Pioneering Applied Machine Learning with Generative AI

10 AUGUST 2024 | 09:30AM - 05:30PM | location La Marvella :- 2nd Block, Jayanagar, Bengaluru

About the workshop

Join us for an immersive workshop designed to unveil the essence of machine learning and enhance your technical prowess. This workshop covers everything from foundational concepts like regression and classification to advanced topics such as model evaluation and tree-based models. Dive into prompt engineering, explore linear and tree-based models, and master advanced ML workflows to elevate your data science skills to new heights.

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Instructor

Modules

  • Unveiling the Essence of Machine Learning
  • Exploring types of ML: Supervised, Unsupervised, Reinforcement
  • Understanding ML problem types: Regression, Classification

  • Generating insights using EDA and Statistics
  • Mastering Linear Regression
  • Unleashing the Potential of Logistic Regression
  • Harnessing the power of Regularization (L1/L2)

  • What is prompt engineering?
  • Understanding basic elements of prompts
  • Different prompt engineering techniques
  • Tips and best practices to write effective prompts
  • Learn the best prompts for linear models

  • Navigating Training and Validation Models
  • Exploring Evaluation Metrics: Accuracy, RMSE, ROC, AUC, Confusion Matrix, Precision, Recall, F1 Score
  • Overcoming Overfitting and Bias-Variance trade-off
  • Embracing K-fold Cross Validation
  • Write effective prompts for model evaluation

  • Unraveling the intricacies of Decision Trees
  • Harnessing the power of Bagging and Boosting
  • Unleashing the potential of Random Forest
  • Mastering Gradient Boosting Machines
  • Discovering Feature Importance Techniques
  • Build effective prompts for tree-based models

  • Streamlining Model Pipelines
  • Unveiling the art of Feature Engineering and Selection
  • Craft best prompts for feature engineering
  • Model Hyperparameter tuning and brief on Auto ML

  • Basic understanding of mathematics, particularly statistics and linear algebra
  • Familiarity with Python programming
  • Understanding of fundamental machine learning concepts
  • Experience with data analysis and visualization libraries (e.g., Pandas, Matplotlib)
  • Familiarity with Jupyter Notebooks or similar interactive development environments like Google Colab or VS code
  • Basic understanding of natural language processing concepts (for prompt engineering sections)
*Note: These are tentative details and are subject to change.
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