Table of contents

• Introduction to Machine learning
  • Analytics
  • Categories of ML algorithms
  • A typical ML algorithm uses the following steps
  • Framework for develping ML models
• Python
  • Google colab help
  • Python OS and filesystem
  • First steps with python
  • Python variables and data types
  • Python conditionals and loops
  • Python functions and scope
  • Numpy
• Python and data science
  • Core Python Libraries for Data Analysis
  • Descriptive statistics
  • Probability distributions and hypothesis testing
  • Linear Regression
  • Advanced machine learning
• References

Machine learning tools

• Development

  • PyCharm
  • VScode
  • Jupyter

• Training

  • PyTorch Lightning
  • Scikit-Learn
  • TensorFlow
  • LightGBM
  • XGBoost
  • PyTorch

• Tuning

  • Keras tune
  • Hyperopt
  • Optuna

• Optimization

  • SparseML

• Development / packaging

  • Hugging Face Endpoints
  • DeepSparse
  • Torch Serve
  • ONNX
  • TFX

• Visualization

  • Matplotlib
  • Seaborn
  • Kangas
  • Plotly

Reference

1. Machine learning using python, Manarajan Pradhan, U Dinesh Kumar
2. Please follow lecture series provided at Jovian.ml, i.e. Data Analysis with Python: Zero to Pandas
3. Python roadmap
4. Python tutorials at w3school
5. 30 Days of Python
6. Python official documents
7. Data science roadmaps
8. Statistics, Murray R. Spiegel, Larry J. Stephens