Diploma in AI and ML Techniques

Python Basics Review – Variables and Data Types – Control Flow – Functions – List Comprehensions – Dictionary Operations – File Handling. NumPy for Numerical Computing – NumPy Arrays – Array Operations – Mathematical Operations – Broadcasting – Array Indexing and Slicing – NumPy Functions for ML. Pandas for Data Manipulation – Series and DataFrames – Reading Data – Data Selection – Data Filtering – Grouping Operations – Merging DataFrames.

Matplotlib Basics – Creating Plots – Line Plots, Scatter Plots, Bar Charts – Customizing Plots – Subplots – Saving Figures. Seaborn for Statistical Visualization – Distribution Plots – Categorical Plots – Relationship Plots – Heatmaps – Advanced Visualizations. Visualization for ML – Feature Distribution Visualization – Correlation Visualization – Target Variable Analysis – Feature Relationships – Pre-ML Data Exploration.

Introduction to scikit-learn – scikit-learn API – Estimators and Predictors – Fit and Predict Pattern – Model Evaluation Basics. Data Splitting – Train-Test Split – Cross-Validation Concepts – Stratified Splitting – Time Series Splitting. Basic ML Workflow – Loading Data – Splitting Data – Training Model – Making Predictions – Evaluating Model.

Handling Missing Values – Detecting Missing Values – Imputation Strategies – Dropping Missing Values – Handling Missing Data in scikit-learn. Feature Scaling – Why Scale Features? – Standardization – Normalization – Min-Max Scaling – When to Scale. Encoding Categorical Variables – Label Encoding – One-Hot Encoding – Ordinal Encoding – Encoding in scikit-learn – Handling Text Data.

Supervised Learning Overview – What is Supervised Learning? – Regression vs Classification – Training Process – Model Generalization – Overfitting and Underfitting. Model Evaluation Basics – Accuracy Metrics – Confusion Matrix – Precision, Recall, F1-Score – Mean Squared Error – R-squared. Simple ML Models – Linear Regression Basics – Logistic Regression Basics – k-Nearest Neighbors – Model Comparison.

Data Quality Issues – Missing Values – Duplicate Records – Inconsistent Data – Outliers – Data Types – Data Quality Assessment. Handling Missing Data – Types of Missingness: MCAR, MAR, MNAR – Detection Methods – Imputation Strategies: Mean, Median, Mode, KNN Imputation – Advanced Imputation – When to Drop Missing Values. Outlier Detection and Treatment – What are Outliers? – Detection Methods: IQR, Z-score, Isolation Forest – Outlier Treatment: Removal, Capping, Transformation – Domain-Specific Outliers.

Why Feature Scaling? – Impact on ML Algorithms – Algorithms Requiring Scaling – Algorithms Not Requiring Scaling. Scaling Techniques – Standardization (Z-score Normalization) – Min-Max Scaling – Robust Scaling – Normalization – When to Use Each Method. Data Transformation – Log Transformation – Square Root Transformation – Box-Cox Transformation – Power Transformations – Handling Skewed Data.

Categorical Data Types – Nominal Variables – Ordinal Variables – High Cardinality Categorical – Handling Categorical Data. Encoding Techniques – Label Encoding – One-Hot Encoding – Ordinal Encoding – Target Encoding – Frequency Encoding – Binary Encoding. Text Data Encoding – Bag of Words – TF-IDF – Word Embeddings (Introduction) – Text Preprocessing – Handling Text Features.

Feature Engineering Concepts – What is Feature Engineering? – Creating New Features – Domain Knowledge – Feature Interactions – Polynomial Features. Temporal Features – Date and Time Features – Time-based Features – Cyclical Encoding – Lag Features – Rolling Statistics. Numerical Feature Engineering – Binning – Discretization – Feature Interactions – Ratio Features – Aggregated Features.

Feature Selection Importance – Curse of Dimensionality – Benefits of Feature Selection – Feature Selection vs Feature Extraction. Feature Selection Methods – Filter Methods: Correlation, Chi-square, Mutual Information – Wrapper Methods: Forward Selection, Backward Elimination – Embedded Methods: Lasso, Ridge, Tree-based – Feature Importance. Dimensionality Reduction (Introduction) – Principal Component Analysis (PCA) Basics – When to Use PCA – PCA Limitations – Other Dimensionality Reduction Techniques.

Linear Regression Fundamentals – What is Linear Regression? – Simple Linear Regression – Multiple Linear Regression – Assumptions of Linear Regression – Cost Function – Gradient Descent. Implementing Linear Regression – Using scikit-learn – Training Linear Regression – Making Predictions – Interpreting Coefficients – Model Evaluation. Polynomial Regression – When to Use Polynomial Regression – Polynomial Features – Overfitting in Polynomial Regression – Regularization Concepts.

Logistic Regression – What is Logistic Regression? – Sigmoid Function – Decision Boundary – Binary Classification – Multi-class Classification – Using scikit-learn. Classification Metrics – Accuracy – Precision, Recall, F1-Score – Confusion Matrix – ROC Curve and AUC – Classification Report. Classification Algorithms Overview – k-Nearest Neighbors (k-NN) – Naive Bayes – Algorithm Comparison – When to Use Each Algorithm.

Decision Trees – What are Decision Trees? – How Decision Trees Work – Splitting Criteria: Gini, Entropy – Tree Pruning – Overfitting in Trees – Using scikit-learn. Random Forests – What are Random Forests? – Bagging Concept – Random Forest Algorithm – Feature Importance – Hyperparameter Tuning – Advantages of Random Forests. Gradient Boosting – Boosting Concept – Gradient Boosting Machines – XGBoost Introduction – LightGBM Introduction – When to Use Boosting.

Support Vector Machines (SVM) – What are SVMs? – Maximum Margin Concept – Kernel Trick – Linear vs Non-linear SVMs – SVM Hyperparameters – Using scikit-learn. Advanced Classification – Neural Networks Basics (Introduction) – Algorithm Selection Guide – Ensemble Voting – Stacking Concepts. Model Evaluation and Validation – Cross-Validation – Stratified Cross-Validation – Learning Curves – Validation Curves – Bias-Variance Trade-off.

Hyperparameter Tuning – What are Hyperparameters? – Grid Search – Random Search – Bayesian Optimization (Introduction) – scikit-learn Tools. Model Comparison – Comparing Multiple Models – Performance Metrics – Computational Cost – Interpretability – Choosing Best Model. Model Interpretation – Feature Importance – Model Coefficients – Partial Dependence Plots (Introduction) – Model Explainability Basics.

Introduction to Neural Networks – What are Neural Networks? – Biological Inspiration – Perceptron Model – Multi-Layer Perceptron (MLP) – Neural Network Architecture: Input Layer, Hidden Layers, Output Layer – Forward Propagation. Activation Functions – Why Activation Functions? – Sigmoid Function – Tanh Function – ReLU and Variants: Leaky ReLU, ELU, Swish – Activation Function Selection – Vanishing Gradient Problem. Neural Network Training – Loss Functions: MSE, Cross-Entropy – Cost Function – Gradient Descent – Learning Rate – Batch Processing – Epochs and Iterations.

Backpropagation Algorithm – What is Backpropagation? – Chain Rule – Computing Gradients – Backward Pass – Gradient Flow – Implementing Backpropagation. Optimization Algorithms – Gradient Descent Variants: Batch, Stochastic, Mini-batch – Momentum – RMSprop – Adam Optimizer – Learning Rate Scheduling – Adaptive Learning Rates. Regularization Techniques – Overfitting in Neural Networks – L1 and L2 Regularization – Dropout – Early Stopping – Data Augmentation – Batch Normalization.

TensorFlow and Keras Introduction – TensorFlow Overview – Keras High-Level API – TensorFlow vs Keras – Installation and Setup – TensorFlow 2.x Features. Building Models with Keras – Sequential API – Functional API – Model Definition – Layer Types: Dense, Dropout, BatchNormalization – Compiling Models – Model Summary. Training Models – Model Training: fit() method – Validation Data – Callbacks: EarlyStopping, ModelCheckpoint, ReduceLROnPlateau – Training History – Monitoring Training.

Deep Networks – Deep vs Shallow Networks – Benefits of Depth – Challenges: Vanishing Gradients, Overfitting – Residual Connections – Skip Connections. Network Architectures – Feedforward Networks – Wide vs Deep Networks – Network Design Principles – Hyperparameter Tuning: Layers, Neurons, Learning Rate – Architecture Search Basics. Transfer Learning Concepts – What is Transfer Learning? – Pre-trained Models – Fine-tuning – Feature Extraction – Transfer Learning Benefits – When to Use Transfer Learning.

Model Evaluation – Training vs Validation vs Test Sets – Evaluation Metrics for Deep Learning – Confusion Matrix – Classification Report – Regression Metrics – Model Comparison. Model Saving and Loading – Saving Models – Model Formats: H5, SavedModel – Loading Models – Model Versioning – Model Checkpointing. Introduction to Model Deployment – Deployment Options – Model Conversion – ONNX Format (Introduction) – Deployment Considerations – Model Serving Basics.

Introduction to Computer Vision – What is Computer Vision? – Image Representation – Pixels and Color Channels – Image Preprocessing – Common CV Tasks: Classification, Detection, Segmentation. Convolutional Neural Networks (CNNs) – Why CNNs for Images? – Convolution Operation – Filters and Kernels – Feature Maps – Convolution Layers – Pooling Layers: Max Pooling, Average Pooling – CNN Architecture. Building CNNs – CNN Layers: Conv2D, MaxPooling2D, Flatten – CNN Architecture Design – Building CNN with Keras – Training CNNs – Visualizing CNN Features.

Popular CNN Architectures – LeNet – AlexNet – VGG – ResNet Concepts – Transfer Learning with Pre-trained CNNs – Using Pre-trained Models: VGG16, ResNet50. Image Augmentation – Why Data Augmentation? – Augmentation Techniques: Rotation, Scaling, Flipping, Cropping – Keras ImageDataGenerator – Augmentation Best Practices – Handling Small Datasets. Transfer Learning for Images – Loading Pre-trained Models – Feature Extraction – Fine-tuning – Freezing Layers – Transfer Learning Workflow – When to Use Transfer Learning.

Introduction to NLP – What is NLP? – NLP Applications – Text Representation Challenges – Text Preprocessing – Tokenization – Text Cleaning. Text Preprocessing – Lowercasing – Removing Punctuation – Stop Word Removal – Stemming – Lemmatization – Handling Special Characters – Text Normalization. Text Representation – Bag of Words – TF-IDF – Word Embeddings Introduction – Word2Vec Concepts – Embedding Dimensions – Text Vectorization.

Neural Networks for Text – Feedforward Networks for NLP – Embedding Layers – Sequence Models Introduction – RNN Concepts – LSTM Introduction – GRU Introduction. Sentiment Analysis – What is Sentiment Analysis? – Building Sentiment Classifiers – Using Pre-trained Embeddings – Text Classification with Deep Learning – Model Architecture for Sentiment Analysis. Text Classification – Binary Classification – Multi-class Classification – Multi-label Classification – Building Text Classifiers – Evaluating Text Models – Handling Imbalanced Data.

Image Classification Project – End-to-End Image Classification – Data Collection and Preparation – Model Building – Training and Evaluation – Deployment Considerations. Sentiment Analysis Project – Building Sentiment Analyzer – Data Preprocessing – Model Development – Training and Tuning – Evaluation and Testing. Combining CV and NLP – Image Captioning Concepts – Visual Question Answering Introduction – Multimodal Learning Basics – Real-world Applications.

Introduction to MLOps – What is MLOps? – MLOps vs DevOps – ML Lifecycle – Challenges in Production ML – MLOps Principles – MLOps Maturity Levels. ML Workflow – Data Collection – Data Preparation – Model Training – Model Evaluation – Model Deployment – Model Monitoring – Continuous Improvement. MLOps Tools and Platforms – MLflow Introduction – Kubeflow Concepts – TensorFlow Extended (TFX) Overview – Cloud ML Platforms – Tool Comparison.

Model Versioning – Why Version Models? – Model Versioning Strategies – Version Control for Models – Model Registry – Model Metadata – Model Lineage. MLflow for Model Management – MLflow Components: Tracking, Projects, Models, Registry – Logging Experiments – Model Registration – Model Serving – MLflow Workflow. Model Storage – Model Formats: H5, SavedModel, ONNX, Pickle – Model Storage Best Practices – Cloud Storage for Models – Model Archival – Retrieving Models.

Deployment Strategies – Batch Inference – Real-time Inference – Edge Deployment – Cloud Deployment – On-premises Deployment – Deployment Options Comparison. Model Serving – REST API for Models – Flask/FastAPI for Model Serving – Model Endpoints – Request/Response Handling – Error Handling – Load Balancing. Containerization – Docker for ML Models – Creating Docker Images – Docker Compose – Container Orchestration Basics – Kubernetes Introduction – Container Best Practices.

Model Monitoring – Why Monitor Models? – Data Drift Detection – Concept Drift Detection – Performance Monitoring – Latency Monitoring – Resource Monitoring. Monitoring Metrics – Prediction Accuracy – Model Performance Metrics – Data Quality Metrics – System Metrics – Business Metrics – Alerting Thresholds. Model Retraining – When to Retrain? – Automated Retraining – Retraining Triggers – A/B Testing Models – Model Rollback – Continuous Learning Concepts.

CI/CD for ML – Continuous Integration for ML – Continuous Deployment – Testing ML Models – Model Validation – Pipeline Automation – CI/CD Tools for ML. ML Pipeline Automation – Automated Data Pipeline – Automated Training Pipeline – Automated Deployment – Pipeline Orchestration – Error Handling in Pipelines – Pipeline Monitoring. Production Best Practices – Code Quality – Documentation – Security Considerations – Cost Optimization – Scalability – Disaster Recovery – Compliance.