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machine learning
Folder: categories/machine-learning
271 items under this folder.
29 Sept 2025
inference versus prediction
GenAI
ml
29 Sept 2025
inference
ml
29 Sept 2025
initialization methods
deep_learning
optimisation
29 Sept 2025
interoperable
explainability
29 Sept 2025
lambda architecture
modeling
orchestration
29 Sept 2025
model-agnostic feature importance
explainability
modeling
29 Sept 2025
objective function
29 Sept 2025
semi-structured data
modeling
storage
29 Sept 2025
vanishing and exploding gradients problem
deep_learning
ml
optimisation
29 Sept 2025
Type II Error (False Negative)
evaluation
29 Sept 2025
Types of Neural Networks
deep_learning
ml
29 Sept 2025
Typical Output Formats in Neural Networks
algorithm
deep_learning
exploration
29 Sept 2025
UMAP
explainability
visualization
29 Sept 2025
Unsupervised Learning
clustering
field
29 Sept 2025
Use Cases for a Simple Neural Network Like
deep_learning
29 Sept 2025
Variability in linear models
math
29 Sept 2025
Variance in ML
29 Sept 2025
Vector Embedding
language_models
math
29 Sept 2025
WCSS and elbow method
clustering
29 Sept 2025
Weak Learners
modeling
29 Sept 2025
When and why not to us regularisation
data_quality
exploration
optimisation
29 Sept 2025
Why Removing Outliers May Improve Regression but Harm Classification
anomaly_detection
29 Sept 2025
Why Type 1 and Type 2 matter
classifier
evaluation
29 Sept 2025
Why does increasing the number of models in a ensemble not necessarily improve the accuracy
modeling
29 Sept 2025
Why does the Adam Optimizer converge
optimisation
29 Sept 2025
Why standardise features
ml
preprocessing
29 Sept 2025
Wrapper Methods
optimisation
29 Sept 2025
XGBoost
optimisation
29 Sept 2025
Xaiver
deep_learning
optimisation
29 Sept 2025
conceptual data model
modeling
29 Sept 2025
emergent behavior
ml
29 Sept 2025
f-regression
explainability
statistics
29 Sept 2025
granularity
database
modeling
29 Sept 2025
Ridge
29 Sept 2025
SARIMA
explainability
ml
statistics
29 Sept 2025
Sammon’s Mapping
29 Sept 2025
Scikit-Learn
analysis
29 Sept 2025
Secretary Problem
learning
optimisation
probability
statistics
29 Sept 2025
Sentence Transformers
deep_learning
NLP
29 Sept 2025
Sklearn Pipeline
29 Sept 2025
Specificity
evaluation
29 Sept 2025
Spectral Clustering
29 Sept 2025
Supervised Learning
field
29 Sept 2025
Support Vector Classifier
classifier
29 Sept 2025
Support Vector Machines
classifier
clustering
29 Sept 2025
Support Vector Regression
algorithm
regressor
29 Sept 2025
Tensorflow
deep_learning
software
29 Sept 2025
Test Loss When Evaluating Models
evaluation
29 Sept 2025
Text Classification
analysis
classifer
NLP
29 Sept 2025
Time Series Python Packages
anomaly_detection
modeling
29 Sept 2025
Train-Dev-Test Sets
modeling
29 Sept 2025
Transfer Learning
modeling
29 Sept 2025
Transformed Target Regressor
regressor
transformation
29 Sept 2025
Transformer
deep_learning
NLP
29 Sept 2025
Transformers vs RNNs
deep_learning
29 Sept 2025
Type I Error (False Positive)
evaluation
29 Sept 2025
Precision-Recall Curve
evaluation
29 Sept 2025
Precision
evaluation
29 Sept 2025
Prediction Intervals vs Confidence Interval
ml
statistics
time_series
29 Sept 2025
Principal Component Analysis
cleaning
visualization
29 Sept 2025
PyCaret
ml
python
software
29 Sept 2025
PyOD
anomaly_detection
python
29 Sept 2025
PyTorch
deep_learning
python
29 Sept 2025
Pytorch vs Tensorflow
deep_learning
ml
29 Sept 2025
Q-Learning
algorithm
regressor
29 Sept 2025
ROC (Receiver Operating Characteristic)
evaluation
29 Sept 2025
Random Forest for Time Series
classifier
ml_process
time_series
29 Sept 2025
Random Forest
classifier
29 Sept 2025
Recall
evaluation
29 Sept 2025
Recommender systems
evaluation
modeling
29 Sept 2025
Recurrent Neural Networks
deep_learning
time_series
29 Sept 2025
Regression Metrics
code_snippet
evaluation
29 Sept 2025
Regression
regressor
statistics
29 Sept 2025
Regularisation of Tree based models
evaluation
explainability
optimisation
29 Sept 2025
Regularisation
explainability
optimisation
process
visualization
29 Sept 2025
Reinforcement learning
field
ml
29 Sept 2025
Relationships in memory
language_models
memory_management
29 Sept 2025
Reward Function
deep_learning
29 Sept 2025
Neural Scaling Laws
drafting
29 Sept 2025
Neural network in Practice
deep_learning
29 Sept 2025
Neural network
deep_learning
drafting
29 Sept 2025
Non-negative matrix factorization in ML
29 Sept 2025
Non-parametric tests
evaluation
statistics
29 Sept 2025
Normalisation of data
data_quality
modeling
statistics
29 Sept 2025
Normalisation vs Standardisation
ml
statistics
29 Sept 2025
One-hot encoding
preprocessing
transformation
29 Sept 2025
Optimisation function
optimisation
selection
29 Sept 2025
Optimisation techniques
optimisation
process
29 Sept 2025
Optimising Neural Networks
deep_learning
optimisation
29 Sept 2025
Optimising a Logistic Regression Model
ml
optimisation
29 Sept 2025
Optuna
optimisation
29 Sept 2025
Ordinary Least Squares
ml
29 Sept 2025
Orthogonalization
explainability
ml
29 Sept 2025
Outliers
anomaly_detection
cleaning
statistics
29 Sept 2025
Over parameterised models
explainability
modeling
29 Sept 2025
PCA Explained Variance Ratio
explainability
29 Sept 2025
PCA Principal Components
explainability
ml
29 Sept 2025
PCA-Based Anomaly Detection
anomaly_detection
29 Sept 2025
PDP and ICE
evaluation
29 Sept 2025
Percentile Detection
anomaly_detection
29 Sept 2025
Performance Drift
data_quality
evaluation
explainability
29 Sept 2025
Polynomial Regression
ml
modeling
29 Sept 2025
Positional Encoding
deep_learning
NLP
29 Sept 2025
Precision or Recall
evaluation
29 Sept 2025
Maximum Likelihood Estimation
modeling
statistics
29 Sept 2025
Median Absolute Error
29 Sept 2025
Mermaid
modeling
29 Sept 2025
Metadata Handling
explainability
29 Sept 2025
Methods for Handling Outliers
anomaly_detection
preprocessing
29 Sept 2025
Metric
business
evaluation
29 Sept 2025
Mini-batch gradient descent
math
ml
optimisation
29 Sept 2025
Model Building
modeling
selection
29 Sept 2025
Model Deployment using PyCaret
29 Sept 2025
Model Ensemble
architecture
modeling
29 Sept 2025
Model Evaluation vs Model Optimisation
evaluation
optimisation
29 Sept 2025
Model Evaluation
evaluation
modeling
29 Sept 2025
Model Interpretability
explainability
29 Sept 2025
Model Observability
explainability
modeling
29 Sept 2025
Model Optimisation
29 Sept 2025
Model Parameters Tuning
optimisation
selection
29 Sept 2025
Model Parameters
modeling
optimisation
29 Sept 2025
Model Selection
evaluation
optimisation
process
29 Sept 2025
Model Validation
evaluation
modeling
29 Sept 2025
Model parameters vs hyperparameters
modeling
29 Sept 2025
Momentum
optimisation
29 Sept 2025
Moving Average Forecast
forecasting
ml
time_series
29 Sept 2025
Multinomial Naive bayes
classifier
ml
statistics
29 Sept 2025
Multiple Linear Regression
29 Sept 2025
Naive Bayes Classifier
analysis
classifer
ml
NLP
probability
29 Sept 2025
Naive Forecast
29 Sept 2025
Neural Network Classification
deep_learning
29 Sept 2025
LLM Evaluation Metrics
evaluation
NLP
29 Sept 2025
Label encoding vs One-hot encoding
preprocessing
29 Sept 2025
Labelling data
process
29 Sept 2025
Lagrange multipliers in optimisation
math
optimisation
29 Sept 2025
Latent Dirichlet Allocation
explainability
NLP
29 Sept 2025
Latent Semantic Indexing
29 Sept 2025
Learning Curve
evaluation
ml
29 Sept 2025
Learning Rate
optimisation
29 Sept 2025
Learning Styles
architecture
29 Sept 2025
LightGBM vs XGBoost vs CatBoost
ml
29 Sept 2025
LightGBM
optimisation
29 Sept 2025
Linear Regression
regressor
29 Sept 2025
Local Interpretable Model-agnostic Explainations
explainability
29 Sept 2025
Local Outlier Factor (LOF)
clustering
29 Sept 2025
Logistic Regression Statsmodel Summary table
ml
regressor
29 Sept 2025
Logistic Regression does not predict probabilities
ml
regressor
statistics
29 Sept 2025
Logistic Regression
classifier
regressor
29 Sept 2025
Logistic regression in sklearn & Gradient Descent
ml
29 Sept 2025
Loss function
optimisation
29 Sept 2025
Loss versus Cost function
optimisation
29 Sept 2025
MLOPS for Time Series
29 Sept 2025
Machine Learning Operations
process
29 Sept 2025
Machine Learning
field
29 Sept 2025
Manifold Learning
exploration
29 Sept 2025
Markov Decision Processes
modeling
29 Sept 2025
Graph Theory Community
clustering
graph
29 Sept 2025
GridSeachCv
optimisation
29 Sept 2025
Growth Models in Time Series
29 Sept 2025
Hierarchical Clustering
clustering
29 Sept 2025
High cross validation accuracy is not directly proportional to performance on unseen test data
explainability
ml
29 Sept 2025
Histogram
29 Sept 2025
How do we evaluate of LLM Outputs
evaluation
29 Sept 2025
How to use Sklearn Pipeline
question
python
29 Sept 2025
Hyperparameter Tuning
optimisation
process
29 Sept 2025
Hyperparameter
modeling
optimisation
29 Sept 2025
Impact of multicollinearity on model parameters
evaluation
modeling
statistics
29 Sept 2025
Inertia K Means Cost Function
clustering
evaluation
29 Sept 2025
Interoperability
explainability
29 Sept 2025
Interpretability
drafting
explainability
29 Sept 2025
Interpreting logistic regression model parameters
explainability
ml
29 Sept 2025
Isolated Forest
anomaly_detection
data_quality
29 Sept 2025
Jaccard Coefficient
clustering
math
29 Sept 2025
K-means
clustering
29 Sept 2025
K-nearest neighbours
classifier
ml
29 Sept 2025
Keras
deep_learning
python
29 Sept 2025
Kernel Density Estimation
ml
statistics
29 Sept 2025
Kernelling
ml
process
29 Sept 2025
Kmeans vs GMM
clustering
29 Sept 2025
L1 Regularisation
ml
optimisation
regularization
selection
29 Sept 2025
LBFGS
optimisation
regressor
29 Sept 2025
F1 Score
evaluation
29 Sept 2025
FAISS
NLP
python
29 Sept 2025
Fact Table
database
modeling
29 Sept 2025
Feature Engineering for Time Series
modeling
time_series
29 Sept 2025
Feature Evaluation
evaluation
exploration
29 Sept 2025
Feature Extraction
ml
preprocessing
transformation
29 Sept 2025
Feature Importance
evaluation
explainability
process
29 Sept 2025
Feature Selection
evaluation
explainability
modeling
process
selection
29 Sept 2025
Feature Transformations
optimisation
29 Sept 2025
Feed Forward Neural Network
classifier
deep_learning
29 Sept 2025
Filter Methods
explainability
statistics
29 Sept 2025
Fitting weights and biases of a neural network
deep_learning
ml
29 Sept 2025
Framework for models
29 Sept 2025
GRU
deep_learning
ml
29 Sept 2025
Gaussian Model
clustering
29 Sept 2025
General Linear Regression
regressor
29 Sept 2025
Generalisation
29 Sept 2025
Generative Adversarial Networks
deep_learning
29 Sept 2025
Gini Impurity vs Cross Entropy
evaluation
29 Sept 2025
Gini Impurity
evaluation
ml
statistics
29 Sept 2025
Gradient Boosted Trees
29 Sept 2025
Gradient Boosting Regressor
regressor
29 Sept 2025
Gradient Boosting
optimisation
29 Sept 2025
Gradient Descent
optimisation
29 Sept 2025
Gradient descent in linear regression
ml
optimisation
29 Sept 2025
Graph Neural Network
graph
29 Sept 2025
Customer Growth Modeling
customer_growth
forecasting
growth_models
ml
time_series
29 Sept 2025
DBSCAN
clustering
29 Sept 2025
Data Selection in ML
selection
29 Sept 2025
Data Transformation in Machine Learning
ml
transformation
29 Sept 2025
Decision Theory
math
optimisation
29 Sept 2025
Decision Tree
classifier
regressor
29 Sept 2025
Decision Trees are Fragile
classifer
explainability
model
29 Sept 2025
Deep Learning Frameworks
deep_learning
python
29 Sept 2025
Deep Q-Learning
deep_learning
29 Sept 2025
Dendrograms
clustering
visualization
29 Sept 2025
Determining Threshold Values
evaluation
selection
29 Sept 2025
Dimension Table
database
modeling
29 Sept 2025
Dimensional Modelling
database
modeling
29 Sept 2025
Dimensionality Reduction
process
visualization
29 Sept 2025
Dimensions
modeling
29 Sept 2025
Distributions in Decision Tree Leaves
classifier
explainability
29 Sept 2025
Dropout
deep_learning
optimisation
29 Sept 2025
Dummy variable trap
ml
modeling
preprocessing
29 Sept 2025
Edge ML
architecture
29 Sept 2025
Encoding Categorical Variables
cleaning
preprocessing
regressor
29 Sept 2025
Epoch
deep_learning
ml
29 Sept 2025
Evaluating Language Models
evaluation
language_models
29 Sept 2025
Evaluating Logistic Regression
29 Sept 2025
Evaluating the effectiveness of prompts
GenAI
question
29 Sept 2025
Evaluation Metrics
code_snippet
evaluation
29 Sept 2025
Exploration vs Exploitation
deep_learning
29 Sept 2025
Exponential Smoothing
analysis
ml
time_series
29 Sept 2025
Anomaly Detection in Time Series
anomaly_detection
time_series
29 Sept 2025
Anomaly Detection with Clustering
anomaly_detection
clustering
29 Sept 2025
Anomaly Detection with Statistical Methods
anomaly_detection
ml
statistics
29 Sept 2025
Anomaly Detection
anomaly_detection
29 Sept 2025
Assessing Gen AI generated content
evaluation
GenAI
29 Sept 2025
AutoML
exploration
ml
29 Sept 2025
Automated Feature Creation
transformation
29 Sept 2025
Backpropagation
deep_learning
optimisation
statistics
29 Sept 2025
Batch Normalisation
ml
29 Sept 2025
Bias in ML
architecture
explainability
29 Sept 2025
Binary Classification
classifier
ml
29 Sept 2025
Boosting
architecture
explainability
29 Sept 2025
Business value of anomaly detection
anomaly_detection
business
29 Sept 2025
CART
classifer
mlprocess
regressor
29 Sept 2025
CatBoost
ml
python
29 Sept 2025
Challenges to Model Deployment
data_governance
data_pipeline
data_security
devops
explainability
systems
29 Sept 2025
Class Separability
data_quality
evaluation
29 Sept 2025
Classification Report
evaluation
29 Sept 2025
Classification
classifier
ml
29 Sept 2025
Cluster Density
clustering
29 Sept 2025
Cluster Seperation
clustering
29 Sept 2025
Clustering
clustering
29 Sept 2025
Collaborative Filtering
NLP
recommendation
29 Sept 2025
Confusion Matrix
evaluation
29 Sept 2025
Cost Function
ml
optimisation
29 Sept 2025
Cost-Sensitive Analysis
evaluation
29 Sept 2025
Cross Entropy
architecture
optimisation
29 Sept 2025
AIC in Model Evaluation
evaluation
29 Sept 2025
AUC
evaluation
29 Sept 2025
Accuracy
evaluation
29 Sept 2025
Activation Function
deep_learning
29 Sept 2025
Activation atlases
visualization
29 Sept 2025
Active Learning
classifier
29 Sept 2025
Adam Optimizer
modeling
optimisation
29 Sept 2025
Adaptive Learning Rates
learning
modeling
optimisation
29 Sept 2025
Adjusted R squared
evaluation
statistics
29 Sept 2025
Agent-Based Modelling
modeling
Explorer
categories
computer-science
Algorithms
Big O Notation
BM25 (Best Match 25)
Checksum
Computer Science
Concurrency
Convex Optimisation
csv module
Directed Acyclic Graph (DAG)
Flask
garbage collector
Generators in Python
Hash
Heap Data Structure
Heap Memory
How to search within a graph
Immutable vs mutable
Java
Java vs JavaScript
JavaScript
Knowledge Graph
Langchain
Machine Learning Algorithms
Monte Carlo Simulation
Multiprocessing vs Multithreading
Multithreading
neomodel
Node.JS
Numpy
Processes vs Threads
programming languages
PyGraphviz
QuickSort
Ranking models
Recursive Algorithm
Strongly vs Weakly typed language
Times Series Python Packages
data-analysis
Altair
altair versus seaborn
Binder
Boxplot
Dash
Dashboarding
Dashboards
Data Analysis
Data Analysis Portal
Data Analyst
Data Distribution
Data Mining
Data Product
Data Reduction
Data Visualisation
DuckDB
EDA
ER Diagrams
Heatmap
Label encoding
Linear Discriminant Analysis
Log transformation
Looker Studio
MariaDB vs MySQL
Melt
Multiple Correspondence Analysis
Multivariate Analysis
OLAP
Page Rank
Parquet
Plotly
PowerBI
Preprocessing
Preprocessing Text Classification
Seaborn
SQL Window functions
t-SNE
Tableau
data-engineering
ACID Transaction
Ada boosting
Adding a database to PostgreSQL
Aggregation
Apache Iceberg
Attack mitigation
Attack types
AWS Lambda
Azure
Bagging
Benefits of Data Transformation
Big Data
BigQuery
Cassandra
Cloud Providers
Coaching & Mentoring
Columnar Storage
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Components of the database
Covering Index
Crosstab
CRUD
CUDA
Curse of dimensionality
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Data Architect
Data Architecture
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data lineage
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design pattern
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DuckDB vs SQLite
Durability
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Estimator
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ETL 1
ETL Pipeline Example
ETL vs ELT
EtLT
Event Driven Microservices
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heterogeneous features
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Input is Not Properly Sanitized
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Microsoft Access
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Normalised Schema
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Pgadmin Permissions on Windows
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Pickle
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PostgreSQL
Postman
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Prevention Is Better Than The Cure
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Push-Down
Pydantic
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reverse etl
rollup
Row parameters in SQL
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SQL Groupby
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storage layer object store
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unstructured data
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data-science
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ARIMA
ARIMA vs Random Forest in Time Series
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Autocorrelation vs Autoregression
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Basics of Time Series
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Choosing a Threshold
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Data Understanding
Datasets
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Differencing in Time Series
DS & ML Portal
Evaluating Time Series Forecasts
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Handling Missing Data
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how do you do the data selection
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bat
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CatBoost
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Classification Report
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Clustering
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conceptual data model
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Customer Growth Modeling
Data Selection in ML
Data Transformation in Machine Learning
DBSCAN
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Decision Trees are Fragile
Deep Learning Frameworks
Deep Q-Learning
Dendrograms
Determining Threshold Values
Dimension Table
Dimensional Modelling
Dimensionality Reduction
Dimensions
Distributions in Decision Tree Leaves
Dropout
Dummy variable trap
Edge ML
emergent behavior
Encoding Categorical Variables
Epoch
Evaluating Language Models
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Evaluating the effectiveness of prompts
Evaluation Metrics
Exploration vs Exploitation
Exponential Smoothing
f-regression
F1 Score
Fact Table
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Feature Evaluation
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Generalisation
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Gini Impurity
Gini Impurity vs Cross Entropy
Gradient Boosted Trees
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Gradient Boosting Regressor
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Gradient descent in linear regression
granularity
Graph Neural Network
Graph Theory Community
GridSeachCv
Growth Models in Time Series
GRU
Hierarchical Clustering
High cross validation accuracy is not directly proportional to performance on unseen test data
Histogram
How do we evaluate of LLM Outputs
How to use Sklearn Pipeline
Hyperparameter
Hyperparameter Tuning
Impact of multicollinearity on model parameters
Inertia K Means Cost Function
inference
inference versus prediction
initialization methods
Interoperability
interoperable
Interpretability
Interpreting logistic regression model parameters
Isolated Forest
Jaccard Coefficient
K-means
K-nearest neighbours
Keras
Kernel Density Estimation
Kernelling
Kmeans vs GMM
L1 Regularisation
Label encoding vs One-hot encoding
Labelling data
Lagrange multipliers in optimisation
lambda architecture
Latent Dirichlet Allocation
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LBFGS
Learning Curve
Learning Rate
Learning Styles
LightGBM
LightGBM vs XGBoost vs CatBoost
Linear Regression
LLM Evaluation Metrics
Local Interpretable Model-agnostic Explainations
Local Outlier Factor (LOF)
Logistic Regression
Logistic Regression does not predict probabilities
Logistic regression in sklearn & Gradient Descent
Logistic Regression Statsmodel Summary table
Loss function
Loss versus Cost function
Machine Learning
Machine Learning Operations
Manifold Learning
Markov Decision Processes
Maximum Likelihood Estimation
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Metric
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Multinomial Naive bayes
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Optuna
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Over parameterised models
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Prediction Intervals vs Confidence Interval
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PyCaret
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PyTorch
Pytorch vs Tensorflow
Q-Learning
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Random Forest for Time Series
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Recommender systems
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Scikit-Learn
Secretary Problem
semi-structured data
Sentence Transformers
Sklearn Pipeline
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Time Series Python Packages
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Transformed Target Regressor
Transformer
Transformers vs RNNs
Type I Error (False Positive)
Type II Error (False Negative)
Types of Neural Networks
Typical Output Formats in Neural Networks
UMAP
Unsupervised Learning
Use Cases for a Simple Neural Network Like
vanishing and exploding gradients problem
Variability in linear models
Variance in ML
Vector Embedding
WCSS and elbow method
Weak Learners
When and why not to us regularisation
Why does increasing the number of models in a ensemble not necessarily improve the accuracy
Why does the Adam Optimizer converge
Why Removing Outliers May Improve Regression but Harm Classification
Why standardise features
Why Type 1 and Type 2 matter
Wrapper Methods
Xaiver
XGBoost
natural-language
AI Agents Memory
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Bag of words
BERT
BERTScore
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ChatGPT
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Embedded Methods
embeddings for OOV words
Evaluate Embedding Methods
Fuzzywuzzy
Generative AI
Generative AI From Theory to Practice
Grammar method
Guardrails
How businesses use Gen AI
How LLMs store facts
How to reduce the need for Gen AI responses
How would you decide between using TF-IDF and Word2Vec for text vectorization
In NER how would you handle ambiguous entities
Key Components of Attention and Formula
Knowledge graph vs RAG setup
Language Model Output Optimisation
Language Models
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Why is named entity recognition (NER) a challenging task
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WordNet
OTHER
Addressing_Multicollinearity.py
Bag_of_Words.py
Bandit example output
Bandit_Example_Fixed.py
Click_Implementation.py
Comparing_Ensembles.py
Cross_Entropy_Single.py
Cross_Entropy.py
Debugging.py
Distribution_Analysis.py
Factor_Analysis.py
FastAPI_Example.py
Feature_Distribution.py
Forecasting_AutoArima.py
Forecasting_Baseline.py
Forecasting_Exponential_Smoothing.py
Gaussian_Mixture_Model_Implementation.py
Handling_Missing_Data_Basic.ipynb
Handling_Missing_Data.ipynb
Heatmaps_Dendrograms.py
Imbalanced_Datasets_SMOTE.py
K_Means.py
Momentum.py
One_hot_encoding.py
Pandas_Common.py
Pandas_Stack.py
PCA_Analysis.ipynb
PCA_Based_Anomaly_Detection.py
Pycaret_Anomaly.ipynb
Pycaret_Example.py
Pydantic_More.py
Pydantic.py
Regression_Logistic_Metrics.ipynb
Regularisation.py
ROC_Curve.py
SVM_Example.py
Testing_Pytest.py
Testing_unittest.py
transfer_learning.py
TS_Anomaly_Detection.py
Vector_Embedding.py
Wikipedia_API.py
Word2Vec.py
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