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Machine Learning
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Chapter 1: Introduction To Machine Learning
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What Is Machine Learning?
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The Machine Learning Pipeline
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Key Concepts and Terminology
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Machine Learning Success Factors
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Chapter 1: Introduction To Ml
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Chapter 2: Linear Algebra
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Chapter 2: Mathematical Foundations Linear Algebra
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Chapter 3: Calculus Optimization
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Chapter 4: Probability Theory
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Chapter 5: Statistical Learning Theory
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Chapter 6: Linear Regression
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Chapter 6: Linear Regression Deep Dive
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Chapter 7: Regularization Theory
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Chapter 8: Kernel Methods
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Chapter 8: Kernel Methods For Regression
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Chapter 9: Advanced Regression
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Chapter 10: Logistic Regression And Glms
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Chapter 10: Logistic Regression Glms
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Chapter 11: Decision Trees
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Chapter 12: Support Vector Machines
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Chapter 13: Naive Bayes And Probabilistic Classifiers
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Chapter 13: Naive Bayes Probabilistic Classifiers
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Chapter 14: K Nearest Neighbors
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Chapter 15: Bagging And Random Forests
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Chapter 15: Bagging Random Forests
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Chapter 16: Boosting Theory
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Chapter 17: Modern Boosting
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Chapter 18: Model Evaluation Metrics
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Chapter 19: Cross Validation And Resampling
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Chapter 19: Cross Validation Resampling
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Chapter 20: Hyperparameter Optimization
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Chapter 21: Clustering Algorithms
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Chapter 22: Density Estimation And Mixture Models
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Chapter 22: Density Estimation Mixture Models
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Chapter 23: Dimensionality Reduction
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Chapter 24: Manifold Learning
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Chapter 25: Anomaly And Outlier Detection
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Chapter 25: Anomaly Outlier Detection
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Chapter 26: Bayesian Inference
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Chapter 27: Graphical Models
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Chapter 28: Gaussian Processes
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Chapter 29: Variational Inference
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Chapter 30: Neural Network Foundations
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Chapter 31: Backpropagation And Optimization
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Chapter 31: Backpropagation Optimization
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Chapter 32: Regularization In Deep Learning
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Chapter 33: Convolutional Neural Networks
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Chapter 34: Recurrent Neural Networks
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Chapter 35: Attention And Transformers
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Chapter 35: Attention Transformers
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Chapter 36: Generative Models
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Chapter 37: Semi Supervised And Self Supervised Learning
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Chapter 37: Semi Supervised Self Supervised Learning
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Chapter 38: Transfer Learning And Domain Adaptation
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Chapter 38: Transfer Learning Domain Adaptation
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Chapter 39: Reinforcement Learning
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Chapter 40: Recommendation Systems
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Chapter 41: Feature Engineering And Selection
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Chapter 41: Feature Engineering Selection
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Chapter 42: Automl Neural Architecture Search
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Chapter 43: Ml Interpretability And Fairness
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Chapter 43: Ml Interpretability Fairness
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Chapter 44: Research Frontiers
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Chapter 45: Ml Engineering Career
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Applied ML engineering: validate data releases
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Synchronized Data Permutation
One bad dataset row consumes several quality allowances
Count bad dataset rows once
Published datasets differ from the snapshot that passed review
Publish the rows captured by the quality run
Course overview & roadmap
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