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.