Lecture Notes on Machine Learning Roadmap

Introduction

• Excitement about sharing the roadmap for machine learning (ML).
• Key resources will be provided in the description: mind map, GitHub repository, slides, timestamps.
• Features a visual interactive mind map for ML.

Exploring the Roadmap

• Covers topics such as ML problems, processes, resources, and tools.
• Emphasis on mathematics underlying ML algorithms.
• Encouragement to playfully explore the roadmap using the Whimsical tool.
• Overview of main topics:
  • ML problems
  • ML process
  • Learning resources
  • Tools
  • Mathematics

What is Machine Learning?

• Definition: ML converts data into numbers and finds patterns in those numbers.
• Arthur Samuel's definition: "ML gives computers the ability to learn without being explicitly programmed."
• Contrast between traditional programming (Software 1.0) and ML (Software 2.0).
• ML requires traditional programming for implementation.

ML vs Traditional Programming

• Traditional programming: manually coding steps to achieve a task.
• Example: Coding a recipe vs. using ML to find optimal steps by learning from data.
• ML uses data inputs and desired outputs to learn patterns, unlike manually writing every rule.

Benefits and Use Cases of ML

• Benefits of using ML:
  • Problems with long lists of rules (e.g., self-driving cars).
  • Continually changing environments.
  • Discovering insights within large collections of data.
• Example: Tesla's use of ML for self-driving cars – collecting data from cameras and using it to improve driving algorithms.

Key Topics Covered

1. Machine Learning Problems

   • Categories: Supervised, Unsupervised, Transfer Learning, Reinforcement Learning.
   • Problem Domains: Classification, Regression, Clustering, Dimensionality Reduction.

2. ML Process Steps

   • Data Collection: Where to get data, privacy, structure, storage.
   • Data Preparation: Exploratory Data Analysis (EDA), data preprocessing, feature engineering.
   • Model Training: Choosing an algorithm, training, reducing overfitting.
   • Analysis & Evaluation: Metrics, model performance.
   • Serving Models: Deployment, inference time, retraining models as needed.

3. ML Tools

   • Libraries: TensorFlow, PyTorch, scikit-learn, etc.
   • Experiment Tracking: TensorBoard, Weights & Biases.
   • Pre-trained Models: TensorFlow Hub, Hugging Face Transformers, Detectron2.
   • Data & Model Tracking: DVC, Weights & Biases Artifacts.
   • Cloud Compute Services: Google Colab, AWS, GCP, Azure.
   • AutoML & Hyperparameter Tuning: Teapot, Google Cloud AutoML, etc.
   • Explainability: What-If Tool, SHAP Values.
   • ML Lifecycle & Deployment: MLflow, Kubeflow, Streamlit.

4. Mathematics in ML

   • Topics to understand: Linear Algebra, Matrix Multiplication, Multivariate Calculus, Chain Rule, Probability, Optimization.
   • Recommended approach: Start with coding, learn math as needed.

5. Learning Resources

   • Beginner Path: Concepts, tools, Python, NumPy, pandas, scikit-learn; milestones projects.
   • Advanced Path: Fastai, TensorFlow, Full Stack Deep Learning, milestone projects.
   • Supplementary Resources: Courses, books, articles, communities for ongoing learning.

Summary

• Reviewed important aspects of ML: problems, processes, tools, mathematics, and resources.
• Emphasized practical exploration and project-based learning.
• Encouraged to customize learning paths and continually seek new understanding.