SKYMIRROR ACADEMY AI & Machine Learning: Intelligent Systems Development 16-Week Intensive Program COURSE OVERVIEW Duration: 16 weeks (4 months) Format: Hybrid (online theory + hands-on projects) Prerequisites: Basic programming knowledge (Python preferred), mathematics fundamentals Target Audience: Aspiring AI/ML engineers, data scientists, developers seeking AI specialization LEARNING PATH STRUCTURE MODULE 0: 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 - Course overview and project-based learning approach MODULE 1: MACHINE LEARNING FUNDAMENTALS (Weeks 1-2) Goal: Understand core ML concepts and supervised learning Topics: - Introduction to machine learning types and applications - Supervised learning: regression and classification - Model evaluation metrics and cross-validation - Feature engineering and data preprocessing - Scikit-learn library fundamentals - Overfitting, underfitting, and bias-variance tradeoff Project: House price prediction model with feature engineering MODULE 2: ADVANCED MACHINE LEARNING (Weeks 3-4) Goal: Master unsupervised learning and ensemble methods Topics: - Unsupervised learning: clustering, dimensionality reduction - K-means, hierarchical clustering, 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 MODULE 3: NEURAL NETWORKS & DEEP LEARNING (Weeks 5-6) Goal: Build and train neural networks from scratch Topics: - Neural network fundamentals and perceptrons - Backpropagation and gradient descent optimization - Deep learning frameworks: TensorFlow and PyTorch - Activation functions, loss functions, and optimizers - Regularization techniques: dropout, batch normalization - Model architecture design and hyperparameter tuning Project: Multi-class image classifier using neural networks MODULE 4: CONVOLUTIONAL NEURAL NETWORKS (Weeks 7-8) Goal: Implement computer vision solutions with CNNs Topics: - CNN architecture: convolution, pooling, fully connected layers - Popular CNN architectures: LeNet, AlexNet, VGG, ResNet - Transfer learning and pre-trained models - Image preprocessing and data augmentation - Object detection and image segmentation basics - OpenCV for computer vision tasks Project: Real-time object detection application MODULE 5: 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 - Text preprocessing and tokenization - Bidirectional RNNs and stacked architectures Project: Stock price prediction and sentiment analysis system MODULE 6: NATURAL LANGUAGE PROCESSING (Weeks 11-12) Goal: Build intelligent text processing applications Topics: - Text preprocessing: tokenization, stemming, lemmatization - Bag of words, TF-IDF, and word embeddings - Word2Vec, GloVe, and contextual embeddings - Transformer architecture and attention mechanisms - BERT, GPT, and modern language models - Named entity recognition and part-of-speech tagging Project: Intelligent chatbot with sentiment analysis MODULE 7: COMPUTER VISION APPLICATIONS (Weeks 13-14) Goal: Develop advanced computer vision systems Topics: - Advanced image processing techniques - Face detection and recognition systems - Object tracking and motion analysis - Image segmentation and edge detection - Real-time video processing with OpenCV - Integration with web applications and APIs Project: Intelligent surveillance system with face recognition MODULE 8: 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 - Time series forecasting system for financial markets - AI-powered automation solution for specific industry Requirements: - End-to-end AI solution with data pipeline - Model training, evaluation, and deployment - Web interface or API for user interaction - Documentation and presentation of results - Production-ready code with proper testing ASSESSMENT STRATEGY - Assignments: 50% (hands-on projects and coding) - Quizzes: 20% (knowledge checks and theory) - Participation: 10% (discussions and peer reviews) - Capstone Project: 20% (final solution and presentation) - Progression Requirement: 75% minimum score to advance REQUIRED TOOLS & SOFTWARE Development Environment: - Python 3.8+ with Anaconda distribution - Jupyter Notebook and JupyterLab - Visual Studio Code with Python extensions - Git and GitHub for version control AI/ML Libraries: - Scikit-learn for traditional ML - TensorFlow and Keras for deep learning - PyTorch for research and experimentation - OpenCV for computer vision - NLTK and spaCy for NLP - Pandas and NumPy for data manipulation - Matplotlib and Seaborn for visualization Cloud Platforms: - Google Colab for GPU/TPU access - AWS/GCP/Azure for model deployment - Kaggle for datasets and competitions - Weights & Biases for experiment tracking CAREER OUTCOMES Upon completion, graduates will be able to: - Design and implement machine learning solutions - Build and deploy neural networks for various applications - Develop computer vision systems for real-world problems - Create NLP applications including chatbots and text analysis - Deploy AI models to production environments - Evaluate and optimize model performance STUDENT SUPPORT - Weekly live coding sessions and Q&A - Industry mentor guidance from AI professionals - Peer learning groups and project collaboration - Portfolio development and career guidance - Job placement assistance and interview preparation © 2025 Skymirror Academy. 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