Module #1 Introduction to Advanced Machine Learning Overview of advanced machine learning techniques, importance, and applications
Module #2 Deep Learning Fundamentals Review of deep learning concepts, neural networks, and perceptrons
Module #3 Convolutional Neural Networks (CNNs) Architecture, applications, and implementation of CNNs for image recognition
Module #4 Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks Architecture, applications, and implementation of RNNs and LSTMs for sequence data
Module #5 Transfer Learning and Fine-Tuning Using pre-trained models and fine-tuning for specific tasks
Module #6 Transfer Learning with CNNs and RNNs Applying transfer learning to CNNs and RNNs for image and sequence data
Module #7 Deep Learning for Natural Language Processing (NLP) Applications of deep learning in NLP, including text classification and language models
Module #8 Attention Mechanisms and Transformers Architecture and applications of attention mechanisms and transformers in NLP
Module #9 Generative Adversarial Networks (GANs) Architecture, applications, and implementation of GANs for generating new data
Module #10 Variational Autoencoders (VAEs) Architecture, applications, and implementation of VAEs for dimensionality reduction
Module #11 Unsupervised Learning and Clustering Techniques and applications of unsupervised learning, including clustering and dimensionality reduction
Module #12 ervised Learning with Imbalanced Data Techniques and strategies for handling imbalanced data in supervised learning
Module #13 Ensemble Learning and Bagging Techniques and applications of ensemble learning, including bagging and boosting
Module #14 Gradient Boosting and XGBoost Architecture, applications, and implementation of gradient boosting and XGBoost
Module #15 Machine Learning for Time Series Data Techniques and applications of machine learning for time series data, including forecasting and anomaly detection
Module #16 Graph Neural Networks (GNNs) and Geometric Deep Learning Architecture, applications, and implementation of GNNs and geometric deep learning for graph-structured data
Module #17 Explainable AI and Model Interpretability Techniques and strategies for explaining and interpreting machine learning models
Module #18 Model Selection and Hyperparameter Tuning Techniques and strategies for selecting and tuning machine learning models
Module #19 Handling Missing Data and Outliers Techniques and strategies for handling missing data and outliers in machine learning
Module #20 Big Data and Distributed Machine Learning Techniques and strategies for scaling machine learning to large datasets and distributed systems
Module #21 Deep Reinforcement Learning Architecture, applications, and implementation of deep reinforcement learning for decision-making
Module #22 Meta-Learning and Few-Shot Learning Techniques and applications of meta-learning and few-shot learning for rapid adaptation
Module #23 Adversarial Attacks and Defense Techniques and strategies for attacking and defending machine learning models
Module #24 Real-World Applications of Advanced Machine Learning Case studies and applications of advanced machine learning techniques in industry and research
Module #25 Course Wrap-Up & Conclusion Planning next steps in Advanced Machine Learning Techniques career
