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OVERVIEW
What is Octai?
GETTING STARTED
Connect your data
Manipulate data
Train ML models
Production
IMPORT
Import
Overview
Summary Statistics
Charts
Line
Scatter
Bar
Pie
Heatmap
Sunburst
Sankey
Geomap
Boxplot
Radar
Tree
Treemap
Calendar
Candlestick
Funnel
MINE
Mine
File Preprocess Functions
Merge
Merge as of
Copy
Concat
Pivot
Aggregate
Split
Stack
Unstack
Melt
Transpose
Script
Feature Flow
Arithmetic
Statistics
Functions
Calendar
Lambda
Window
Assign
Shift
Normalizer
Encoder
TFIDF
Post Process Functions
Filter
Sort
Rename
Fill Na
Column Filter
Drop Duplicates
Drop Na
Convert
String Operations
Replace
Clip
Sample
Resample
MODEL
Model
Metrics
Log Loss
Accuracy
F1 Score
Precision
Roc Auc Score
Balanced Accuracy
Average Precision
Matthew's Correlation Coefficient
Recall Score
F05 Score
F2 Score
Gini Coefficient
AUC Precision Recall
Weightless Cohen Kappa
Linear Cohen Kappa
Quadratic Cohen Kappa
DEPLOY
Deploy
CASE STUDIES
Churn Prediction
Predictive Maintenance
EXAMPLE PROJECTS
House Price Prediction
Safe Driver Prediction
Energy Predictor
MACHINE LEARNING 101
Introduction to ML
Lesson 1: Understanding Machine Learning
What is Machine Learning?
Supervised Learning
Unsupervised Learning
Reinforcement Learning
Semi-Supervised Learning
Machine Learning vs. Traditional Programming
Real-World Applications of Machine Learning
Lesson 2: Key Concepts in Machine Learning
Data
Features
Independent Variable
Dependent Variable/ Target
Labels
Models
Algorithms
Evaluation Metrics
Categorical Encoding
Overfitting and Underfitting
Cross-Validation
Median
Visualization
Lesson 3: Popular Machine Learning Algorithms
Regression
Linear Regression
Logistic Regression
Tree-based
Decision Trees
Random Forests
Gradient Boosting
LightGBM
Support Vector Machines
k-Nearest Neighbors
Neural Networks
Principal Component Analysis
k-Means Clustering
Lesson 4: Deep Learning and Neural Networks
Artificial Neural Networks
Convolutional Neural Networks
Recurrent Neural Networks
Autoencoders
Transfer Learning
Reinforcement Learning in Deep Learning
Lesson 5: Tools and Libraries for Machine Learning
Python and Machine Learning
NumPy and Pandas
Scikit-learn
TensorFlow
Keras
PyTorch
Jupyter Notebooks
Lesson 6: Implementing Machine Learning Solutions
Train-Test Split
Validation
Tuning Hyperparameters
Experiment
Lesson 7: Ethical Considerations in Machine Learning
Bias and Fairness
Transparency and Explainability
Privacy and Security
Responsible AI Development and Deployment
FAQ
Frequently Asked Questions
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Lesson 2: Key Concepts in Machine Learning
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