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Data Science and Machine Learning Fundamentals [Theory Only]
Introduction to Machine Learning?
What are Machine Learning Terminologies? (2:31)
What is Machine Learning? (3:53)
Evaluation Metrics in Machine Learning
Classification vs Regression in Machine Learning (3:23)
Evaluating Performance: Classification Error Metrics (18:02)
Evaluating Performance: Regression Error Metrics (9:36)
Supervised Learning with Machine Learning
What is Supervised Learning in Machine Learning? (5:06)
Supervised Learning Algorithms
Linear Regression Algorithm Theory (7:23)
What is Bias Variance Trade-Off? (10:47)
Logistic Regression Algorithm Theory (4:29)
K-Fold Cross-Validation Theory (4:11)
Hyperparameter Optimization Theory (6:16)
K Nearest Neighbors Algorithm Theory (6:25)
Decision Tree Algorithm Theory (9:15)
Random Forest Algorithm Theory (5:43)
Support Vector Machine Algorithm Theory (5:03)
Unsupervised Learning with Machine Learning
What is unsupervised Learning in Machine Learning? (3:31)
Unsupervised Learning Algorithms
Hierarchical Clustering Algorithm Theory (4:31)
K Means Clustering Algorithm Theory (4:06)
Principal Component Analysis (PCA) Theory (8:44)
What is the Recommender System? Part 1 (4:31)
What is the Recommender System? Part 2 (4:08)
Hyperparameter Optimization Theory
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