Introduction to Machine Learning - Part 1 & 2
Tue 25 Feb 2025 10:00 AM - Wed 26 Feb 2025 2:30 PM
The Bridge, Dumfries, DG2 9AW
Description
Summary
Welcome to the fascinating world of machine learning (ML)! Machine learning is a branch of artificial intelligence (AI) that allows computers to learn without explicit programming. It empowers them to identify patterns and make predictions based on data, revolutionizing various fields.
Eligibility
You need to be a Dumfries and Galloway region resident at the age of 19 and above, the caveat to that is that you're a school leaver with no positive destination in mind.
Pre-Requisite
This course is designed for beginners with no prior experience in machine learning. Basic and Intermediate Python Programming Course is required. A basic understanding of mathematics (algebra, statistics) is helpful but not mandatory.
Course Duration
Two Days (1000 to 1430 hours GMT) with a 30 minutes break in both sessions.
Course Objectives
- Gain a foundational understanding of machine learning concepts and terminology.
- Explore the different types of machine learning algorithms (supervised, unsupervised, reinforcement).
- Learn the basic steps involved in building a machine learning model.
- Practice data pre-processing techniques for machine learning tasks.
- Gain hands-on experience with implementing simple machine learning models using Python libraries.
Outline
Day 1
Introduction to Machine Learning (1 hour)
- What is machine learning?
- Applications of machine learning in various domains
- Different types of machine learning (supervised, unsupervised, reinforcement)
- Machine learning workflow (data acquisition, pre-processing, model training, evaluation)
Supervised Learning (1 hour)
- Classification and regression problems in machine learning
- Common supervised learning algorithms (linear regression, decision trees, K-Nearest Neighbors)
- Understanding model training, prediction, and evaluation metrics
Data Pre-processing for Machine Learning (1 hour)
- Importance of data cleaning and handling missing values
- Feature scaling and normalization techniques
- Introduction to feature engineering concepts
Day 2
Hands-on Lab 1: Data Pre-processing and Supervised Learning with Python (1.5 hours)
- Introduction to Python libraries for machine learning (Scikit-learn)
- Using Python to load data, pre-process, and perform exploratory data analysis
- Implementing simple supervised learning models (linear regression, decision trees)
- Evaluating model performance and interpreting results
Unsupervised Learning (1 hour)
- Introduction to unsupervised learning for data exploration and clustering
- Common unsupervised learning algorithms (K-Means clustering, Principal Component Analysis)
- Applications of unsupervised learning in real-world scenarios
Machine Learning with Big Data (1 hour)
- Challenges of handling large datasets in machine learning
- Introduction to tools and techniques for big data machine learning (e.g., Spark)
- Understanding the importance of scalable algorithms and distributed computing
Course Wrap-up (30 min)
- Q&A and Discussion on key machine learning concepts
- Introduction to further learning resources for machine learning studies
- Course Feedback
Note: This course emphasizes hands-on learning through a lab session using Python libraries like Scikit-learn. The focus is on providing a basic understanding of core concepts and practical experience with simple algorithms. If time allows, briefly introduce additional topics like model selection, hyperparameter tuning, or model deployment considerations.
Location
The Bridge, Dumfries, DG2 9AW