Advanced Machine Learning and Deep Learning Applications

This advanced course is designed for learners who already have a foundation in Machine Learning and want to master cutting-edge ML and AI techniques. Across 10 modules, you’ll explore advanced supervised and unsupervised learning, deep learning architectures, and practical deployment strategies. You’ll begin with ensemble methods like Random Forests, XGBoost, LightGBM, and CatBoost, before diving into clustering (GMM, Hierarchical) and dimensionality reduction (t-SNE, UMAP). The course also covers Neural Networks, CNNs for image recognition, RNNs/LSTMs for sequential data and NLP, and an introduction to Reinforcement Learning. Hands-on training includes hyperparameter optimization, cross-validation, model interpretability, and deployment using Flask/FastAPI. You’ll also learn best practices for productionizing ML models, including monitoring and evaluation. - Through real-world projects, you will apply these skills to build: - Customer Churn Prediction (Ensemble Models) - Image Classification (CNN) - Sentiment Analysis (RNN/LSTM) - Time Series Forecasting (LSTM) - Recommendation Systems (Collaborative + Deep Learning) - Fraud Detection (Gradient Boosting) By the end of this course, you’ll be able to design, optimize, and deploy scalable machine learning solutions, preparing you for roles in AI Engineering, Data Science, and Advanced ML Research.