Advanced Machine Learning Algorithms
( 24 Modules )

Module #1
Introduction to Advanced Machine Learning
Overview of machine learning, importance of advanced techniques, and course objectives

Module #2
Deep Learning Fundamentals
Neural networks, perceptrons, and backpropagation

Module #3
Convolutional Neural Networks (CNNs)
Image recognition, convolutional layers, and pooling layers

Module #4
Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) Networks
Sequential data, RNNs, LSTMs, and gradient vanishing/exploding problems

Module #5
Transfer Learning and Fine-Tuning
Using pre-trained models, fine-tuning, and domain adaptation

Module #6
Ensemble Methods
Bagging, boosting, random forests, and gradient boosting machines

Module #7
Support Vector Machines (SVMs) and Kernel Methods
Maximum-margin classification, kernel trick, and support vector regression

Module #8
Gradient Boosting and XGBoost
Gradient boosting, XGBoost, and extreme gradient boosting

Module #9
Natural Language Processing (NLP) Fundamentals
Text preprocessing, tokenization, and language models

Module #10
Word Embeddings and Language Models
Word2Vec, GloVe, and BERT

Module #11
Attention Mechanisms
Self-attention, multi-head attention, and transformers

Module #12
Generative Adversarial Networks (GANs)
GANs, DCGANs, and conditional GANs

Module #13
Variational Autoencoders (VAEs) and Generative Models
VAEs, normalizing flows, and generative adversarial networks

Module #14
Recommendation Systems
Collaborative filtering, matrix factorization, and content-based filtering

Module #15
Time-Series Forecasting
ARIMA, exponential smoothing, and LSTM-based forecasting

Module #16
Unsupervised Learning and Clustering
K-means, hierarchical clustering, and density-based clustering

Module #17
Anomaly Detection
One-class SVM, local outlier factor, and density-based anomaly detection

Module #18
Explainable AI (XAI) and Model Interpretability
LIME, SHAP, and treeExplainer

Module #19
Model Evaluation and Selection
Metrics, cross-validation, and hyperparameter tuning

Module #20
Big Data and Distributed Machine Learning
Hadoop, Spark, and distributed ML algorithms

Module #21
Real-World Applications of Advanced ML
Case studies in computer vision, NLP, and recommender systems

Module #22
Deep Learning for Computer Vision
Object detection, segmentation, and tracking

Module #23
Advanced NLP Topics
Sentiment analysis, named entity recognition, and question answering

Module #24
Course Wrap-Up & Conclusion
Planning next steps in Advanced Machine Learning Algorithms career

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Advanced Machine Learning Algorithms ( 24 Modules )

Advanced Machine Learning Algorithms
( 24 Modules )