• Understand the fundamentals of machine learning.
• Explore linear regression and logistic regression.
• Understand classification and its evaluation metrics.
• Work with numerical and categorical data.
• Understand datasets, generalization, and overfitting.
• Explore neural networks and embeddings.
• Understand machine learning production systems.
• Explore AutoML and machine learning fairness.

Module 1: ML Models

• Linear Regression
• Logistic Regression
• Classification

Module 2: Data

• Working with Numerical Data
• Working with Categorical Data
• Datasets, Generalization, and Overfitting

Module 3: Advanced ML Models

• Neural Networks
• Embeddings
• Intro to Large Language Models

Module 4: Real-World ML

• Production ML Systems
• AutoML
• ML Fairness

5 days

Course Completion Certificate by Network Binary

• Explore linear regression using practical exercises.
• Practice classification and model evaluation.
• Work with numerical data.
• Work with categorical data and data transformations.
• Explore datasets, generalization, and overfitting.
• Practice neural network concepts.
• Explore embeddings and large language models.
• Review production ML systems and responsible ML concepts.

• Learners interested in understanding machine learning fundamentals.
• Individuals who want to explore how machine learning models work.
• Developers interested in building foundational machine learning knowledge.
• Learners preparing to explore advanced machine learning topics.

• Familiarity with variables, linear equations, graphs, histograms, and statistical means.
• Programming experience is recommended, ideally in Python.
• Basic understanding of algebra and statistics is helpful.
• Linear algebra knowledge is useful.
• Calculus is optional for advanced topics