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 (3)
I really liked the end where we took the time to play around with CHAT GPT. The room was not set up the best for this- instead of one large table a couple of small ones so we could get into small groups and brainstorm would have helped
Nola - Laramie County Community College
Course - Artificial Intelligence (AI) Overview
Working from first principles in a focused way, and moving to applying case studies within the same day
Maggie Webb - Department of Jobs, Regions, and Precincts
Course - Artificial Neural Networks, Machine Learning, Deep Thinking
That it was applying real company data. Trainer had a very good approach by making trainees participate and compete
