“Alone we can do so little, together we can do so much!”

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AI Helpers

AI Tools

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Gen AI (Level 0):

1. AI/ML etc

   Getting Started!

2. ⁠Foundation models

   Understanding the Beginning

   Choosing Foundation Models

3. ⁠LLMs

   Understanding Large Language Models

   LLM evaluation

4. ⁠Context window/tokens

   Understanding how to use Large Language Models

5. ⁠Prompt engineering and types of prompts

   Crafting Effective Input for Language Models

6. ⁠Turing test/ Arc AGI Benchmark

   Unraveling the Differences

7. ⁠Multi Modal LLMs

   Understanding Multi modal

8. ⁠Fine Tuning

   Finetuning

9. Transfer Learning

   Transfer Learning

   Knowledge Distillation

   Model Distillation vs Pruning

10. ⁠Vector search/Embeddings

    Embedding and Vector Search

11. RAG, its application and Pinecone vector-database

    Retrieval-Augmented Generation (RAG), Its Applications, and Pinecone Vector Database

12. ⁠Fine tuning vs RAG

    Finetuning vs RAG

13. ⁠Knowledge graphs and Knowledge graph RAG

    Knowledge Graphs & Knowledge Graph RAGs

14. ⁠Prompt chaining, Langchain

    Prompt chaining & LangChain

15. ⁠AI Agents, Tool calling

    AI agents and Tool Calling

16. Hugging face

    A Brief History: From Chatbots to AI Giants

    AI Model Marketplace and a lot more…

    Transformers Library

    Model Hub

    Hugging Face Spaces

    The Datasets Library

    Inference Widget vs Inference API vs Deploy

17. ⁠Diffusion models/Stability AI

    Diffusion Models and Stability AI: A Deep Dive into Generative AI

18. LLM Testing

    LLM Testing

19. Model Context Protocol

    Basics of MCP

PPTs

AI Landscape Presentation Slides

Create High-Impact Slides

Basics of Machine Learning (Level 1):

1. Introduction

   Basics of ML

2. Linear Regression

   Linear regression

   Solving More Complex Boundaries (Polynomial Regression)

   Gradient Descent

3. Classification Part 1

   Problems of linear regression for classification

   Logistics Regression

   Multiclass Logistics Regression

   Regularization

   Classification measures

4. Classification Part 2

   Introduction to Decision Trees

   Handling Continuous-Valued Features in Decision Trees

   Information Gain, Gain Ratio, Gini Index

   Overfitting in Decision Trees

5. Feature Scaling

   Feature scaling

6. Random Forests

   Introduction

   Data Bagging anf Feature Selection

7. Naive Bayes

   Introduction

   Probability Estimation of Discrete Valued Features

   Probabilities of Continuous Valued Data

   Text Classification Using Naive Bayes

8. KNN

9. SVM

10. PCA

11. NLP