This course is part of the AI for Scientific Research Specialization

3.8
stars
10 ratings

Sabrina Moore +2 more instructors

Offered By

AI for Scientific Research SpecializationLearnQuest

About this Course

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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.

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Shareable Certificate

Earn a Certificate upon completion

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Course 2 of 4 in the

AI for Scientific Research Specialization

Intermediate Level

Some basic Python knowledge

Approx. 11 hours to complete

English

What you will learn

Implement and evaluate machine learning models (neural networks, random forests, etc.) on scientific data in Python

Skills you will gain

Artificial Neural Network
Machine Learning
Python Programming
Random Forest
PCA

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

Some basic Python knowledge

Approx. 11 hours to complete

English

Instructors

Offered by

LearnQuest

LearnQuest is the preferred training partner to the world’s leading companies, organizations, and government agencies. Our team boasts 20+ years of experience designing, developing and delivering a full suite industry-leading technology education classes and training solutions across the globe. Our trainers, equipped with expert industry experience and an unparalleled commitment to quality, facilitate classes that are offered in various delivery formats so our clients can obtain the training they need when and where they need it.

Syllabus - What you will learn from this course

Week1

Week 1

2 hours to complete

Before the AI: Preparing and Preprocessing Data

In this module, we'll tackle the steps taken before we can use AI algorithms. We'll start with an introduction to the most prominent data preprocessing techniques including filling in missing values and removing outliers. Then we'll dive into data transformations including PCA and LDA, two methods featured heavily for dimensionality reduction. Finally, we'll learn how to code the algorithms in Python to set up your data for use in the next module.

2 hours to complete

12 videos (Total 47 min), 4 readings, 2 quizzes

12 videos

Course Introduction45sSetting Up the Environment2mModule Introduction51sAnatomy of a Dataset (I)5mAnatomy of a Dataset (II)4mData Preprocessing Techniques6mCalculating Eigenvalues and Eigenvectors5mIntroduction to PCA3mMath of PCA5mPCA in Action (I)3mPCA in Action (II)6mIntroduction to LDA2m

4 readings

Data Preprocessing10mPCA Explained10mMatrix Multiplication10mLDA in Practice10m

2 practice exercises

Practice Quiz: Eigenvalues and Eigenvectors10mData Preprocessing Techniques30m

Week2

Week 2

4 hours to complete

Foundational AI Algorithms: K-Means and SVM

In this module, we'll dive into two of the most foundational machine learning algorithms: K-Means and support vector machines. We'll start by comparing the two branches of ML: supervised and unsupervised learning. Then, we'll go into the specific similarities and differences between K-Nearest neighbors for classification and K-Means clustering. Finally, we'll perform deep dives into K-Means and SVMs, learning the basic theory behind them and how to implement each in Python.

4 hours to complete

4 videos (Total 13 min), 3 readings, 3 quizzes

Week3

Week 3

4 hours to complete

Advanced AI: Neural Networks and Decision Trees

In this module, we'll explore some advanced AI techniques. We'll start with tree-based algorithms, made popular because of the use of random forests for both classification and regression. Then, we'll build our way to neural networks, starting from experimentation on the different models. We'll spend some time in the Tensorflow playground getting familiar with the different mechanics behind neural networks. Finally, we'll code our own neural networks to make predictions on unseen data.

4 hours to complete

1 video (Total 1 min), 4 readings, 2 quizzes

Week4

Week 4

2 hours to complete

Course Project

2 hours to complete

Reviews

3.8

2 reviews

5 stars
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4 stars
10%
3 stars
40%
2 stars
10%

TOP REVIEWS FROM MACHINE LEARNING MODELS IN SCIENCE

by RJJul 7, 2022

I would have had more stars, but a couple of the programming assignments had different values for random used for the answer and not what was listed in the question.

View all reviews

About the AI for Scientific Research Specialization

In the AI for Scientific Research specialization, we'll learn how to use AI in scientific situations to discover trends and patterns within datasets. Course 1 teaches a little bit about the Python language as it relates to data science. We'll share some existing libraries to help analyze your datasets. By the end of the course, you'll apply a classification model to predict the presence or absence of heart disease from a patient's health data. Course 2 covers the complete machine learning pipeline, from reading in, cleaning, and transforming data to running basic and advanced machine learning algorithms.In the final project, we'll apply our skills to compare different machine learning models in Python. In Course 3, we will build on our knowledge of basic models and explore more advanced AI techniques. We’ll describe the differences between the two techniques and explore how they differ. Then, we’ll complete a project predicting similarity between health patients using random forests. In Course 4, a capstone project course, we'll compare genome sequences of COVID-19 mutations to identify potential areas a drug therapy can look to target. By the end, you'll be well on your way to discovering ways to combat disease with genome sequencing.

Frequently Asked Questions

When will I have access to the lectures and assignments?
Access to lectures and assignments depends on your type of enrollment. If you take a course in audit mode, you will be able to see most course materials for free. To access graded assignments and to earn a Certificate, you will need to purchase the Certificate experience, during or after your audit. If you don't see the audit option:
The course may not offer an audit option. You can try a Free Trial instead, or apply for Financial Aid.
The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.
What will I get if I subscribe to this Specialization?
When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile. If you only want to read and view the course content, you can audit the course for free.
Is financial aid available?
Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

More questions? Visit the Learner Help Center.

Machine Learning Models in Science

About this Course

What you will learn

Skills you will gain

Instructors

Sabrina Moore

Rajvir Dua

Neelesh Tiruviluamala

Offered by

LearnQuest

Syllabus - What you will learn from this course

Before the AI: Preparing and Preprocessing Data

Foundational AI Algorithms: K-Means and SVM

Advanced AI: Neural Networks and Decision Trees

Course Project

Reviews

TOP REVIEWS FROM MACHINE LEARNING MODELS IN SCIENCE

About the AI for Scientific Research Specialization

Frequently Asked Questions

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Machine Learning Models in Science

About this Course

What you will learn

Skills you will gain

Instructors

Sabrina Moore

Rajvir Dua

Neelesh Tiruviluamala

Offered by

LearnQuest

Syllabus - What you will learn from this course

Before the AI: Preparing and Preprocessing Data

Foundational AI Algorithms: K-Means and SVM

Advanced AI: Neural Networks and Decision Trees

Course Project

Reviews

TOP REVIEWS FROM MACHINE LEARNING MODELS IN SCIENCE

About the AI for Scientific Research Specialization

Frequently Asked Questions

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Learn Something New

Popular Data Science Courses

Popular Computer Science & IT Courses

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