Machine Learning

ML Foundations

What is Machine Learning

Working with Data

Exploratory Data Analysis

Data Preprocessing

Feature Engineering

Regression Methods

Linear Regression Models

Classification Methods

Classification Basics

Decision Trees

Gradient Boosting

Support Vector Machines

Instance-Based Learning

Probabilistic Classifiers

Ensemble Methods

Linear Discriminant Analysis

Model Evaluation

Validation Techniques

Classification Metrics

Probabilistic Metrics

Threshold and ROC Metrics

Regression Metrics

Unsupervised Learning

Clustering Algorithms

Advanced Clustering Methods

Clustering Evaluation

Dimensionality Reduction

Association Rule Mining

Neural Network Basics

Neural Network Architecture

Loss Functions and Training

Optimization Algorithms

Regularization Techniques

Advanced Training Techniques

Computer Vision

CNN Architecture

Computer Vision Tasks

Sequence Models

Recurrent Neural Networks

Advanced RNN Architectures

Attention Mechanisms

Transformer Architecture

NLP

Word Representations

Text Preprocessing

NLP Tasks

NLP Evaluation Metrics

Generative Models

GAN Architecture

Autoencoders

Anomaly Detection

Anomaly Detection Methods

Reinforcement Learning

RL Fundamentals

RL Theory

RL Algorithms

Exploration Strategies

Time Series Analysis

Time Series Fundamentals

Applied Time Series

Recommender Systems

Recommendation Approaches

Advanced Recommendations

Production ML and MLOps

Model Diagnostics

Model Deployment

MLOps Practices

Privacy and Security

Ethics and Explainability

Advanced ML Topics

Data Quality Pitfalls

Advanced Learning Paradigms

Graph Neural Networks

Model Efficiency

Model Reliability

Domain Adaptation

Large Language Models

Working with LLMs