Machine Learning

You can write clean Python scripts using NumPy vectors and Pandas dataframes while computing derivatives and probability distributions for model training.

1. Python essentials, NumPy & Pandas for data handling6 hrs
   • ndarray slicing
   • DataFrame groupby
   • vectorized operations
   • missing data imputation
   • pd.read_csv memory_map
   • np.where conditional assignment
2. Linear algebra: vectors, matrices & operations6 hrs
   • matrix multiplication
   • eigenvalues and eigenvectors
   • dot product
   • transpose operations
   • np.linalg.inv
   • singular value decomposition
3. Probability & statistics for machine learning6 hrs
   • Bayes theorem
   • conditional probability
   • variance and standard deviation
   • probability density functions
   • scipy.stats normal distribution
   • expectation and covariance
4. Descriptive stats, distributions & hypothesis testing6 hrs
   • p-values
   • z-test and t-test
   • normal distribution
   • skewness and kurtosis
   • scipy.stats.ttest_ind
   • confidence intervals
5. Python environment management and tooling4 hrs
   • pip and virtualenv
   • requirements.txting
   • poetry dependency management
   • jupyter kernel setup
   • black linter configuration
6. Debugging data pipelines and common numeric pitfalls4 hrs
   • floating point precision errors
   • NaN propagation in NumPy
   • setting random seeds with np.random.seed
   • memory profiling with sys.getsizeof
   • traceback inspection in Pandas

Hands-on project: Write a vectorized data analysis pipeline in Python that processes raw CSV logs using NumPy and Pandas to compute custom statistical metrics without explicit loops, meeting memory efficiency constraints under 512 MB.

You can clean raw datasets, handle missing inputs, encode categorical variables, and generate exploratory visualizations with Seaborn and Matplotlib.

1. Handling missing values, outliers & data cleaning5 hrs
   • imputation strategies
   • IQR outlier capping
   • drop vs fill logic
   • duplicate removal
   • SimpleImputer transformer
   • pandas.DataFrame.dropna
2. Feature scaling, encoding & feature engineering5 hrs
   • StandardScaler
   • MinMaxScaler
   • OneHotEncoder
   • label encoding
   • OrdinalEncoder handling
   • ColumnTransformer setup
3. Exploratory data analysis with Matplotlib & Seaborn5 hrs
   • pairplot
   • heatmap
   • boxplot
   • figure subplot layout
   • sns.histplot
   • plt.subplots sizing
4. Correlation, distributions & insight generation5 hrs
   • Pearson correlation
   • Spearman rank
   • distribution plots
   • feature selection heuristics
   • pandas.DataFrame.corr
   • variance threshold filtering
5. Automated data profiling and validation4 hrs
   • Pandas Profiling reports
   • Great Expectations schema validation
   • detecting data drift in incoming batches
   • schema enforcement
   • logging data quality metrics
6. Data hygiene and reproducible EDA scripts4 hrs
   • refactoring notebooks into modular scripts
   • logging execution steps
   • handling multi-file datasets
   • managing cache directories
   • cli arguments for data scripts

Hands-on project: Perform end-to-end exploratory data analysis on a messy customer churn dataset, resolving missing values and generating publication-ready correlation matrices with Seaborn while documenting outlier trimming thresholds.

You can train and evaluate regression and classification models using Scikit-learn, interpreting precision, recall, and ROC-AUC curves correctly.

1. Linear & logistic regression8 hrs
   • Ordinary Least Squares
   • sigmoid function
   • cost function
   • gradient descent optimization
   • linear_model.LinearRegression
   • linear_model.LogisticRegression
2. Decision trees & random forests8 hrs
   • Gini impurity
   • information gain
   • bootstrap aggregating
   • feature importance scores
   • tree.DecisionTreeClassifier
   • ensemble.RandomForestClassifier
3. Support Vector Machines & K-Nearest Neighbours8 hrs
   • kernel trick
   • hyperplane margin
   • Euclidean distance
   • KNN distance weighting
   • svm.SVC
   • neighbors.KNeighborsClassifier
4. Model evaluation: accuracy, precision, recall, ROC-AUC8 hrs
   • confusion matrix
   • F1-score
   • ROC curve
   • cross-entropy loss
   • metrics.classification_report
   • metrics.roc_auc_score
5. Handling class imbalance in supervised datasets4 hrs
   • SMOTE oversampling
   • class_weight balancing parameter
   • undersampling majority classes
   • precision-recall tradeoff tuning
   • cost-sensitive learning
6. Debugging convergence and gradient issues in models4 hrs
   • learning rate tuning
   • feature scaling impact on convergence
   • detecting multicollinearity in linear models
   • handling singular matrices
   • max_iter threshold adjustments

Hands-on project: Build a multi-class classification model using Logistic Regression and Random Forests to predict customer purchase intent, evaluating performance with precision-recall curves and setting optimal classification thresholds.

You can group unlabelled data with clustering algorithms and reduce feature dimensionality using Principal Component Analysis.

1. K-Means & hierarchical clustering8 hrs
   • elbow method
   • silhouette score
   • agglomerative clustering
   • dendrograms
   • cluster.KMeans
   • cluster.AgglomerativeClustering
2. Dimensionality reduction with PCA8 hrs
   • covariance matrix
   • eigenvalue decomposition
   • explained variance ratio
   • feature projection
   • decomposition.PCA
   • reconstruction error calculation
3. Anomaly detection & association rules8 hrs
   • Isolation Forest
   • One-Class SVM
   • Apriori algorithm
   • support and confidence metrics
   • ensemble.IsolationForest
   • mlxtend association rules
4. Density-based clustering and advanced manifolds4 hrs
   • DBSCAN epsilon and min_samples
   • OPTICS clustering
   • t-SNE visualization embedding
   • UMAP manifold projection
   • handling noise points in clusters
5. Evaluating unsupervised cluster quality4 hrs
   • Calinski-Harabasz index
   • Davies-Bouldin score
   • comparing cluster assignments across runs
   • handling high-dimensional sparse distance matrices
   • visualizing cluster boundaries
6. Practical pitfalls in unsupervised learning4 hrs
   • curse of dimensionality in distance metrics
   • scaling sensitivity in K-Means
   • interpreting nebulous cluster boundaries
   • handling outliers that distort centroids
   • memory limits on large distance matrices

Hands-on project: Apply K-Means clustering and PCA on customer purchase records to segment retail buyers into distinct behavioral personas for targeted marketing campaigns, validating cluster stability with silhouette scores.

You can tune hyperparameters using grid search and build high-performance gradient boosting models with XGBoost to prevent overfitting.

1. Cross-validation & the bias-variance trade-off6 hrs
   • K-Fold cross-validation
   • stratified sampling
   • underfitting vs overfitting
   • learning curves
   • model_selection.KFold
   • model_selection.learning_curve
2. Hyperparameter tuning (Grid & Random Search)6 hrs
   • GridSearchCV
   • RandomizedSearchCV
   • parameter grid configuration
   • scoring metrics
   • model_selection.GridSearchCV
   • model_selection.RandomizedSearchCV
3. Regularisation techniques6 hrs
   • L1 Lasso
   • L2 Ridge
   • elastic net
   • penalty coefficient lambda
   • linear_model.Ridge
   • linear_model.Lasso
4. Bagging, boosting & XGBoost6 hrs
   • gradient boosting machines
   • XGBoost hyperparameters
   • learning rate
   • tree depth tuning
   • xgb.XGBClassifier
   • early_stopping_rounds
5. Stacking and voting ensemble architectures4 hrs
   • VotingClassifier hard and soft voting
   • StackingClassifier meta-estimator
   • out-of-fold predictions for stacking
   • ensemble weight optimization
   • reducing variance via multi-model blending
6. Advanced boosting libraries and lightgbm comparison4 hrs
   • LightGBM histogram-based algorithm
   • CatBoost categorical feature handling
   • comparing training speed across tree models
   • memory consumption trade-offs in gradient boosting
   • distributed boosting setups

Hands-on project: Train an XGBoost classifier with hyperparameter tuning via RandomizedSearchCV to maximize predictive accuracy on a complex financial fraud dataset while ensuring validation latency remains under 50ms.

You can build feedforward neural networks and convolutional architectures using TensorFlow and Keras to process image and text data.

1. Neural networks & backpropagation8 hrs
   • activation functions
   • ReLU
   • softmax
   • backpropagation calculus
   • tf.keras.activations
   • gradient clipping
2. Building models with TensorFlow & Keras8 hrs
   • Sequential API
   • model compile
   • model fit callbacks
   • TensorBoard logging
   • tf.keras.callbacks.EarlyStopping
   • tf.keras.optimizers.Adam
3. Convolutional Neural Networks (CNNs) for images8 hrs
   • conv2d layers
   • max pooling
   • flatten
   • dropout regularization
   • tf.keras.layers.Conv2D
   • tf.keras.layers.MaxPooling2D
4. Introduction to Natural Language Processing8 hrs
   • tokenization
   • word embeddings
   • padding
   • text classification
   • tf.keras.layers.TextVectorization
   • tf.keras.layers.Embedding
5. Recurrent architectures and sequence modeling4 hrs
   • LSTM cell gates
   • GRU architecture
   • bidirectional wrappers
   • sequence padding masks
   • vanishing gradient handling in RNNs
6. Debugging deep neural network training failures4 hrs
   • diagnosing exploding and vanishing gradients
   • learning rate scheduler configuration
   • batch normalization placement rules
   • monitoring loss curves for overfitting
   • GPU memory allocation errors in TensorFlow

Hands-on project: Build and train a Convolutional Neural Network (CNN) in TensorFlow to classify image inputs with high validation accuracy, using data augmentation and dropout layers to prevent overfitting.

You can write comprehensive test suites for ML pipelines, validate data integrity before inference, and audit models for fairness and bias.

1. Testing data transformations and feature pipelines5 hrs
   • pytest fixtures for datasets
   • asserting pandas DataFrame shapes and types
   • testing custom transformer fit and transform methods
   • mocking external data sources in ML tests
   • verifying pipeline idempotency
2. Data validation and contract testing5 hrs
   • schema validation with Pandera
   • detecting null injection and outlier spikes
   • asserting categorical feature value domains
   • testing inference payload contracts
   • handling malformed incoming requests
3. Model robustness and stress testing5 hrs
   • adversarial perturbation testing
   • evaluating performance on edge cases
   • measuring inference latency under load
   • memory leak detection during batch inference
   • handling out-of-vocabulary inputs
4. Fairness, bias, and model auditing5 hrs
   • disparate impact analysis
   • equalized odds evaluation
   • auditing protected attributes in datasets
   • mitigating algorithmic bias
   • model explainability with SHAP values
5. Automated testing in CI pipelines4 hrs
   • GitHub Actions workflow for ML tests
   • running regression tests on model updates
   • caching dataset dependencies in CI
   • linting ML codebase with flake8
   • code coverage reporting with pytest-cov
6. Regression testing across model iterations4 hrs
   • baseline performance comparison checks
   • asserting minimum F1 score thresholds in test suites
   • detecting silent feature misalignment
   • managing golden dataset snapshots
   • automated smoke tests for deployed artifacts

Hands-on project: Implement unit and integration tests for a feature engineering pipeline using PyTest, adding input validation guards and automated checks for data drift.

You can serialize models, build serving APIs with FastAPI, track experiments with MLflow, and containerize applications using Docker.

1. Model serialization & versioning6 hrs
   • pickle
   • joblib
   • artifact storage
   • model versioning schemas
   • joblib.dump compression levels
   • model metadata sidecars
2. Serving models with Flask / FastAPI6 hrs
   • Pydantic schemas
   • API routing
   • asynchronous endpoints
   • error handling
   • fastapi.FastAPI
   • uvicorn server execution
3. Containerizing with Docker6 hrs
   • Dockerfile creation
   • image build
   • container execution
   • port mapping
   • multi-stage build patterns
   • slim python base images
4. Experiment tracking with MLflow & cloud deployment6 hrs
   • mlflow.log_param
   • mlflow.log_metric
   • model registry
   • cloud endpoint deployment
   • mlflow.sklearn.log_model
   • tracking server backend store
5. Model monitoring and observability in production4 hrs
   • logging prediction distributions
   • monitoring feature drift with Evidently AI
   • setting up Prometheus metrics for inference latency
   • alerting on prediction error spikes
   • shadow deployment patterns
6. Troubleshooting inference bottlenecks and memory limits4 hrs
   • profiling Python inference memory usage
   • optimizing FastAPI concurrency worker threads
   • handling OOM errors in containerized runners
   • caching model predictions with Redis
   • reducing serialization overhead

Hands-on project: Containerize a trained machine learning model inside a multi-stage Dockerfile, expose it via FastAPI with Pydantic validation, and log training runs using MLflow.

You can design distributed training setups, manage feature stores, and scale inference pipelines to handle high enterprise throughput securely.

1. Distributed training and data parallelization5 hrs
   • Ray framework for distributed computing
   • Horovod multi-node training setup
   • Dask dataframe distributed processing
   • managing GPU cluster resources
   • checkpoint synchronization across nodes
2. Feature store architecture and management5 hrs
   • Feast feature store setup
   • offline vs online feature stores
   • preventing data leakage in feature joins
   • feature versioning and lineage tracking
   • low-latency feature retrieval for inference
3. Security, compliance, and governance in ML5 hrs
   • handling PII in training datasets
   • GDPR right to explanation compliance
   • securing ML endpoints with API keys and OAuth2
   • encrypting model artifacts at rest
   • audit logging for model decisions
4. Enterprise CI/CD and automated model retraining5 hrs
   • Kubeflow pipelines orchestration
   • triggering retraining on drift detection
   • automated canary model rollouts
   • rollback strategies for degraded models
   • managing model registry promotion gates
5. Cost optimization and infrastructure sizing4 hrs
   • estimating GPU vs CPU instance costs
   • auto-scaling inference pods on Kubernetes
   • model quantization for memory reduction
   • ONNX runtime for faster inference
   • batching requests to maximize hardware utilization
6. Disaster recovery and high availability in ML systems4 hrs
   • model registry backup strategies
   • multi-region inference endpoint routing
   • fallback heuristic rules when model fails
   • handling upstream data outage scenarios
   • runbook documentation for on-call engineers

Hands-on project: Design a distributed batch inference pipeline architecture specification, detailing feature store integration, secret management, and rollback procedures for a high-throughput enterprise system.

You can implement security controls, data privacy compliance checks, and audit trails for production machine learning models.

1. Data privacy and secure storage in ML pipelines3 hrs
   • anonymizing sensitive customer records
   • differential privacy mechanisms
   • securing S3 model bucket access
   • managing database credentials securely
   • data retention policies
2. Model explainability and interpretability tools3 hrs
   • SHAP value calculations
   • LIME local surrogate models
   • feature contribution plots
   • global vs local model explanation
   • communicating model decisions to stakeholders
3. Model security and vulnerability assessment3 hrs
   • defending against data poisoning attacks
   • membership inference vulnerability checks
   • securing pickling and unpickling pipelines
   • dependency scanning for ML libraries
   • handling prompt injection in ML workflows
4. Compliance frameworks and audit logging3 hrs
   • EU AI Act compliance requirements
   • maintaining model training lineage records
   • immutable audit logs for prediction requests
   • documenting hyperparameter configurations
   • version control for training datasets
5. Risk mitigation and model fallback strategies2 hrs
   • implementing rule-based fallback heuristics
   • handling confidence score threshold rejections
   • graceful degradation during service outages
   • human-in-the-loop review queues
   • incident response runbooks for ML failures
6. Regulatory audit preparation and sign-off2 hrs
   • compiling model card documentation
   • presenting fairness metrics to risk committees
   • archiving model artifacts for regulatory inspection
   • securing sign-off from compliance officers

Hands-on project: Perform a comprehensive security and bias audit on an enterprise credit scoring model, documenting data lineage, access controls, and compliance measures.

You can design large-scale machine learning architectures for recommendation engines, fraud detection, and predictive maintenance.

1. Building real-time recommendation systems3 hrs
   • collaborative filtering techniques
   • content-based recommendation matching
   • matrix factorization with SVD
   • handling cold-start user problems
   • caching top recommendations in Redis
2. Designing fraud detection pipelines3 hrs
   • real-time transaction scoring architecture
   • handling extreme class imbalance in streaming data
   • feature generation across sliding time windows
   • latency constraints in financial transactions
   • ensemble scoring models for anomaly detection
3. Predictive maintenance and time-series forecasting3 hrs
   • sensor data preprocessing and resampling
   • lag features and rolling window statistics
   • ARIMA and exponential smoothing models
   • LSTM networks for time-series prediction
   • detecting equipment failure anomalies early
4. Scaling NLP applications in production3 hrs
   • vector database integration with FAISS
   • semantic search pipeline architecture
   • embedding generation at scale
   • handling concurrent query requests
   • optimizing inference compute costs
5. System design interview practice for ML engineers2 hrs
   • structuring an ML system design interview response
   • estimating compute and storage requirements
   • addressing data scale and latency trade-offs
   • evaluating online vs offline metrics
   • mock system design whiteboarding sessions
6. Architecture review and failure mode analysis2 hrs
   • identifying single points of failure in ML topologies
   • designing fallback paths for upstream data failures
   • cost modeling for large-scale cloud ML architectures
   • peer review of production architecture designs

Hands-on project: Design a complete system architecture document for a real-time recommendation engine handling 10,000 requests per second, detailing caching strategies and failure recovery.

You can serve the same feature definition to training and inference and stop train-serve skew.

1. Train-serve skew90 min
   • Why offline accuracy lies
   • Point-in-time correctness
   • Leakage from future data
2. Feature stores90 min
   • Offline vs online store
   • Feature definitions as code
   • Backfilling a new feature
3. Improving data, not the model90 min
   • Label quality audit
   • Targeted data collection
   • Slice-based error analysis
4. Reproducibility90 min
   • Dataset versioning
   • Feature lineage
   • Reproducing a past prediction

Hands-on project: Define five features in a feature store with point-in-time-correct training retrieval and low-latency online serving, then demonstrate and fix a train-serve skew bug caused by a leaked future value.

You can design, build, and deploy an end-to-end machine learning system while presenting your portfolio professionally to hiring panels.

1. Build & deploy an end-to-end ML application6 hrs
   • system integration
   • inference pipeline
   • health checks
   • production monitoring
   • cloud VM provisioning
   • nginx reverse proxy setup
2. Resume, GitHub portfolio & Kaggle profile building6 hrs
   • README structuring
   • Kaggle notebooks
   • resume bullet points
   • portfolio presentation
   • architectural diagram drafting
   • open source contribution hygiene
3. Mock interviews, DSA refresh & HR rounds6 hrs
   • behavioral interview prep
   • ML system design rounds
   • algorithmic coding questions
   • salary negotiation
   • system design whiteboard practice
   • handling tricky edge case questions
4. Final architectural defense and code review6 hrs
   • presenting capstone architecture to mentors
   • defending trade-offs in model selection
   • code walkthrough and refactoring feedback
   • handling edge cases in production scenarios
   • final portfolio polish and sign-off
5. Peer code reviews and collaborative debugging4 hrs
   • reviewing teammate pull requests
   • debugging merge conflicts in ML repositories
   • identifying performance bottlenecks in peer code
   • writing constructive code review feedback
6. Final career coaching and offer negotiation strategy2 hrs
   • evaluating job offers across startups and enterprises
   • negotiating compensation packages for ML engineering roles
   • understanding equity and stock options
   • long-term career growth planning in artificial intelligence

Hands-on project: Deploy a complete machine learning capstone system on cloud infrastructure, complete with automated inference testing, GitHub portfolio documentation, and mock interview clearance.