Course Content
Introduction to the Course
0/1
Introduction
01:53
Machine Learning. An Introduction.
0/2
What is machine learning? Key ML Terminology.
06:45
Practice. Predict the price of houses. Dataset 1. Beginner’s Guide to Jupyter
10:24
Practice. How does machine learning work?
0/3
How does machine learning work? Prediction of construction time and cost.
07:13
Practice. Prediction of price and time. Data upload and preparation (Part 1/2)
08:57
Practice. Prediction of price and time. Evaluation Metrics (Part 2/2)
10:40
Workflow of a Machine Learning project.
0/1
Workflow of a Machine Learning project. Stages of the Machine Learning Modeling
05:11
Practice. San Francisco – explore Building Permits Data. Build Predictive Model.
0/2
Practice. Data loading and preparation to Analyzing (Part 1/2).
12:32
Practice. Cost Prediction. Way to build a Predictive Model (Part 2/2).
14:08
Machine Learning in Construction. Predicting the Time and Cost of Projects . 📚 9 Lessons

📚 Additional materials on topics covered in the course:

K-means clustering
https://en.wikipedia.org/wiki/K-means_clustering

Machine Learning Basics with the K-Nearest Neighbors Algorithm
https://towardsdatascience.com/machine-learning-basics-with-the-k-nearest-neighbors-algorithm-6a6e71d01761

🔎 Topics covered in this course:

  • Prediction of time and cost for small training dataset

  • K-means supervised Machine Learning algorithm

  • Understanding K-means Clustering in Machine Learning

  • Overview of Machine Learning Algorithms

Previous
Next
0% Complete

Subscribe Newsletter


Share