Will I earn university credit for completing the Specialization?

No, this is not a university specialization.

What will I be able to do upon completing the Specialization?

You will be able to use AI and Machine Learning techniques to analyze datasets to discover patterns and to predict future values.

Is this course really 100% online? Do I need to attend any classes in person?

This course is completely online, so there’s no need to show up to a classroom in person. You can access your lectures, readings and assignments anytime and anywhere via the web or your mobile device.

Can I just enroll in a single course?

Yes! To get started, click the course card that interests you and enroll. You can enroll and complete the course to earn a shareable certificate. When you subscribe to a course that is part of a Specialization, you’re automatically subscribed to the full Specialization. Visit your learner dashboard to track your progress.

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.

Can I take the course for free?

No, you cannot take this course for free. 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. If you cannot afford the fee, you can apply for financial aid.

AI for Scientific Research Specialization

Launch Your Career in Data Science. Use artificial intelligence to discover and test hypothesis.

Instructors: Sabrina Moore

Included with Coursera Plus

Learn more

4 course series

Get in-depth knowledge of a subject

3.1

(60 reviews)

Beginner level

Recommended experience

1 month at 10 hours a week

Flexible schedule

Earn a career credential

Share your expertise with employers

4 course series

Get in-depth knowledge of a subject

3.1

(60 reviews)

Beginner level

Recommended experience

1 month at 10 hours a week

Flexible schedule

Earn a career credential

Share your expertise with employers

What you'll learn

How to use AI in scientific situations to discover trends and patterns within datasets
The complete machine learning process
Use artificial intelligence to predict sequences in datasets

Overview

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.

What’s included

Shareable certificate

Add to your LinkedIn profile

Taught in English

17 practice exercises

Advance your subject-matter expertise

Learn in-demand skills from university and industry experts
Master a subject or tool with hands-on projects
Develop a deep understanding of key concepts
Earn a career certificate from LearnQuest

Specialization - 4 course series

Each course in this specialization contains practice labs built on the Coursera lab platform. You will use the provided libraries and models to perform machine learning and AI instructions that help answer important questions in your dataset. The final course is a capstone project where you will compare genome sequences of COVID-19 mutations to identify potential areas a drug therapy can look to target. It begins with the basic setup and walks through the entire analysis process.

Introduction to Data Science and scikit-learn in Python

Course 113 hours3.7 (52 ratings)View course

What you'll learn

Employ artificial intelligence techniques to test hypothesis in Python
Apply a machine learning model combining Numpy, Pandas, and Scikit-Learn

Skills you'll gain

Scikit Learn (Machine Learning Library), Python Programming, Pandas (Python Package), NumPy, Data Structures, Data Manipulation, Regression Analysis, Machine Learning, Feature Engineering, Data Science, Predictive Modeling, Classification And Regression Tree (CART), Statistical Methods, Supervised Learning, Statistical Hypothesis Testing, Programming Principles, Exploratory Data Analysis, and Data Analysis

Machine Learning Models in Science

Course 211 hours4.0 (12 ratings)View course

What you'll learn

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

Skills you'll gain

Supervised Learning, Data Cleansing, Machine Learning Algorithms, Dimensionality Reduction, Unsupervised Learning, Predictive Modeling, Machine Learning, Artificial Neural Networks, Random Forest Algorithm, Data Transformation, Applied Machine Learning, Artificial Intelligence and Machine Learning (AI/ML), Data Processing, and Python Programming

Neural Networks and Random Forests

Course 310 hoursView course

What you'll learn

In this course, we will build on our knowledge of basic models and explore advanced AI techniques. We’ll start with a deep dive into neural networks, building our knowledge from the ground up by examining the structure and properties. Then we’ll code some simple neural network models and learn to avoid overfitting, regularization, and other hyper-parameter tricks. After a project predicting likelihood of heart disease given health characteristics, we’ll move to random forests. We’ll describe the differences between the two techniques and explore their differing origins in detail. Finally, we’ll complete a project predicting similarity between health patients using random forests.

Skills you'll gain

Random Forest Algorithm, Artificial Neural Networks, Tensorflow, Keras (Neural Network Library), Scikit Learn (Machine Learning Library), Predictive Modeling, Regression Analysis, Supervised Learning, Machine Learning, Python Programming, Deep Learning, and Classification And Regression Tree (CART)

Capstone Project: Advanced AI for Drug Discovery

Course 412 hoursView course

What you'll learn

Analyzing genome sequences to find similarities and identify target subsequences using predctive models.

Skills you'll gain

Exploratory Data Analysis, Dimensionality Reduction, Unsupervised Learning, Bioinformatics, Regression Analysis, Predictive Modeling, Data Manipulation, Artificial Neural Networks, Deep Learning, Machine Learning, Machine Learning Algorithms, Drug Development, Pandas (Python Package), Applied Machine Learning, Molecular Biology, Feature Engineering, and Scikit Learn (Machine Learning Library)