Advanced Machine Learning Techniques
( 25 Modules )

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

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Advanced Machine Learning Techniques ( 25 Modules )

Advanced Machine Learning Techniques
( 25 Modules )