating new features, using text tf-idf vectorization and polynomial features to boost model performance.

Explore feature selection in Python using filter, wrapper, and embedded methods. Apply to the iris dataset with chi-squared tests, recursive feature elimination, and regularized linear models to pick top features.

Explore bagging and bootstrap aggregating to boost accuracy with decision trees and random forests. Learn training with iris data, evaluate accuracy, and understand ensemble predictions reduce overfitting and improve generalization.

Explore boosting techniques—AdaBoost, gradient boosting, and XGBoost—using decision trees to build stronger predictive models, demonstrated on the iris data set and evaluated with accuracy.

Explore how overfitting and underfitting affect ensemble methods like random forest, using the iris dataset to compare training and test performance and model complexity.

Explore neural networks, computational modules inspired by the brain, with input, hidden, and output layers and activation functions, enabling deep learning from large data for image recognition and nlp.

Explore the fundamental structure of neural networks, including neurons, layers, weights, biases, and activation functions like relu and sigmoid. Learn to compute single and multi-layer outputs using matrix operations.

Explore sigmoid, ReLU, and tanh activation functions in neural networks, their ranges and uses for binary classification and hidden layers, and understand issues like vanishing gradients in Python.

Explore backpropagation as the core training algorithm for neural networks, adjusting weights via forward and backward propagation and a simple one-hidden-layer network with a sigmoid activation.

Explore TensorFlow, a Google open-source library for building and training deep learning models, and Keras, a simple, high-level API for modular networks, transfer learning, and scalable production.