Machine Learning 4-Day Schedule
There are many machine learning courses online, but few that are specifically tailored to engineers. This course has the fundamental background that is common to other courses, but the case studies and learning exercises are specifically created for engineers. The case studies are a valuable starting point for engineering-specific applications.
As machine learning continues to influence various industries, this course equips engineers with valuable skills to tackle real-world problems and make data-driven decisions. The hands-on group project further enhances their learning experience, preparing them for practical applications. The course covers a wide range of topics, including unsupervised and supervised learning methods, classification, and regression. Through mathematical details and case studies, students gain an intuitive understanding of machine learning while learning to implement state-of-the-art methods using Python. The course is 4 days with 4-8 learning activities each day.
Day 1 (Time) | Machine Learning for Engineers |
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8:45 AM | Log-in and System Check |
9 AM |
Course Overview
Install Python Install Packages |
9:30 AM | 1️⃣ Classification Overview |
10:30 PM | Break |
10:45 PM | 2️⃣ Classification Case Study |
12 PM | Lunch Break |
12:45 PM | 3️⃣ Regression Overview |
2 PM | Break |
2:15 PM | 4️⃣ Regression Case Study |
3:30 PM | Knowledge Assessment and Review |
4 PM | Conclude Day 1 |
Day 2 | Data Science with TCLab |
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9 AM | TCLab Project / Introduction / Help |
9:30 AM | 1️⃣ TCLab Overview |
10:00 AM | 2️⃣ Import Data |
10:30 AM | 3️⃣ Statistics |
11 AM | 4️⃣ Visualize |
11:30 AM | 5️⃣ Prepare Data |
12 PM | Lunch Break |
12:45 PM | 6️⃣ Regression |
1:45 PM | 7️⃣ Features |
2:15 PM | Break |
2:30 PM | 8️⃣ Classification |
3:30 PM | Knowledge Assessment and Review |
4 PM | Conclude Day 2 |
Day 3 | Data-Engineering with Python |
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9 AM | 1️⃣ k-Nearest Neighbors |
10:30 AM | Break |
10:45 AM | 2️⃣ Logistic Regression |
12 PM | Lunch Break |
12:45 PM | 3️⃣ Additive Manufacturing Case Study |
2 PM | Break |
2:15 PM | 4️⃣ Defect Detection Case Study |
3:30 PM | Knowledge Assessment and Review |
4 PM | Conclude Day 3 |
Day 4 | Self-Guided Project |
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9 AM | Individual / Group Project Introduction |
12 PM | Lunch Break |
2:15 PM | Break |
2:30 PM | Presentations |
3:30 PM | Summarize Course and Certificates |
4 PM | Conclude Day 4 |