Machine Learning for Unstructured Data Analysis
( 25 Modules )

Module #1
Introduction to Unstructured Data Analysis
Overview of unstructured data, its importance, and challenges in analysis

Module #2
Types of Unstructured Data
Text, image, audio, and video data:characteristics, examples, and applications

Module #3
Machine Learning Fundamentals
Basics of machine learning, supervised and unsupervised learning, and model evaluation

Module #4
Data Preprocessing for Unstructured Data
Data cleaning, normalization, and feature extraction techniques for unstructured data

Module #5
Text Data Analysis:Introduction to NLP
Natural Language Processing (NLP) concepts, tokenization, and text preprocessing

Module #6
Text Feature Extraction Techniques
Bag-of-words, TF-IDF, word embeddings (Word2Vec, GloVe), and topic modeling

Module #7
Text Classification:Sentiment Analysis and Beyond
Supervised learning approaches for text classification, including sentiment analysis

Module #8
Image Data Analysis:Introduction to Computer Vision
Fundamentals of computer vision, image types, and image preprocessing

Module #9
Image Feature Extraction Techniques
Convolutional Neural Networks (CNNs), transfer learning, and image features

Module #10
Image Classification and Object Detection
Image classification, object detection, and segmentation using CNNs

Module #11
Audio Data Analysis:Introduction to Audio Processing
Audio signals, audio features, and audio preprocessing

Module #12
Audio Feature Extraction Techniques
Audio feature extraction, including spectrograms and mel-frequency cepstral coefficients

Module #13
Audio Classification and Tagging
Audio classification, tagging, and recommendation using machine learning

Module #14
Video Data Analysis:Introduction to Video Processing
Video signals, video features, and video preprocessing

Module #15
Video Feature Extraction Techniques
Video feature extraction, including optical flow and 3D convolutional networks

Module #16
Video Classification and Activity Recognition
Video classification, activity recognition, and object detection in videos

Module #17
Deep Learning for Unstructured Data Analysis
Introduction to deep learning architectures for unstructured data analysis

Module #18
Recurrent Neural Networks (RNNs) for Sequential Data
RNNs, LSTM, and GRU for text, audio, and video data analysis

Module #19
Transfer Learning and Fine-tuning for Unstructured Data
Using pre-trained models and fine-tuning for unstructured data analysis

Module #20
Unstructured Data Analysis in Practice
Case studies and applications of machine learning for unstructured data analysis

Module #21
Handling Imbalanced Datasets and Class Imbalance
Techniques for handling class imbalance in unstructured data analysis

Module #22
Model Evaluation and Selection for Unstructured Data
Metrics and techniques for evaluating and selecting machine learning models

Module #23
Explainability and Interpretability in Machine Learning
Techniques for explaining and interpreting machine learning models for unstructured data

Module #24
Ethical Considerations in Unstructured Data Analysis
Ethical implications of machine learning for unstructured data analysis

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Machine Learning for Unstructured Data Analysis career

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Machine Learning for Unstructured Data Analysis ( 25 Modules )

Machine Learning for Unstructured Data Analysis
( 25 Modules )