Programming Languages

(0 reviews)

In order to help students become more proficient programmers in a variety of languages, this course presents fundamental programming concepts with a focus on functional programming and design principles.

Key AI Functions:recursion,higher order,function pattern matching,function programming,computer science

Description

Reviews

Alternative Tools

Description for Programming Languages

Overview of Programming Languages: Acquire foundational programming principles via the languages ML, Racket, and Ruby, while understanding language architecture and the ability to transition between other programming languages.
Focus on Functional Programming: Highlight functional programming methodologies that are crucial for developing reusable, resilient, and sophisticated software, applicable in contemporary programming languages.
Creating Accurate and Appealing Programs: Establish a foundation for comprehending and utilizing language constructs proficiently, emphasizing program design concepts that yield clean, efficient code.
Deep Thinking Beyond Syntax: Develop the capacity for critical analysis of programming languages, emphasizing principles over syntax to improve proficiency in any language.

Level: Intermediate

Certification Degree: Yes

Languages the Course is Available: 22

Offered by: On Coursera provided by University of Washington

Duration: 29 hours (approximately)

Schedule: Flexible

Reviews for Programming Languages

0 / 5

from 0 reviews

Ease of Use

Ease of Customization

Intuitive Interface

Value for Money

Support Team Responsiveness

Featured Tools

Self-Driving Cars Specialization

MLOps in R: Deploying machine learning models using vetiver

ML Classification

Foundations of Machine Learning

Machine Learning Basics

Alternative Tools for Programming Languages

Featured Tools

Self-Driving Cars Specialization

Begin Your Professional Journey in Self-Driving Vehicles. Be at the vanguard of the autonomous driving industry.

#Self-Driving Cars #Software system

MLOps in R: Deploying machine learning models using vetiver

Acquire proficiency in the development, deployment, and monitoring of machine learning models in real-world applications through automated pipelines.

#vetiver #model deployment

ML Classification

Learn through case studies, techniques, challenges, and objectives to master classification tasks, techniques, and metrics in Python for effective machine learning on various datasets.

#Logistic Regression #Statistical Classification

Foundations of Machine Learning

Develop and evaluate machine learning models using regression, trees, and unsupervised techniques to address various business challenges.

#Logistic Regression #Unsupervised Learning

Machine Learning Basics

Learn fundamental machine learning principles, including K nearest neighbor, linear regression, and model analysis, with prerequisites of Python programming and basic mathematics.

#Machine Learning #Python

Compare & Review AI Tool Directory AI Course Marketplace