What you will learn
- Understand the principles and categories of machine learning algorithms
- Implement linear and logistic regression models
- Work with decision trees, random forests, and support vector machines
- Apply k-nearest neighbors and clustering algorithms
- Use dimensionality reduction techniques such as PCA
- Explore neural networks and deep learning fundamentals
- Considerations and best practices for selecting and optimizing algorithms
Beneficial for
- Data Scientists
- Machine Learning Engineers
- Analysts and Researchers in Machine Learning
- Software Developers interested in machine learning algorithms
- Anyone seeking a comprehensive understanding of various machine learning algorithms
Course Pre-requisite
- Basic understanding of machine learning concepts
- Familiarity with programming (preferably Python)
- Enthusiasm for exploring and implementing machine learning algorithms is key.
Course Outline
Understanding the fundamentals of machine learning
Overview of supervised, unsupervised, and reinforcement learning
Key considerations in algorithm selection for different tasks
Basics of linear regression for predicting continuous outcomes
Mathematical foundations of linear regression models
Applications and practical considerations in linear regression
Introduction to logistic regression for binary classification
Logistic regression vs. linear regression
Extensions and applications of logistic regression
Principles of decision trees for classification and regression
Ensemble learning with random forests
Tuning parameters and optimizing random forest models
Understanding the principles of support vector machines
Linear and non-linear SVMs for classification tasks
Hyperparameter tuning and optimization in SVMs
Basics of k-nearest neighbors algorithm
Distance metrics and model selection in k-NN
Applications and limitations of k-NN
Overview of unsupervised clustering algorithms
K-means clustering and hierarchical clustering
Evaluation and applications of clustering algorithms
Dimensionality reduction with PCA
Mathematical foundations and implementation of PCA
Use cases and considerations in applying PCA
Basics of artificial neural networks
Training neural networks with backpropagation
Introduction to deep learning and its applications
Overview of ensemble learning techniques
Bagging, boosting, and stacking algorithms
Creating robust models with ensemble learning
