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Duration 14 hours
Course Outline
Foundations: Machine Learning and Google Colab
- Core concepts of machine learning
- Initializing and setting up Google Colab
- Reviewing essential Python features
Supervised Learning using Scikit-learn
- Constructing regression models
- Building classification models
- Assessing and enhancing model accuracy
Techniques in Unsupervised Learning
- Applying clustering algorithms
- Implementing dimensionality reduction strategies
- Learning association rules
Advanced Machine Learning Theories
- Introduction to neural networks and deep learning
- Utilizing support vector machines
- Exploring ensemble learning methods
Specialized Topics in ML
- Developing features for better model input
- Adjusting hyperparameters for optimization
- Understanding model interpretability
The Machine Learning Development Cycle
- Preparing and preprocessing data
- Selecting the most appropriate models
- Deploying trained models to production
Capstone Challenge
- Articulating the project problem statement
- Gathering and refining datasets
- Training models and conducting final evaluations
Recap and Future Directions
Requirements
- Solid grasp of fundamental programming principles
- Practical proficiency in Python coding
- Working knowledge of core statistical concepts
Target Audience
- Data scientists
- Software engineers and developers
Testimonials (2)
Getting people that never used AI some repetition in prompting and people that do use AI to consider different methods to using it.
Matthew Gay - Tarsus Pharmaceuticals
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
