🎓 My Machine Learning Learning Journey

Student: Leon Motaung

Technologies Covered: Python, NumPy, Pandas, Linear Algebra, Statistics, Machine Learning Theory

📘 Course I Studied

I began my formal Machine Learning learning by studying one of the most famous ML lectures in the world:

Stanford CS229: Machine Learning – Lecture 1 (Autumn 2018)
Taught by Prof. Andrew Ng – a globally recognized AI researcher and Co-founder of Coursera & DeepLearning.AI.

👨‍🏫 Professor Highlight

Professor: Andrew Ng
Adjunct Professor of Computer Science at Stanford University.
One of the pioneers of modern AI and Machine Learning.
His lecture provided a deep foundation for understanding ML systems.

📺 Key Topics from Lecture 1

• Introduction to Machine Learning
• Applications of ML in real-world systems
• Supervised vs. Unsupervised Learning
• Regression & Classification
• Reinforcement Learning overview
• The importance of data in ML
• ML as a combination of statistics + algorithms + data

🧠 Concepts I Learned

• ML is about learning patterns from data
• Supervised Learning: uses labeled data (e.g., predicting price or category)
• Unsupervised Learning: finds hidden patterns (e.g., clustering customers)
• Feature engineering plays a massive role in accuracy
• Data preprocessing is crucial — ML is 80% data cleaning!
• Understanding loss functions & model evaluation (RMSE, accuracy, etc.)

🧮 Mathematical Foundations

To understand ML deeply, I also started revising:

• Linear Algebra – vectors, matrices, dot products
• Calculus – derivatives & gradients for optimization
• Probability & Statistics – mean, variance, distributions
• Optimization – gradient descent & cost functions

🧭 My Next Steps

• Implement Linear Regression from scratch using Python
• Practice supervised learning projects
• Learn Logistic Regression and Classification
• Follow CS229 assignments and notes
• Start building my own ML portfolio

🔥 This course gave me a strong foundation and a clear direction in Machine Learning. I now understand the mathematical and real-world side of ML — and I’m ready to build more!