Machine Learning Models in Science
- Offered byCoursera
Machine Learning Models in Science at Coursera Overview
Duration | 12 hours |
Start from | Start Now |
Total fee | Free |
Mode of learning | Online |
Difficulty level | Intermediate |
Official Website | Explore Free Course  |
Credential | Certificate |
Machine Learning Models in Science at Coursera Highlights
- Flexible deadlines Reset deadlines in accordance to your schedule.
- Shareable Certificate Earn a Certificate upon completion
- 100% online Start instantly and learn at your own schedule.
- Course 2 of 4 in the AI for Scientific Research Specialization
- Intermediate Level S'ome basic Python knowledge
- Approx. 12 hours to complete
- English Subtitles: English
Machine Learning Models in Science at Coursera Course details
- This course is aimed at anyone interested in applying machine learning techniques to scientific problems. In this course, we'll learn about the complete machine learning pipeline, from reading in, cleaning, and transforming data to running basic and advanced machine learning algorithms. We'll start with data preprocessing techniques, such as PCA and LDA. Then, we'll dive into the fundamental AI algorithms: SVMs and K-means clustering. Along the way, we'll build our mathematical and programming toolbox to prepare ourselves to work with more complicated models. Finally, we'll explored advanced methods such as random forests and neural networks. Throughout the way, we'll be using medical and astronomical datasets. In the final project, we'll apply our skills to compare different machine learning models in Python.
Machine Learning Models in Science at Coursera Curriculum
Before the AI: Preparing and Preprocessing Data
Course Introduction
Setting Up the Environment
Module Introduction
Anatomy of a Dataset (I)
Anatomy of a Dataset (II)
Data Preprocessing Techniques
Calculating Eigenvalues and Eigenvectors
Introduction to PCA
Math of PCA
PCA in Action (I)
PCA in Action (II)
Introduction to LDA
Data Preprocessing
PCA Explained
Matrix Multiplication
LDA in Practice
Practice Quiz: Eigenvalues and Eigenvectors
Data Preprocessing Techniques
Foundational AI Algorithms: K-Means and SVM
Module Introduction
Machine Learning in Science
Supervised and Unsupervised Learning Techniques
K-Means vs K-Nearest Neighbors
Unsupervised vs Supervised Learning
Sci-kit Learn Docs: K-Means Clustering
Scikit-Learn Docs: Support Vector Machines
Practice Quiz: K-Means and SVM
Basics of Machine Learning
Advanced AI: Neural Networks and Decision Trees
Module Introduction
Decision Trees
Understanding Random Forests
What is a Neural Network
Neural Networks Explanation and History
Practice Quiz: Neural Networks using scikit-learn
Course Project
Machine Learning Models in Science at Coursera Admission Process
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