Course Outline
Implementing Machine Learning Algorithms with Julia
Foundational Concepts
- Supervised and unsupervised learning
- Cross-validation and model selection techniques
- The bias-variance tradeoff
Linear and Logistic Regression
(Including NaiveBayes and GLM approaches)
- Foundational concepts
- Fitting linear regression models
- Model diagnostics
- Naive Bayes implementation
- Fitting a logistic regression model
- Model diagnostics
- Model selection methods
Distance Metrics
- Understanding distance metrics
- Euclidean distance
- Cityblock (Manhattan) distance
- Cosine similarity
- Correlation distance
- Mahalanobis distance
- Hamming distance
- Mean Absolute Deviation (MAD)
- Root Mean Square (RMS)
- Mean Squared Deviation
Dimensionality Reduction
-
Principal Component Analysis (PCA)
- Linear PCA
- Kernel PCA
- Probabilistic PCA
- Independent Component Analysis (ICA)
- Multidimensional Scaling
Modified Regression Techniques
- Core principles of regularization
- Ridge regression
- Lasso regression
- Principal Component Regression (PCR)
Clustering Techniques
- K-means clustering
- K-medoids clustering
- DBSCAN
- Hierarchical clustering
- Markov Cluster Algorithm
- Fuzzy C-means clustering
Core Machine Learning Models
(Utilizing NearestNeighbors, DecisionTree, LightGBM, XGBoost, EvoTrees, and LIBSVM packages)
- Gradient boosting principles
- K-Nearest Neighbors (KNN)
- Decision tree models
- Random forest models
- XGBoost
- EvoTrees
- Support Vector Machines (SVM)
Artificial Neural Networks
(Utilizing the Flux package)
- Stochastic gradient descent and associated strategies
- Multilayer perceptrons: forward pass and backpropagation
- Regularization techniques
- Recurrent Neural Networks (RNN)
- Convolutional Neural Networks (ConvNets)
- Autoencoders
- Hyperparameter tuning
Requirements
This course targets individuals who already have a background in data science and statistics.
Testimonials (2)
the ML ecosystem not only MLFlow but Optuna, hyperops, docker , docker-compose
Guillaume GAUTIER - OLEA MEDICAL
Course - MLflow
I enjoyed participating in the Kubeflow training, which was held remotely. This training allowed me to consolidate my knowledge for AWS services, K8s, all the devOps tools around Kubeflow which are the necessary bases to properly tackle the subject. I wanted to thank Malawski Marcin for his patience and professionalism for training and advice on best practices. Malawski approaches the subject from different angles, different deployment tools Ansible, EKS kubectl, Terraform. Now I am definitely convinced that I am going into the right field of application.
