77 Languages
Logo

Apprentice Mode
10 Modules / ~100 pages
Wizard Mode
~25 Modules / ~400 pages
🎓
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


Ready to Learn, Share, and Compete?

Language Learning Assistant
with Voice Support

Hello! Ready to begin? Let's test your microphone.
Copyright 2025 @ WIZAPE.com
All Rights Reserved
CONTACT-USPRIVACY POLICY