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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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Updated 5 months ago