Machine Learning Models in Science
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.
Monthly Subscription Starting at AED 99 + VAT
- Level Professional
- المدة 12 ساعات hours
- الطبع بواسطة LearnQuest
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Offered by
عن
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.الوحدات
Course Introduction
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2
Videos
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1
Discussion
1
Discussions
- Welcome to the Course
2
Videos
- Course Introduction
- Setting Up the Environment
Anatomy of a Dataset
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3
Videos
3
Videos
- Module Introduction
- Anatomy of a Dataset (I)
- Anatomy of a Dataset (II)
Data Preprocessing Techniques
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1
Videos
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1
Readings
1
Videos
- Data Preprocessing Techniques
1
Readings
- Data Preprocessing
Eigenvalues and Eigenvectors
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1
Videos
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1
Assignment
1
Assignment
- Practice Quiz: Eigenvalues and Eigenvectors
1
Videos
- Calculating Eigenvalues and Eigenvectors
Introduction to Principal Component Analysis
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2
Videos
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1
Readings
2
Videos
- Introduction to PCA
- Math of PCA
1
Readings
- PCA Explained
Deep Dive into PCA
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3
Videos
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2
Readings
3
Videos
- PCA in Action (I)
- PCA in Action (II)
- Introduction to LDA
2
Readings
- Matrix Multiplication
- LDA in Practice
Quiz: Data Preprocessing Techniques
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1
Assignment
1
Assignment
- Data Preprocessing Techniques
Machine Learning in Science
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3
Videos
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1
Readings
3
Videos
- Module Introduction
- Machine Learning in Science
- Supervised and Unsupervised Learning Techniques
1
Readings
- Unsupervised vs Supervised Learning
K-Means vs K-NN
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1
Videos
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1
Readings
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1
Discussion
1
Discussions
- Comparing Algorithms
1
Videos
- K-Means vs K-Nearest Neighbors
1
Readings
- Sci-kit Learn Docs: K-Means Clustering
Support Vector Machines
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1
Readings
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2
Lab
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1
Programming
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1
Assignment
- Practice: Support Vector Machines
1
Assignment
- Practice Quiz: K-Means and SVM
2
Labs
- Support Vector Machines
- Programming Assignment Solutions: Practice: Support Vector Machines
1
Readings
- Scikit-Learn Docs: Support Vector Machines
Quiz: Basics of Machine Learning
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1
Assignment
1
Assignment
- Basics of Machine Learning
Tree Based Algorithms
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1
Videos
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2
Readings
1
Videos
- Module Introduction
2
Readings
- Decision Trees
- Understanding Random Forests
Introduction to Neural Networks
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2
Readings
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1
Discussion
1
Discussions
- NN Playground
2
Readings
- What is a Neural Network
- Neural Networks Explanation and History
Implementing Neural Networks
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1
Lab
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1
Assignment
1
Assignment
- Practice Quiz: Neural Networks using scikit-learn
1
Labs
- Implementing Neural Networks
Module Project: Neural Networks in Python
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1
Lab
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1
Programming
- Neural Networks in Python
1
Labs
- Programming Assignment Solutions: Neural Networks in Python
Final Project: Comparing ML Models
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1
Lab
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1
Programming
- Final Project: Comparing ML Models
1
Labs
- Programming Assignment Solutions: Final Project: Comparing ML Models
Auto Summary
Unlock the power of machine learning in scientific research with this comprehensive course. Dive into data preprocessing, fundamental AI algorithms like SVM and K-means, and advanced methods such as random forests and neural networks. Ideal for professionals in data science and AI, this course features hands-on projects using medical and astronomical datasets. Available on Coursera with Starter and Professional subscription options.
Sabrina Moore
Rajvir Dua
Neelesh Tiruviluamala