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Machine Learning with Python – Course Overview & Traffic Prediction Project

In this video, I present an overview of my key learnings from the Alison Machine Learning course, covering both the theoretical foundations and practical applications of Machine Learning using Python.

🔹 Introduction to Machine Learning

What is Machine Learning?

Deterministic vs Probabilistic models

Supervised vs Unsupervised Learning

Regression modeling steps

The goal of ML: Generalization to unseen data

🔹 Key Algorithms Explained

K-Nearest Neighbours (KNN) – Distance metrics, hyperparameters, curse of dimensionality

Decision Trees – Gini Impurity, Entropy, Information Gain, Pruning

Ensemble Learning – Bagging, Boosting, and Random Forest

Support Vector Machines (SVM) – Soft margins, Kernel functions, Gamma effects

K-Means Clustering – Strengths and weaknesses

Principal Component Analysis (PCA) – Dimensionality reduction and real-world applications

🌍 Real-World Applications Covered

Fraud detection

Video classification

Crop classification

Facial recognition

Image compression

Traffic prediction systems