Module 1: Python for AI & Data Science Foundations
Build the programming foundation required for AI and machine learning.
- Python fundamentals: data structures, loops, functions, OOP
- File handling and error management
- NumPy for numerical computing and array operations
- Pandas for data manipulation and preprocessing
- Data visualization using Matplotlib and Seaborn
- Working with Jupyter Notebook and development environments
Module 2: Mathematics for Machine Learning
Understand the core mathematical concepts behind AI models.
- Linear algebra: vectors, matrices, transformations
- Eigenvalues, eigenvectors, and matrix operations
- Calculus: derivatives, gradients, optimization
- Gradient descent and loss minimization
- Probability distributions and statistics
- Hypothesis testing and correlation analysis
Module 3: Machine Learning Fundamentals
Learn how machines learn from data.
- Supervised vs unsupervised learning
- Regression (Linear, Logistic)
- Classification algorithms (Decision Trees, SVM, k-NN)
- Model evaluation metrics (Accuracy, Precision, Recall, F1-score)
- Overfitting, underfitting, and bias-variance tradeoff
- Feature engineering and data preprocessing
Module 4: Advanced Machine Learning & Optimization
Improve model performance using advanced techniques.
- Ensemble learning: Random Forest, Bagging, Boosting
- Gradient boosting (XGBoost, LightGBM)
- Hyperparameter tuning (Grid Search, Random Search)
- Model interpretability (feature importance, SHAP)
- Optimization strategies for better performance
Module 5: Deep Learning & Neural Networks
Build intelligent systems using neural networks.
- Neural network architecture and activation functions
- Forward and backward propagation
- Loss functions and optimization techniques
- TensorFlow / PyTorch fundamentals
- Training deep learning models
- Regularization techniques (Dropout, BatchNorm)
Module 6: Computer Vision
Work with image data and visual AI systems.
- Convolutional Neural Networks (CNNs)
- Image classification and feature extraction
- Transfer learning with pretrained models
- Object detection basics
- Image preprocessing and augmentation
Module 7: Natural Language Processing (NLP)
Understand and process human language using AI.
- Text preprocessing (tokenization, stemming, lemmatization)
- Word embeddings (Word2Vec, GloVe)
- Sequence models (RNN, LSTM, GRU)
- Transformers (BERT, GPT basics)
- Text classification and sentiment analysis
Module 8: Unsupervised Learning & Clustering
Discover patterns and hidden insights in data.
- Clustering algorithms (K-Means, Hierarchical, DBSCAN)
- Dimensionality reduction (PCA, t-SNE)
- Anomaly detection techniques
- Customer segmentation strategies
Module 9: MLOps & Model Deployment
Learn how to deploy and scale AI systems.
- Model deployment using APIs (Flask/FastAPI)
- Model serialization and versioning
- Docker for ML applications
- Cloud deployment basics
- ML pipelines and monitoring
Module 10: Generative AI & Large Language Models (LLMs)
Work with cutting-edge AI technologies.
- GPT architecture and LLM fundamentals
- Prompt engineering techniques
- Retrieval-Augmented Generation (RAG)
- Vector databases and embeddings
- Building AI-powered applications
Module 1: Python for AI & Data Science Foundations
Build the programming foundation required for AI and machine learning.
- Python fundamentals: data structures, loops, functions, OOP
- File handling and error management
- NumPy for numerical computing and array operations
- Pandas for data manipulation and preprocessing
- Data visualization using Matplotlib and Seaborn
- Working with Jupyter Notebook and development environments
Module 2: Mathematics for Machine Learning
Understand the core mathematical concepts behind AI models.
- Linear algebra: vectors, matrices, transformations
- Eigenvalues, eigenvectors, and matrix operations
- Calculus: derivatives, gradients, optimization
- Gradient descent and loss minimization
- Probability distributions and statistics
- Hypothesis testing and correlation analysis
Module 3: Machine Learning Fundamentals
Learn how machines learn from data.
- Supervised vs unsupervised learning
- Regression (Linear, Logistic)
- Classification algorithms (Decision Trees, SVM, k-NN)
- Model evaluation metrics (Accuracy, Precision, Recall, F1-score)
- Overfitting, underfitting, and bias-variance tradeoff
- Feature engineering and data preprocessing
Module 4: Advanced Machine Learning & Optimization
Improve model performance using advanced techniques.
- Ensemble learning: Random Forest, Bagging, Boosting
- Gradient boosting (XGBoost, LightGBM)
- Hyperparameter tuning (Grid Search, Random Search)
- Model interpretability (feature importance, SHAP)
- Optimization strategies for better performance
Module 5: Deep Learning & Neural Networks
Build intelligent systems using neural networks.
- Neural network architecture and activation functions
- Forward and backward propagation
- Loss functions and optimization techniques
- TensorFlow / PyTorch fundamentals
- Training deep learning models
- Regularization techniques (Dropout, BatchNorm)
Module 6: Computer Vision
Work with image data and visual AI systems.
- Convolutional Neural Networks (CNNs)
- Image classification and feature extraction
- Transfer learning with pretrained models
- Object detection basics
- Image preprocessing and augmentation
Module 7: Natural Language Processing (NLP)
Understand and process human language using AI.
- Text preprocessing (tokenization, stemming, lemmatization)
- Word embeddings (Word2Vec, GloVe)
- Sequence models (RNN, LSTM, GRU)
- Transformers (BERT, GPT basics)
- Text classification and sentiment analysis
Module 8: Unsupervised Learning & Clustering
Discover patterns and hidden insights in data.
- Clustering algorithms (K-Means, Hierarchical, DBSCAN)
- Dimensionality reduction (PCA, t-SNE)
- Anomaly detection techniques
- Customer segmentation strategies
Module 9: MLOps & Model Deployment
Learn how to deploy and scale AI systems.
- Model deployment using APIs (Flask/FastAPI)
- Model serialization and versioning
- Docker for ML applications
- Cloud deployment basics
- ML pipelines and monitoring
Module 10: Generative AI & Large Language Models (LLMs)
Work with cutting-edge AI technologies.
- GPT architecture and LLM fundamentals
- Prompt engineering techniques
- Retrieval-Augmented Generation (RAG)
- Vector databases and embeddings
- Building AI-powered applications
