Machine Learning Fundamentals and Statistics

Time series fundamentals including seasonality, trends, ARIMA models, exponential smoothing, and modern deep learning approaches for forecasting.

Introduction to neural networks, deep learning architectures, CNN, RNN, LSTM, training techniques, and popular frameworks like TensorFlow and PyTorch.

Practical approaches to problem identification, solution design, data requirements assessment, and business value evaluation for ML projects.

Comprehensive introduction to ML covering definitions, types of machine learning problems, and application domains.

In-depth coverage of statistical foundations including descriptive statistics, probability distributions, central limit theorem, and statistical inference.

Comprehensive study of supervised learning including linear regression, logistic regression, decision trees, random forests, SVM, and neural networks.

Study of unsupervised learning algorithms including K-means, hierarchical clustering, PCA, t-SNE, and anomaly detection methods.

Detailed coverage of evaluation metrics, cross-validation, confusion matrices, ROC curves, precision-recall curves, and statistical significance testing.

Comprehensive study of overfitting, underfitting, bias-variance tradeoff, and regularization methods including L1, L2, dropout, and early stopping.

Practical techniques for feature engineering including scaling, encoding categorical variables, handling missing data, feature creation, and automated feature selection.

Advanced techniques for hyperparameter optimization including automated tuning methods, optimization algorithms, and best practices for model improvement.

Comprehensive coverage of ensemble methods including random forests, gradient boosting, AdaBoost, XGBoost, and model stacking techniques.