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Addressing_Multicollinearity.py
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Bandit_Example_Fixed.py
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Comparing_Ensembles.py
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Distribution_Analysis.py
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DuckDB in python
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EDA_Pandas.py
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etlt
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Event Driven Events
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Explain different gradient descent algorithms, their advantages, and limitations.
Explain the curse of dimensionality
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fact table
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Factor_Analysis.py
facts
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FastAPI_Example.py
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Forward Propagation in Neural Networks
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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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Handling_Missing_Data_Basic.ipynb
Handling_Missing_Data.ipynb
Handwritten Digit Classification
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Heatmaps_Dendrograms.py
heterogeneous features
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High cross validation accuracy is not directly proportional to performance on unseen test data
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
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Imbalanced_Datasets_SMOTE.py
Immutable vs mutable
Impact of multicollinearity on model parameters
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In NER how would you handle ambiguous entities
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inference versus prediction
information theory
Input is Not Properly Sanitized
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Interpreting logistic regression model parameters
Interquartile Range (IQR) Detection
interview notepad
ipynb
Isolation Forest and Its Use in Anomaly Detection
Java vs JavaScript
JavaScript
Johnson–Lindenstrauss lemma
Json
Json to Yaml
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Justfile
K_Means.py
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K-nearest neighbours
Kaggle Abalone regression example
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Kmeans vs GMM
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Language Models Large (LLMs) vs Small (SLMs)
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lemmatization
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LightGBM vs XGBoost vs CatBoost
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Linked List
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Microsoft Access
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Momentum.py
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nbconvert
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nltk
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Normalisation vs Standardisation
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npy Files A NumPy Array storage
OLTP
oltp (online transactional processing)
One Pager Template
One_hot_encoding.py
One-hot encoding
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Optimising a Logistic Regression Model
Optimising Neural Networks
Optuna
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Over parameterised models
Overfitting in Machine Learning
p values
p-values in linear regression in sklearn
Pandas
Pandas Dataframe Agent
Pandas Pivot Table
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Pandas_Common.py
Pandas_Stack.py
Parametric tests
parametric vs non-parametric models
parametric vs non-parametric tests
Parquet
parsimonious
Part of speech tagging
PCA Explained Variance Ratio
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PCA_Analysis.ipynb
PCA_Based_Anomaly_Detection.py
PCA-Based Anomaly Detection
pdoc
PDP and ICE
Percentile Detection
Performance Dimensions
Performance Drift in Machine Learning
Physical Model
Poetry
Positional Encoding
PostgreSQL
PowerBI
Powerquery
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Powershell versus cmd
Powershell vs Bash
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Precision or Recall
Precision-Recall Curve
Preprocessing
Prevention Is Better Than the Cure
Primary Key
Principal Component Analysis
Probability in other fields
Problem Definition
programming languages
Prompt engineering
Prompt Extracting information from blog posts
Prompting
Proportion Test
Publish and Subscribe
Pull Request Template
PyCaret
Pycaret_Anomaly.ipynb
Pycaret_Example.py
Pydantic
Pydantic_More.py
Pydantic.py
Pyright
Pyright vs Pydantic
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Pytest
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Python Click
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Pytorch vs Tensorflow
Q-Learning
Quartz
QUERY GSheets
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Querying
QuickSort
R
R squared
R-squared metric not always a good indicator of model performance in regression
Race Conditions
RAG
Random Forest Regression
Random Forests
React
Reasoning tokens
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Recommender systems
Recurrent Neural Networks
Recursive Algorithm
Regression Analysis and its Applications
Regression metrics
Regression_Logistic_Metrics.ipynb
Regularisation of Tree based models
Regularisation.py
Regularization in Machine Learning
Reinforcement learning
Relating Tables Together
Relational Database
Relationships in memory
requirements.txt
REST API
Reward Function
Ridge
ROC (Receiver Operating Characteristic)
ROC_Curve.py
rollup
Row-based Storage
Sarsa
Scala
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Scaling Agentic Systems
Scaling Server
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Scientific Method
Search
Security
semantic layer
Semantic Relationships
Sentence Similarity
shapefile
SHapley Additive exPlanations
Sharepoint
Silhouette Analysis
Single source of truth
Sklearn
sklearn datasets
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Small Language Models
Smart Grids
SMOTE (Synthetic Minority Over-sampling Technique)
SMSS
Snowflake
Snowflake Schema
Software Design Patterns
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SparseCategorialCrossentropy or CategoricalCrossEntropy
Specificity
Spreadsheets vs Databases
SQL Groupby
SQL Injection
SQL Joins
SQL vs NoSQL
SQL Window functions
SQLAlchemy
SQLAlchemy vs. sqlite3
SQLite
SQLite Studio
Stacking
Standard deviation
Standardisation
Star Schema
Statistical Assumptions
Statistical Tests
Statistics
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Stochastic Gradient Descent
Stored Procedures
Strongly vs Weakly typed language
Summarisation
Supervised Learning
Support Vector Classifier (SVC)
Support Vector Machines
Support Vector Regression
SVM_Example.py
Symbolic computation
Sympy
syntactic relationships
t-SNE
T-test
Tableau
Technical Analysis of Named Entity Recognition
Technical Debt
Technical Design Doc Template
Telecommunications
Tensorflow
Terminal commands
Test Loss When Evaluating Models
Testing
Testing_Pytest.py
Testing_unittest.py
Text2Cypher
TF-IDF
The Data Hierarchy of Needs
Thinking Systems
Time Series
Time Series Forecasting
Time Series Identify Trends and Patterns
Tokenisation
TOML
tool.bandit
tool.ruff
tool.uv
Train-Dev-Test Sets
Transaction
Transfer Learning
transfer_learning.py
Transformed Target Regressor
Transformer
Transformers vs RNNs
TS_Anomaly_Detection
TS_Anomaly_Detection.py
Turning a flat file into a database
Types of Computational Bugs
Types of Database Schema
Types of Neural Networks
TypeScript
Typical Output Formats in Neural Networks
Ubuntu
UML
unittest
univariate vs multivariate
unstructured data
Unsupervised learning
Untitled
Use Cases for a Simple Neural Network Like
Use of RNNs in energy sector
Utilities
Vacuum
vanishing and exploding gradients problem
variance
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)
What algorithms or models are used within the energy sector
What algorithms or models are used within the telecommunication sector
What are Data Processing Techniques (row-based, columnar, vectorized)?
What are the best practices for evaluating the effectiveness of different prompts
What are the top Cloud Providers?
What can ABM solve within the energy sector
What is a Data Lake?
What is a Data Lakehouse?
What is a Data Product?
What is a Data Warehouse?
What is a Jinja Template?
What is a Lambda Architecture?
What is a Metric?
What is a policy in RL
What is a Push-Down?
What is a Soft Delete?
What is a Storage Layer / Object Store?
What is an In-Memory Format?
What is Apache Airflow?
What is Apache Spark?
What is Business Intelligence
What is Dagster?
What is Data Governance?
What is Data Integration?
What is Data Lineage?
What is Data Literacy?
What is Data Observability?
What is Data Quality?
What is data transformation?
What is declarative?
What is DevOps?
What is ETL?
What is Functional Programming?
What is Granularity
What is imperative?
What is Kubernetes?
What is Machine Learning?
What is MapReduce?
What is Master Data Management (MDM)?
What is Normalization?
What is OLAP (Online Analytical Processing)?
What is Reverse ETL?
What is Schema Evolution?
What is semi-structured data?
What is Slowly Changing Dimension?
What is SQL?
What is structured data?
What is the Big-O Notation?
What is the difference between odds and probability
What is the role of gradient-based optimization in training deep learning models.
What is YAML?
When and why not to us regularisation
Why and when is feature scaling necessary
Why does increasing the number of models in a ensemble not necessarily improve the accuracy
Why does label encoding give different predictions from one-hot encoding
Why does the Adam Optimizer converge
Why is named entity recognition (NER) a challenging task
Why is the Central Limit Theorem important when working with small sample sizes
Why JSON is Better than Pickle for Untrusted Data
Why Type 1 and Type 2 matter
Why use ER diagrams
Wikipedia_API.py
Windows Subsystem for Linux
Word2vec
Word2Vec.py
Wrapper Methods
XGBoost
Z-Normalisation
Z-NormalisationZ-Score
Z-Test
univariate vs multivariate
Single feature versus multiple features