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Machine Learning Models in Science
  • Level Professional
  • Duration 12 hours
  • Course by LearnQuest
  • Offered by

About

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.

Modules

Course Introduction

    • 2 Videos
    • 1 Discussion
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1 Discussions

  • Welcome to the Course

2 Videos

  • Course Introduction
  • Setting Up the Environment

Anatomy of a Dataset

    • 3 Videos
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3 Videos

  • Module Introduction
  • Anatomy of a Dataset (I)
  • Anatomy of a Dataset (II)

Data Preprocessing Techniques

    • 1 Videos
    • 1 Readings
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1 Videos

  • Data Preprocessing Techniques

1 Readings

  • Data Preprocessing

Eigenvalues and Eigenvectors

    • 1 Videos
    • 1 Assignment
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1 Assignment

  • Practice Quiz: Eigenvalues and Eigenvectors

1 Videos

  • Calculating Eigenvalues and Eigenvectors

Introduction to Principal Component Analysis

    • 2 Videos
    • 1 Readings
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2 Videos

  • Introduction to PCA
  • Math of PCA

1 Readings

  • PCA Explained

Deep Dive into PCA

    • 3 Videos
    • 2 Readings
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3 Videos

  • PCA in Action (I)
  • PCA in Action (II)
  • Introduction to LDA

2 Readings

  • Matrix Multiplication
  • LDA in Practice

Quiz: Data Preprocessing Techniques

    • 1 Assignment
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1 Assignment

  • Data Preprocessing Techniques

Machine Learning in Science

    • 3 Videos
    • 1 Readings
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3 Videos

  • Module Introduction
  • Machine Learning in Science
  • Supervised and Unsupervised Learning Techniques

1 Readings

  • Unsupervised vs Supervised Learning

K-Means vs K-NN

    • 1 Videos
    • 1 Readings
    • 1 Discussion
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1 Discussions

  • Comparing Algorithms

1 Videos

  • K-Means vs K-Nearest Neighbors

1 Readings

  • Sci-kit Learn Docs: K-Means Clustering

Support Vector Machines

    • 1 Readings
    • 2 Lab
    • 1 Programming
    • 1 Assignment
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1 Programming

  • 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

    • 1 Assignment
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1 Assignment

  • Basics of Machine Learning

Tree Based Algorithms

    • 1 Videos
    • 2 Readings
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1 Videos

  • Module Introduction

2 Readings

  • Decision Trees
  • Understanding Random Forests

Introduction to Neural Networks

    • 2 Readings
    • 1 Discussion
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1 Discussions

  • NN Playground

2 Readings

  • What is a Neural Network
  • Neural Networks Explanation and History

Implementing Neural Networks

    • 1 Lab
    • 1 Assignment
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1 Assignment

  • Practice Quiz: Neural Networks using scikit-learn

1 Labs

  • Implementing Neural Networks

Module Project: Neural Networks in Python

    • 1 Lab
    • 1 Programming
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1 Programming

  • Neural Networks in Python

1 Labs

  • Programming Assignment Solutions: Neural Networks in Python

Final Project: Comparing ML Models

    • 1 Lab
    • 1 Programming
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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
Instructors

Sabrina Moore

Rajvir Dua
Instructors

Rajvir Dua

Neelesh Tiruviluamala
Instructors

Neelesh Tiruviluamala