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Apprentice Mode
10 Modules / ~100 pages
Wizard Mode
~25 Modules / ~400 pages
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CREATE AN EVENT
Machine Learning Algorithm Design Techniques
( 25 Modules )
Module #1
Introduction to Machine Learning Algorithm Design
Overview of machine learning, importance of algorithm design, and course objectives
Module #2
Mathematical Foundations of Machine Learning
Review of linear algebra, calculus, probability, and optimization techniques
Module #3
Model Selection and Evaluation
Metrics for evaluating machine learning models, overfitting, and underfitting
Module #4
Bias-Variance Tradeoff
Understanding the tradeoff between bias and variance in machine learning models
Module #5
Designing Linear Regression Models
Simple and multiple linear regression, regularization, and feature engineering
Module #6
Designing Logistic Regression Models
Binary and multi-class logistic regression, logistic loss function, and regularization
Module #7
Decision Trees and Random Forests
Decision tree construction, entropy, and information gain, random forests, and ensemble methods
Module #8
Support Vector Machines (SVMs)
Maximum-margin classification, soft margin SVMs, and kernel methods
Module #9
Designing Neural Networks
Introduction to neural networks, perceptron, multilayer perceptron, and backpropagation
Module #10
Convolutional Neural Networks (CNNs)
Architecture, convolutional layers, pooling layers, and applications
Module #11
Recurrent Neural Networks (RNNs)
Architecture, recurrent layers, long short-term memory (LSTM) networks, and applications
Module #12
Unsupervised Learning Techniques
K-means clustering, hierarchical clustering, and dimensionality reduction techniques
Module #13
Dimensionality Reduction Techniques
Principal component analysis (PCA), t-SNE, and autoencoders
Module #14
Anomaly Detection Techniques
One-class SVM, local outlier factor (LOF), and isolation forest
Module #15
Ensemble Methods
Bagging, boosting, and stacking, and ensemble model selection
Module #16
Model Interpretability Techniques
Feature importance, partial dependence plots, and SHAP values
Module #17
Hyperparameter Tuning Techniques
Grid search, random search, and Bayesian optimization
Module #18
Model Selection and Hyperparameter Tuning in Practice
Case studies and best practices for model selection and hyperparameter tuning
Module #19
Handling Imbalanced Datasets
Resampling techniques, cost-sensitive learning, and class weighting
Module #20
Handling Missing Data
Imputation techniques, mean/mode imputation, and multiple imputation
Module #21
Handling High-Dimensional Data
Feature selection techniques, recursive feature elimination, and sparse models
Module #22
Designing for Deployability
Model deployment, model serving, and model monitoring
Module #23
Ethics and Fairness in Machine Learning
Fairness metrics, bias detection, and ethics in machine learning
Module #24
Case Studies in Machine Learning Algorithm Design
Real-world applications of machine learning algorithm design techniques
Module #25
Course Wrap-Up & Conclusion
Planning next steps in Machine Learning Algorithm Design Techniques career
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