Overview
This lecture outlines a modern, step-by-step approach to learning machine learning (ML) in 2025, emphasizing practical skills, foundational math, key resources, and the importance of building projects and sharing your work.
Step 1: Learn Python Basics
- Python is the main programming language in machine learning.
- Focus on understanding lists, dictionaries, for loops, if-else statements, list comprehensions, and class inheritance.
- Use free resources like YouTube and Google to start learning Python and always code along.
- Build simple projects for practice, such as calculators or games, but don't spend too long at this stage.
Step 2: Understand Essential Math
- Most ML requires basic undergraduate-level math, not advanced topics.
- Learn derivatives, vectors, matrices, and core probability theory concepts (especially Bayes' Rule).
- Pick up useful math tricks (logarithm and summation rules) as needed.
- Recommended resource: "Why Machines Learn" book for intuitive, ML-focused math explanations.
- Supplement with YouTube videos, Google searches, and LLMs, but verify information.
Step 3: Study Classical Machine Learning
- Classical ML is foundational and should not be skipped.
- Use Andrew Ng’s Machine Learning Specialization to learn models like logistic regression, decision trees, and recommendation systems.
- Gain practical experience by coding and implementing ML pipelines with tools like TensorFlow.
Step 4: Learn Deep Learning
- Decide between an applied path (quick job-readiness) or a theoretical path (in-depth understanding, research).
- For applied learning, complete Deep Learning Specialization by Andrew Ng and watch Stanford’s CS25 series for Transformers.
- For in-depth learning, use the free book "Understanding Deep Learning" for theory, exercises, and a comprehensive curriculum.
Step 5: Build Projects & Practice
- Practical experience is crucial; start with beginner Kaggle projects using libraries like NumPy, Pandas, and Matplotlib.
- Gradually move to more advanced projects and consider re-implementing ML research papers as you progress.
- Reading papers and examining others’ code enhances understanding and skill.
Step 6: Showcase Your Work
- Document and share your learning and projects through blog posts, LinkedIn, or demo websites.
- Writing about your work can lead to valuable opportunities (internships, networking).
- Advanced: Write up and publish papers as your skills grow.
Key Terms & Definitions
- Python — A popular programming language for ML due to its readability and libraries.
- Derivative — A measure of how a function changes as its input changes.
- Vector/Matrix — Structures representing sets of numbers, essential for data representation in ML.
- Probability Theory — Math field covering likelihood and uncertainty, core for ML.
- Transformer Architecture — A modern deep learning model, crucial for advanced ML applications.
- Kaggle — An online platform for ML competitions and sharing data science projects.
Action Items / Next Steps
- Search for a beginner Python tutorial and code along.
- Get "Why Machines Learn" and start reading.
- Enroll in Andrew Ng’s Machine Learning and Deep Learning Specializations.
- Explore Stanford CS25 lecture series on Transformers.
- Register on Kaggle and start a beginner project.
- Share your progress through a blog post or social media update.