AI & Machine Learning Curriculum
A comprehensive 20-week journey into artificial intelligence and machine learning. Build intelligent systems that analyze data, learn from patterns, and make predictions through practical, hands-on projects.
Program Investment
What's Included
- 20 week intensive program
- 24/7 mentor-assisted learning
- 12+ AI & ML projects
- GPU-enabled cloud environments
- Blockchain-verified certification
Foundations & Setup
Week 0
Goal: Establish development environment and mathematical foundations.
Topics:
- Python development environment setup (Anaconda, Jupyter, VS Code)
- Mathematics review: Linear algebra, statistics, calculus basics
- Introduction to AI/ML landscape and career paths
- Git workflow for data science projects
Machine Learning Fundamentals
Weeks 1-2
Goal: Understand core ML concepts and supervised learning.
Topics:
- Supervised & unsupervised learning, model evaluation
- Feature engineering and data preprocessing
- Scikit-learn library fundamentals
- Overfitting, underfitting, and bias-variance tradeoff
Project:
House price prediction model with feature engineering
Advanced Machine Learning
Weeks 3-4
Goal: Master unsupervised learning and ensemble methods.
Topics:
- Clustering, dimensionality reduction (PCA, t-SNE)
- Ensemble methods: Random Forest, Gradient Boosting, XGBoost
- Hyperparameter tuning and model selection
- Feature selection and importance analysis
Project:
Customer segmentation and recommendation system
Neural Networks & Deep Learning
Weeks 5-6
Goal: Build and train neural networks from scratch.
Topics:
- Neural network fundamentals and backpropagation
- Deep learning frameworks: TensorFlow and PyTorch
- Activation functions, loss functions, and optimizers
- Regularization techniques: dropout, batch normalization
Project:
Multi-class image classifier using neural networks
Convolutional Neural Networks
Weeks 7-8
Goal: Implement computer vision solutions with CNNs.
Topics:
- CNN architecture: convolution, pooling, fully connected layers
- Popular architectures: LeNet, AlexNet, VGG, ResNet
- Transfer learning and pre-trained models
- Image processing, object detection, OpenCV
Project:
Real-time object detection application
Recurrent Neural Networks
Weeks 9-10
Goal: Process sequential data with RNNs and LSTMs.
Topics:
- RNN fundamentals and vanishing gradient problem
- LSTM and GRU architectures
- Sequence-to-sequence models and attention mechanisms
- Time series forecasting and analysis
Project:
Stock price prediction and sentiment analysis system
Natural Language Processing
Weeks 11-12
Goal: Build intelligent text processing applications.
Topics:
- Text preprocessing, sentiment analysis, chatbots
- Word embeddings: Word2Vec, GloVe, contextual embeddings
- Transformer architecture and attention mechanisms
- BERT, GPT, and modern language models
Project:
Intelligent chatbot with sentiment analysis
Computer Vision Applications
Weeks 13-14
Goal: Develop advanced computer vision systems.
Topics:
- Face recognition, object detection, face recognition
- Object tracking and motion analysis
- Image segmentation and edge detection
- Real-time video processing with OpenCV
Project:
Intelligent surveillance system with face recognition
Capstone Project
Weeks 15-16
Goal: Deliver a comprehensive AI solution for real-world problems.
Capstone Options:
- Predictive Analytics Dashboard for business intelligence
- Computer Vision Application for healthcare or security
- NLP-powered content analysis and recommendation system
- AI-powered automation solution for specific industry
Requirements:
End-to-end AI solution with data pipeline, model deployment, web interface, and production-ready code
Ready to start your journey in AI & Machine Learning?
Apply to Skymirror Academy