Free Udemy Course: Machine Learning Mastery: 600+ Conceptual & Scenario Q&A

Unlock the power of Machine Learning with this comprehensive question-based course designed for learners, job-seekers, and professionals alike. "Machine Learning Mastery: 600+ Conceptual & Scenario-Based Q&A" is your one-stop destination to sharpen your understanding, test your knowledge, and prepare for real-world challenges and interviews.This course provides 12 structured modules covering foundational theories to hands-on deployment, all backed by 600+ carefully curated questions—including both conceptual and real-world scenarios. Whether you're preparing for interviews, brushing up skills for a role in data science or ML engineering, or just starting your ML journey, this course offers the depth and clarity you need.Course Syllabus Overview:1. Foundations of Machine LearningWhat is ML and how it differs from traditional programmingML categories: Supervised, Unsupervised, Semi-supervised, ReinforcementKey terms: Features, labels, models, predictionsThe ML workflow from problem to evaluationEthical considerations and bias in ML2. Mathematical FoundationsLinear Algebra: Vectors, matrices, eigenvaluesCalculus: Gradients, derivatives, chain ruleProbability: Bayes' theorem, distributions, expectationsInformation theory: Entropy, KL-divergence3. Data Preprocessing and CleaningManaging missing or noisy dataCategorical encoding: Label, One-hotFeature scaling: Normalization, standardizationOutlier treatment, data binning, imputation4. Feature Engineering and SelectionCreating domain-specific featuresPolynomial & interaction featuresFeature selection: mutual information, Lasso, tree-basedDimensionality reduction techniques overview5. Supervised Learning AlgorithmsRegression: Linear, Ridge, LassoClassification: Logistic RegressionDecision Trees, Random ForestsSVM (linear & kernel), k-Nearest Neighbors6. Unsupervised Learning AlgorithmsClustering: k-Means, DBSCAN, HierarchicalAssociation rule learning: Apriori, FP-GrowthAnomaly Detection: One-Class SVM, Isolation ForestDensity Estimation: GMM7. Dimensionality Reduction TechniquesPCA, t-SNE, UMAP, LDALinear vs non-linear dimensionality reduction8. Model Evaluation and ValidationMetrics: Accuracy, Precision, Recall, F1, AUCRegression: RMSE, MSE, R²Cross-validation strategies: K-Fold, LOOCVConfusion matrix interpretation9. Model Optimization and RegularizationOverfitting vs underfitting, Bias-variance tradeoffRegularization: L1, L2, ElasticNetEarly stopping techniquesHyperparameter tuning: GridSearchCV, RandomizedSearchCV, Bayesian search10. Neural Networks and Deep LearningPerceptron and MLP basics, backpropagationActivation functions: Sigmoid, ReLU, TanhLoss functions: MSE, Cross-entropyCNNs (for image tasks), RNNs, LSTM (for sequences)11. ML in Production and DeploymentModel serialization: Pickle, Joblib, ONNXREST APIs using Flask/FastAPICI/CD for ML projectsDocker containerizationModel monitoring and versioning12. Tools, Libraries, and Real-World ProjectsPython basics for MLLibraries: NumPy, Pandas, Matplotlib, SeabornFrameworks: Scikit-learn, TensorFlow, PyTorchPublic datasets: UCI, Kaggle, HuggingFaceProjects: House price predictor, spam classifier, image recognitionThis course is your complete companion to mastering Machine Learning through 600+ carefully curated questions covering both theoretical concepts and real-world scenarios. By practicing diverse Q&A formats, you’ll solidify your understanding, sharpen interview readiness, and confidently apply ML techniques in practical settings. Whether you're a beginner or an aspiring ML professional, this course helps bridge the gap between learning and doing.