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 Learning

• What is ML and how it differs from traditional programming

• ML categories: Supervised, Unsupervised, Semi-supervised, Reinforcement

• Key terms: Features, labels, models, predictions

• The ML workflow from problem to evaluation

• Ethical considerations and bias in ML

What is ML and how it differs from traditional programming

ML categories: Supervised, Unsupervised, Semi-supervised, Reinforcement

Key terms: Features, labels, models, predictions

The ML workflow from problem to evaluation

Ethical considerations and bias in ML

2. Mathematical Foundations

• Linear Algebra: Vectors, matrices, eigenvalues

• Calculus: Gradients, derivatives, chain rule

• Probability: Bayes' theorem, distributions, expectations

• Information theory: Entropy, KL-divergence

Linear Algebra: Vectors, matrices, eigenvalues

Calculus: Gradients, derivatives, chain rule

Probability: Bayes' theorem, distributions, expectations

Information theory: Entropy, KL-divergence

3. Data Preprocessing and Cleaning

• Managing missing or noisy data

• Categorical encoding: Label, One-hot

• Feature scaling: Normalization, standardization

• Outlier treatment, data binning, imputation

Managing missing or noisy data

Categorical encoding: Label, One-hot

Feature scaling: Normalization, standardization

Outlier treatment, data binning, imputation

4. Feature Engineering and Selection

• Creating domain-specific features

• Polynomial & interaction features

• Feature selection: mutual information, Lasso, tree-based

• Dimensionality reduction techniques overview

Creating domain-specific features

Polynomial & interaction features

Feature selection: mutual information, Lasso, tree-based

Dimensionality reduction techniques overview

5. Supervised Learning Algorithms

• Regression: Linear, Ridge, Lasso

• Classification: Logistic Regression

• Decision Trees, Random Forests

• SVM (linear & kernel), k-Nearest Neighbors

Regression: Linear, Ridge, Lasso

Classification: Logistic Regression

Decision Trees, Random Forests

SVM (linear & kernel), k-Nearest Neighbors

6. Unsupervised Learning Algorithms

• Clustering: k-Means, DBSCAN, Hierarchical

• Association rule learning: Apriori, FP-Growth

• Anomaly Detection: One-Class SVM, Isolation Forest

• Density Estimation: GMM

Clustering: k-Means, DBSCAN, Hierarchical

Association rule learning: Apriori, FP-Growth

Anomaly Detection: One-Class SVM, Isolation Forest

Density Estimation: GMM

7. Dimensionality Reduction Techniques

• PCA, t-SNE, UMAP, LDA

• Linear vs non-linear dimensionality reduction

PCA, t-SNE, UMAP, LDA

Linear vs non-linear dimensionality reduction

8. Model Evaluation and Validation

• Metrics: Accuracy, Precision, Recall, F1, AUC

• Regression: RMSE, MSE, R²

• Cross-validation strategies: K-Fold, LOOCV

• Confusion matrix interpretation

Metrics: Accuracy, Precision, Recall, F1, AUC

Regression: RMSE, MSE, R²

Cross-validation strategies: K-Fold, LOOCV

Confusion matrix interpretation

9. Model Optimization and Regularization

• Overfitting vs underfitting, Bias-variance tradeoff

• Regularization: L1, L2, ElasticNet

• Early stopping techniques

• Hyperparameter tuning: GridSearchCV, RandomizedSearchCV, Bayesian search

Overfitting vs underfitting, Bias-variance tradeoff

Regularization: L1, L2, ElasticNet

Early stopping techniques

Hyperparameter tuning: GridSearchCV, RandomizedSearchCV, Bayesian search

10. Neural Networks and Deep Learning

• Perceptron and MLP basics, backpropagation

• Activation functions: Sigmoid, ReLU, Tanh

• Loss functions: MSE, Cross-entropy

• CNNs (for image tasks), RNNs, LSTM (for sequences)

Perceptron and MLP basics, backpropagation

Activation functions: Sigmoid, ReLU, Tanh

Loss functions: MSE, Cross-entropy

CNNs (for image tasks), RNNs, LSTM (for sequences)

11. ML in Production and Deployment

• Model serialization: Pickle, Joblib, ONNX

• REST APIs using Flask/FastAPI

• CI/CD for ML projects

• Docker containerization

• Model monitoring and versioning

Model serialization: Pickle, Joblib, ONNX

REST APIs using Flask/FastAPI

CI/CD for ML projects

Docker containerization

Model monitoring and versioning

12. Tools, Libraries, and Real-World Projects

• Python basics for ML

• Libraries: NumPy, Pandas, Matplotlib, Seaborn

• Frameworks: Scikit-learn, TensorFlow, PyTorch

• Public datasets: UCI, Kaggle, HuggingFace

• Projects: House price predictor, spam classifier, image recognition

Python basics for ML

Libraries: NumPy, Pandas, Matplotlib, Seaborn

Frameworks: Scikit-learn, TensorFlow, PyTorch

Public datasets: UCI, Kaggle, HuggingFace

Projects: House price predictor, spam classifier, image recognition

This 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.