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Omdena-Toolkit

Welcome to the central repository for Omdena's resources. This repository serves as a comprehensive guide to help Data Scientists and Machine Learning Engineers at all levels, from beginners to advanced practitioners.

Contents

This repository includes tutorials, code examples, notebooks, and libraries for a variety of topics in Data Science and Machine Learning.

1. Introduction to Data Science & Machine Learning

  • Overview of Data Science & Machine Learning
  • Key differences between Data Science and Machine Learning
  • Importance of Data in Decision Making

2. Data Science Fundamentals

  • Python for Data Science
    • Libraries: Pandas, Numpy, Matplotlib, Seaborn
    • Data Cleaning and Preprocessing
    • Exploratory Data Analysis (EDA)
  • Statistics & Probability
    • Descriptive Statistics
    • Probability Distributions
    • Hypothesis Testing
    • A/B Testing
  • Data Visualization
    • Matplotlib, Seaborn
    • Plotly, Dash
    • Tableau (Introductory tutorials)

3. Machine Learning Basics

  • Supervised Learning
    • Regression Models: Linear Regression, Lasso, Ridge, ElasticNet
    • Classification Models: Logistic Regression, Decision Trees, Random Forest, Support Vector Machines (SVM)
    • Evaluation Metrics: Accuracy, Precision, Recall, F1-Score, ROC Curve
  • Unsupervised Learning
    • Clustering: K-Means, DBSCAN, Hierarchical Clustering
    • Dimensionality Reduction: PCA, t-SNE
  • Model Tuning and Hyperparameter Optimization
    • Cross-Validation
    • Grid Search, Random Search
    • Bayesian Optimization

4. Advanced Machine Learning Techniques

  • Deep Learning
    • Neural Networks (ANN)
    • Convolutional Neural Networks (CNN)
    • Recurrent Neural Networks (RNN)
    • Transformers and Attention Mechanism
    • GANs (Generative Adversarial Networks)
  • Natural Language Processing (NLP)
    • Text Preprocessing
    • Tokenization, Lemmatization, Stemming
    • Text Classification
    • Word Embeddings (Word2Vec, GloVe)
    • Transformers (BERT, GPT)
  • Reinforcement Learning
    • Markov Decision Processes (MDP)
    • Q-Learning
    • Policy Gradient Methods

5. Libraries and Tools

  • Deep Learning Libraries
    • TensorFlow
    • Keras
    • PyTorch
    • FastAI
  • Data Manipulation and Analysis
    • Pandas
    • NumPy
    • SciPy
    • Dask
  • Model Deployment
    • Flask/Django for API Development
    • FastAPI
    • Streamlit for Interactive Dashboards
    • Docker for Containerization
    • Kubernetes for Orchestration
    • MLflow, DVC for Model Versioning
  • Other Useful Libraries
    • Scikit-learn for Classic ML
    • XGBoost, LightGBM, CatBoost for Boosting Models
    • Optuna for Hyperparameter Optimization
    • Plotly, Matplotlib, Seaborn for Visualization
    • SQL for Data Querying

6. Best Practices and Guidelines

  • Code Style and Documentation
  • Version Control with Git
  • Collaborative Work in GitHub (Forking, Pull Requests, Issues)
  • Writing Tests for Machine Learning Models
  • Model Interpretability (LIME, SHAP)
  • Deployment Pipelines (CI/CD)

7. Notebooks

  • Beginner Notebooks
    • Introduction to Python and Data Science Libraries
    • Basic EDA on Sample Datasets
    • Implementing Linear Regression
  • Intermediate Notebooks
    • K-Means Clustering Example
    • Hyperparameter Tuning with GridSearchCV
    • Building a Random Forest Classifier
  • Advanced Notebooks
    • Neural Network for Image Classification (CNN)
    • Time Series Forecasting with ARIMA
    • BERT for Text Classification
    • RL agent training using Q-Learning

8. Tutorials

  • Data Preprocessing
    • Handling Missing Values
    • Feature Engineering
    • Scaling and Normalization
  • Machine Learning
    • Model Evaluation and Selection
    • Overfitting vs Underfitting
    • Feature Importance Analysis
  • Deep Learning
    • Building a Neural Network from Scratch
    • Implementing CNNs and RNNs
    • Transfer Learning in Deep Learning

9. Real-World Projects

  • Predictive Analytics for Business
  • Image Classification (Using CNN)
  • Natural Language Processing for Sentiment Analysis
  • Recommendation Systems (Collaborative Filtering, Content-Based Filtering)

Contributing

We welcome contributions to this repository. If you have any ideas, improvements, or new content, feel free to fork the repository and submit a pull request.

License

This repository is licensed under the MIT License.

Contact

For any queries or suggestions, please contact Head of Community- Tushar Aggarwal or raise an issue.