Data Science

8.

Advanced Machine Learning Topics

8.1.

Ensemble Methods

8.1.1.

8.1.1.1.

Bootstrap Aggregating

8.1.1.2.

8.1.1.3.

8.1.2.

8.1.2.1.

8.1.2.2.

Gradient Boosting

8.1.2.3.

8.1.2.4.

8.1.2.5.

8.1.3.

8.1.3.1.

8.1.3.2.

8.1.3.3.

Multi-level Stacking

8.1.4.

8.1.4.1.

8.1.4.2.

8.1.4.3.

Weighted Voting

8.2.

8.2.1.

Neural Network Fundamentals

8.2.1.1.

8.2.1.2.

Multi-layer Perceptron

8.2.1.3.

Universal Approximation Theorem

8.2.2.

Training Neural Networks

8.2.2.1.

Backpropagation Algorithm

8.2.2.2.

Gradient Descent Variants

8.2.2.2.1.

Stochastic Gradient Descent

8.2.2.2.2.

Mini-batch Gradient Descent

8.2.2.2.3.

8.2.2.2.4.

8.2.2.3.

Learning Rate Scheduling

8.2.2.4.

Batch Normalization

8.2.2.5.

8.2.3.

Activation Functions

8.2.3.1.

8.2.3.2.

8.2.3.3.

8.2.3.4.

8.2.3.5.

8.2.3.6.

8.2.4.

Deep Learning Architectures

8.2.4.1.

Convolutional Neural Networks

8.2.4.1.1.

Convolution Operation

8.2.4.1.2.

8.2.4.1.3.

CNN Architectures

8.2.4.1.4.

Applications in Computer Vision

8.2.4.2.

Recurrent Neural Networks

8.2.4.2.1.

8.2.4.2.2.

Long Short-Term Memory

8.2.4.2.3.

Gated Recurrent Unit

8.2.4.2.4.

Bidirectional RNNs

8.2.4.3.

Transformer Architecture

8.2.4.3.1.

Attention Mechanism

8.2.4.3.2.

8.2.4.3.3.

Multi-Head Attention

8.2.4.3.4.

Positional Encoding

8.2.5.

Regularization Techniques

8.2.5.1.

L1 and L2 Regularization

8.2.5.2.

8.2.5.3.

8.2.5.4.

Data Augmentation

8.2.6.

Deep Learning Frameworks

8.2.6.1.

8.2.6.2.

8.2.6.3.

8.3.

Natural Language Processing

8.3.1.

Text Preprocessing

8.3.1.1.

8.3.1.2.

8.3.1.3.

Stop Word Removal

8.3.1.4.

8.3.1.5.

8.3.2.

Text Representation

8.3.2.1.

8.3.2.2.

8.3.2.3.

Word Embeddings

8.3.2.3.1.

8.3.2.3.2.

8.3.2.3.3.

8.3.2.4.

Contextual Embeddings

8.3.2.4.1.

8.3.2.4.2.

8.3.2.4.3.

8.3.3.

8.3.3.1.

Sentiment Analysis

8.3.3.2.

Named Entity Recognition

8.3.3.3.

Part-of-Speech Tagging

8.3.3.4.

Text Classification

8.3.3.5.

Machine Translation

8.3.3.6.

Question Answering

8.3.4.

8.3.4.1.

Latent Dirichlet Allocation

8.3.4.2.

Non-negative Matrix Factorization

8.3.4.3.

Latent Semantic Analysis

8.4.

Computer Vision

8.4.1.

Image Processing Fundamentals

8.4.1.1.

Image Representation

8.4.1.2.

8.4.1.3.

Image Filtering

8.4.1.4.

8.4.2.

Feature Extraction

8.4.2.1.

8.4.2.2.

8.4.2.3.

8.4.2.4.

Local Binary Patterns

8.4.3.

Deep Learning for Computer Vision

8.4.3.1.

CNN Architectures

8.4.3.1.1.

8.4.3.1.2.

8.4.3.1.3.

8.4.3.1.4.

8.4.3.1.5.

8.4.3.2.

Object Detection

8.4.3.2.1.

8.4.3.2.2.

8.4.3.2.3.

8.4.3.3.

Image Segmentation

8.4.3.3.1.

Semantic Segmentation

8.4.3.3.2.

Instance Segmentation

8.4.3.3.3.

8.5.

Time Series Analysis and Forecasting

8.5.1.

Time Series Components

8.5.1.1.

8.5.1.2.

8.5.1.3.

8.5.1.4.

8.5.2.

8.5.2.1.

Testing for Stationarity

8.5.2.2.

8.5.2.3.

Transformation Methods

8.5.3.

Time Series Models

8.5.3.1.

Autoregressive Models

8.5.3.2.

Moving Average Models

8.5.3.3.

8.5.3.4.

8.5.3.5.

Exponential Smoothing

8.5.3.6.

State Space Models

8.5.4.

Machine Learning for Time Series

8.5.4.1.

Feature Engineering for Time Series

8.5.4.2.

Cross-validation for Time Series

8.5.4.3.

Deep Learning Approaches

8.5.4.3.1.

RNNs for Time Series

8.5.4.3.2.

8.5.4.3.3.

Transformer Models

8.5.5.

Forecasting Evaluation

8.5.5.1.

Mean Absolute Error

8.5.5.2.

Mean Squared Error

8.5.5.3.

Mean Absolute Percentage Error

8.5.5.4.

8.5.5.5.

Forecast Accuracy Measures

8.6.

Anomaly Detection

8.6.1.

Types of Anomalies

8.6.1.1.

Point Anomalies

8.6.1.2.

Contextual Anomalies

8.6.1.3.

Collective Anomalies

8.6.2.

Statistical Methods

8.6.2.1.

8.6.2.2.

8.6.2.3.

8.6.3.

Machine Learning Methods

8.6.3.1.

Isolation Forest

8.6.3.2.

8.6.3.3.

Local Outlier Factor

8.6.3.4.

8.6.4.

Time Series Anomaly Detection

8.6.4.1.

Statistical Process Control

8.6.4.2.

Seasonal Decomposition

8.6.4.3.

LSTM Autoencoders

8.7.

Recommendation Systems

8.7.1.

Collaborative Filtering

8.7.1.1.

User-based Collaborative Filtering

8.7.1.2.

Item-based Collaborative Filtering

8.7.1.3.

Matrix Factorization

8.7.2.

Content-based Filtering

8.7.2.1.

Feature Extraction

8.7.2.2.

Similarity Measures

8.7.2.3.

Profile Building

8.7.3.

Hybrid Approaches

8.7.3.1.

Weighted Hybrid

8.7.3.2.

Switching Hybrid

8.7.3.3.

8.7.4.

Deep Learning for Recommendations

8.7.4.1.

Neural Collaborative Filtering

8.7.4.2.

8.7.4.3.

Deep Matrix Factorization

8.7.5.

Evaluation Metrics

8.7.5.1.

8.7.5.2.

8.7.5.3.

Mean Average Precision

8.7.5.4.

Normalized Discounted Cumulative Gain

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