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DuckDB in python
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ETL 1
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etlt
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Event Driven Events
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Excel
Excel 1
Excel pivot table
Excel vs Google Sheets
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Exponential Smoothing Forecasting
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fact table
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Factor_Analysis.py
facts
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FastAPI
FastAPI_Example.py
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Feature_Distribution.py
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gaussian_mixture_model_implementation.py
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Generative AI From Theory to Practice
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Gini Impurity vs Cross Entropy
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Gitlab
gitlab-ci.yml
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Good Enough Principle in Data Projects
Google Cloud Platform
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Google My Maps Data Extraction
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Google Sheets
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Graph Theory Community
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Groupby vs Crosstab
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Guardrails
Hadoop
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Handling_Missing_Data_Basic.ipynb
Handling_Missing_Data.ipynb
Hash
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Heatmaps_Dendrograms.py
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High cross validation accuracy is not directly proportional to performance on unseen test data
Histogram
Honkit
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How businesses use Gen AI
How do we evaluate of LLM Outputs
how do you do the data selection
How is reinforcement learning being combined with deep learning
How is schema evolution done in practice with SQL
How LLMs store facts
How to do git commit messages properly
How to normalise a merged table
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How to search within a graph
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How would you decide between using TF-IDF and Word2Vec for text vectorization
html
Hugging Face
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Imbalanced_Datasets_SMOTE.py
Immutable vs mutable
Impact of multicollinearity on model parameters
imperative
Implementing Database Schema
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In NER how would you handle ambiguous entities
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Indexing in cypher
Industries of interest
Inertia K Means Cost Function
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inference versus prediction
information theory
initialization methods
Input is Not Properly Sanitized
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interoperable
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Interpreting logistic regression model parameters
Interquartile Range (IQR) Detection
ipynb
Isolated Forest
Jaccard Coefficient
Java
Java vs JavaScript
JavaScript
jinja template
Jobs to be done
Johnson–Lindenstrauss lemma
Joining Datasets
Joining Time Series
Json
Json to SQLite
Junction Tables
Jupyter Book
jupytext
Justfile
K_Means.py
K-means
K-nearest neighbours
Keras
Kernel Density Estimation
Kernel Machines
Kernelling
Key Components of Attention and Formula
Kmeans vs GMM
KNIME
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Knowledge graph vs RAG setup
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kubernetes
L1 Regularisation
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Label encoding vs One-hot encoding
Labelling data
Lagrange multipliers in optimisation
lambda architecture
Langchain
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Language Models
Language Models Large (LLMs) vs Small (SLMs)
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lemmatization
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LightGBM vs XGBoost vs CatBoost
Linear Discriminant Analysis
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Linked List
linkedin learning
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Logistic regression in sklearn & Gradient Descent
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Looker Studio
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Loss versus Cost function
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MariaDB
MariaDB vs MySQL
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Markov Decision Processes
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Mathematical Reasoning in Transformers
Mathematics
Maximum Likelihood Estimation
mean absolute error
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mean vs median
melt
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metric
Microsoft
Microsoft Access
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Missing Data
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ML Engineer
MLOPS for Time Series
MNIST
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model-agnostic feature importance
Modern data team
Momentum
Momentum.py
MongoDB
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Multi-head attention
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Multicollinearity
Multinomial Naive bayes
Multiple Correspondence Analysis
Multiprocessing
Multiprocessing vs Multithreading
Multithreading
Multivariate Analysis
MySql
Naive Bayes Classifier
Named Entity Recognition
nbconvert
nbconvert slideshows
neo4j
neomodel
NER Implementation
NET
Network Design
Neural network
Neural Network Classification
Neural network in Practice
Neural Scaling Laws
Ngrams
NLP
nltk
Node.JS
Non-negative Matrix Factorization
Non-negative matrix factorization in ML
non-parametric
Non-parametric tests
Normalisation
Normalisation of data
Normalisation of Text
Normalisation vs Standardisation
Normalised Schema
NoSQL
NotebookLM
npy Files A NumPy Array storage
Numpy
Object Relational Mapper
objective function
Odds
Odds vs Probability
OLAP (online analytical processing)
OLTP
One Pager Template
One_hot_encoding.py
One-hot encoding
OOV words
Operational Resilience for Growth and Adaptability
Optimisation function
Optimisation techniques
Optimising a Logistic Regression Model
Optimising Neural Networks
Optuna
Ordinary Least Squares
Orthogonalization
Outliers
Over parameterised models
Overfitting
p values
Page Rank
Pandas
Pandas Dataframe Agent
Pandas join vs merge
Pandas Pivot Table
Pandas Stack
Pandas_Common.py
Pandas_Stack.py
Pandoc
Parametric tests
parametric vs non-parametric models
parametric vs non-parametric tests
Parquet
parsimonious
Part of speech tagging
PCA Explained Variance Ratio
PCA Principal Components
PCA_Analysis.ipynb
PCA_Based_Anomaly_Detection.py
PCA-Based Anomaly Detection
pd.Grouper
pdoc
PDP and ICE
Percentile Detection
Performance Dimensions
Performance Drift
pgAdmin
Pgadmin Permissions on Windows
Physical Model
Pickle
Plotly
pmdarima
PMML
Poetry
Polars
Policy
Polynomial Regression
Positional Encoding
PostgreSQL
Postman
PowerBI
Powerquery
PowerShell
Powershell scripts
Powershell versus Command Prompt
Powershell vs Bash
Precision
Precision or Recall
Precision-Recall Curve
Prediction Intervals
Preprocessing
Preprocessing Text Classification
Prevention Is Better Than the Cure
Primary Key
Principal Component Analysis
Probability
Problem Definition
Process Based Parallelism
Process for prototyping
Processes vs Threads
programming languages
project management
Project Management Portal
Prompt engineering
prompt retrievers
Prompts
Proportion Test
Publish and Subscribe
Pull Request Template
push-down
PyCaret
Pycaret_Anomaly.ipynb
Pycaret_Example.py
Pydantic
Pydantic_More.py
Pydantic.py
PyGraphviz
PyOD
Pyright
Pyright vs Pydantic
PySpark
Pytest
Python
Python Click
PyTorch
Pytorch vs Tensorflow
Q-Learning
Q-Q Plot
Quartz
Query Optimisation
Querying
Querying Time Series
QuickSort
R
R squared
R-squared metric not always a good indicator of model performance in regression
Race Conditions
RACI
RAG
Random Access Memory
Random Forest
Random Forest Regression
Ranking models
React
Reasoning tokens
Recall
Recommender systems
Recurrent Neural Networks
Recursive Algorithm
Registering a Scheduled Task
Regression
Regression metrics
Regression_Logistic_Metrics.ipynb
Regularisation
Regularisation of Tree based models
Regularisation.py
Reinforcement learning
Relating Tables Together
Relational Database
Relationships in memory
Relu
Remaining useful life models
Reporting
REST API
Return of Experience Form
Reveal.js
reverse etl
Reward Function
Ridge
ROC (Receiver Operating Characteristic)
ROC_Curve.py
rollup
Root Mean Squared Error
Row parameters in SQL
Row-based Storage
Sammon’s Mapping
Sampling
SARIMA
Sarsa
Scala
Scalability
Scaling Agentic Systems
Scaling Data Science Capability
Scaling Server
Scatter Plots
schema evolution
Scientific Method
Scikit-Learn
Scipy
Seaborn
search
Secretary Problem
Security mitigation
Security Researcher
Security Vulnerabilities
Self Attention
Self attention vs multi-head attention
Self-Attention
semantic layer
Semantic Relationships
Semantic search
semi-structured data
Sentence Similarity
Sentence Transformer Workflow
Sentence Transformers
shapefile
SHapley Additive exPlanations
Sharepoint
Shot Learning
Silhouette Analysis
Similarity Search
Single source of truth
sklearn datasets
Sklearn Pipiline
Slowly Changing Dimension
Small Language Models
Smart Grids
SMOTE (Synthetic Minority Over-sampling Technique)
SMSS
Snowflake
Snowflake Schema
Snowflake vs Hadoop
Soft Deletion
Software Design Patterns
Software Development Life Cycle
Software Development Portal
spaCy
SparseCategorialCrossentropy or CategoricalCrossEntropy
Spearman vs Pearson Correlation
Specificity
Spectral Clustering
Spreadsheets vs Databases
SQL
SQL Groupby
SQL Injection
SQL Joins
SQL vs NoSQL
SQL Window functions
SQLAlchemy
SQLAlchemy vs. sqlite3
SQLite
SQLite Studio
stack memory
Stacking
Standard deviation
Standardisation
Star Schema
Statistical Assumptions
Statistical Tests
Statistical theorems
Statistics
Stemming
Stochastic Gradient Descent
stopwords
storage layer object store
Stored Procedures
Streamlit
Strongly vs Weakly typed language
structured data
Structuring and organizing data
Summarisation
Supervised Learning
Support Vector Classifier
Support Vector Machines
Support Vector Regression
SVM_Example.py
Symbolic computation
Sympy
syntactic relationships
t-SNE
T-test
Tableau
Technical Debt
Technical Design Doc Template
Telecommunications
Tensorflow
Terminal commands
Test Loss When Evaluating Models
Testing
Testing_Pytest.py
Testing_unittest.py
Text Classification
Text2Cypher
TF-IDF
TF-IDF Implementation
Thinking Systems
Time Series
Time Series Forecasting
Time Series Python Packages
Tokenisation
TOML
tool.bandit
tool.ruff
tool.uv
topic modeling
Train-Dev-Test Sets
Transaction
Transfer Learning
transfer_learning.py
Transformed Target Regressor
Transformer
Transformers vs RNNs
TS_Anomaly_Detection.py
Turning a flat file into a database
Type I Error (False Positive)
Type II Error (False Negative)
Types of Computational Bugs
Types of Database Schema
Types of Neural Networks
TypeScript
Typical Output Formats in Neural Networks
Ubuntu
UMAP
UML
unittest
Univariate Analysis
univariate vs multivariate
Unix
unstructured data
Unsupervised learning
Untitled
Untitled 1
Untitled 2
Untitled 3
Usability
Use Cases for a Simple Neural Network Like
Use of RNNs in energy sector
Vacuum
vanishing and exploding gradients problem
Variability in linear models
variance
Variance in ML
Vector Database
Vector Embedding
Vector_Embedding.py
Vectorisation
Vectorized Engine
Vercel
View Use Case
Views
Violin plot
Virtual environments
WCSS and elbow method
Weak Learners
Web Feature Server (WFS)
Web Map Tile Service (WMTS)
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 is named entity recognition (NER) a challenging task
Why JSON is Better than Pickle for Untrusted Data
Why Removing Outliers May Improve Regression but Harm Classification
Why standardise features
Why Type 1 and Type 2 matter
Why use ER diagrams
Wikipedia_API.py
Windows
Windows Scheduled Tasks
Windows Subsystem for Linux
Word2vec
Word2Vec.py
WordNet
Working with SMEs
Wrapper Methods
Xaiver
XGBoost
yaml
Z-Normalisation
Z-Score
Z-Scores vs Prediction Intervals
Z-Test
Sampling
There are different ways to take samples from a popultation.