Machine Learning Fundamentals Explained | AWS ML Associate Episode :1

In this video, I walk through the core concepts of Machine Learning. I break down the three main learning paradigms — Supervised, Unsupervised, and Reinforcement Learning — and explain how data is partitioned into training, validation, and test sets, along with cross-validation techniques. I also dive into the bias-variance tradeoff, covering overfitting and underfitting with practical intuition, and explore feature engineering essentials like encoding, scaling/normalization, and imputation strategies for handling missing data. Finally, I cover the evaluation metrics every ML practitioner should know — accuracy, precision, recall, F1-score, AUC for classification, and MAE, MSE, RMSE, and R² for regression. What You'll Learn: ✅Supervised, Unsupervised, and Reinforcement Learning ✅Train/Validation/Test splits and Cross-Validation ✅Overfitting, Underfitting, and the Bias-Variance Tradeoff ✅Feature Engineering: Encoding, Scaling, and Imputation ✅Classification Metrics: Accuracy, Precision, Recall, F1, AUC ✅Regression Metrics: MAE, MSE, RMSE, R² #MachineLearning #AWS #AWSCloud #CloudComputing #DataScience #MLFundamentals #SupervisedLearning #FeatureEngineering #BiasVarianceTradeoff #SageMaker #ComputerScience #ConcordiaUniversity #AWSCertification