Lecture Notes: How to Become an AI Engineer in 2024

Introduction

• Presenter: Asan Sharma
• Prediction: AI will be the biggest trend of 2024 and the coming decade.
• Goal: Share steps to become an AI engineer from scratch.

What is AI and Machine Learning?

• Basic Definition: Machine learning is the process where a system recognizes patterns and predicts future outcomes.
• Core Concept: Training a blackbox with input-output pairs to identify patterns and make predictions with probabilities.

Step-by-Step Guide

Step 1: Learn Mathematics

• Foundation: Essential for building AI knowledge.
• **Key Areas: **
  • Calculus (Differentiation & Integration)
  • Linear Algebra
  • Probability
• **Resources: **
  • Three Blue One Brown (Videos)
  • KH Academy
  • Free Code Camp

Step 2: Learn Python

• Why Python? It's user-friendly and heavily used in AI and ML.
• **Key Concepts to Learn: **
  • Data types
  • Conditional statements (If conditions)
  • Loops (For loops)
  • Functions
  • Object-Oriented Programming (OOP) Concepts
• **Further Learning: **
  • Tech with Tim (Playlist)
  • Free Code Camp (Tutorials)

Step 3: Data Analysis with Python

• **Libraries to Learn: **
  • Numpy: Handling arrays and matrices.
  • Pandas: Handling tabular data (CSV files).
  • Matplotlib: Visual data representation (bar charts, pie charts, etc.).

Step 4: Pick a Framework

• Popular Frameworks: Pytorch, Sklearn, TensorFlow
• Recommendation: Start with Pytorch or Sklearn for beginners; move to TensorFlow later.

Step 5: Understand Types of Machine Learning Models

• **Three Main Types: **
  • Supervised Learning: Labeled data (e.g., classification, regression).
  • Unsupervised Learning: Unlabeled data (e.g., clustering).
  • Reinforcement Learning: Optimizing for rewards (e.g., game characters).
• **Key Concepts: **
  • Regression vs. Classification models
  • K-nearest neighbors, logistic regression, polynomial regression
• **Resources: **
  • Coursera (Deep Learning AI’s Machine Learning Specialization)

Step 6: Implementing Neural Networks and Deep Learning

• Neural Networks: Layers of neurons processing data to optimize outcomes.
• Deep Neural Networks: Multiple layers for complex problem solving.
• **Key Topics: **
  • Backpropagation
  • Hyperparameters (learning rate, weights, biases)
• **Resources: **
  • Andrej Karpathy (YouTube tutorial)
  • CS50 AI Course (Neural networks section)

Step 7: Convolutional Neural Networks (CNN)

• Use-case: Image classification.
• Concept: Pixels, RGB values, and pattern recognition.
• **Resources: **
  • CS50 AI Course

Step 8: Natural Language Processing (NLP) and RNN

• NLP: Understanding and working with human language.
• **Resources: **
  • Hugging Face (NLP course)

Step 9: Generative AI and Tools

• Generative AI: Building applications with tools like ChatGPT, Stable Diffusion.
• **Educational Resources: **
  • DeepLearning.AI (ChatGPT tutorials)
  • LangChain documentation

Step 10: Utilizing GPT Plugins and GPT Store

• Opportunity: Similar to App Store; create custom GPTs and monetize them.
• Skills Required: Prompt engineering, GPT customization.

Conclusion

• **Summary: **
  • Learn Python and its libraries.
  • Understand machine learning and neural networks.
  • Dive into deep learning and generative AI.
• Next Steps: Explore additional resources mentioned and begin building AI tools.
• Engagement: Questions welcomed in the comments; options to connect on social media.