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Master Classification and Feedforward Networks [2025]

Develop essential data science & ai skills with expert instruction and practical examples.

Online Course
Self-paced learning
Flexible Schedule
Learn at your pace
Expert Instructor
Industry professional
Certificate
Upon completion
What You'll Learn
Master the fundamentals of data science & ai
Apply best practices and industry standards
Build practical projects to demonstrate your skills
Understand advanced concepts and techniques

Skills you'll gain:

Professional SkillsBest PracticesIndustry Standards
Prerequisites & Target Audience

Skill Level

IntermediateSome prior knowledge recommended

Requirements

Basic understanding of data science & ai
Enthusiasm to learn
Access to necessary software/tools
Commitment to practice

Who This Course Is For

Professionals working in data science & ai
Students and career changers
Freelancers and consultants
Anyone looking to improve their skills
Course Information

About This Course

Welcome to the course Master Classification and Feedforward Networks. Classification and Supervised Learning are one of the most important and common tasks for Data Science, Machine Learning, modeling, and AI. This video course will teach you to master Classification and Supervised Learning with a number of advanced Classification techniques such as the XGBoost Classifier.

You will learn to use practical classification hands-on theory and learn to execute advanced Classification tasks with ease and confidence. You will learn to use Classification models such as Logistic Regression, Linear Discriminant Analysis, Gaussian Naïve Bayes Classifier models, Decision Tree Classifiers, Random Forest Classifiers, and Voting Classifier modelsYou will learn to handle advanced model structures such as feedforward artificial neural networks for classification tasks and to use effective augmented decision surfaces graphs and other graphing tools to assist in judging Classifier performanceYou will learn to:Master Classification and Supervised Learning both in theory and practiceMaster Classification models from Logistic Regression and Linear Discriminant Analysis to the XGBoost Classifier, and the Gaussian Naïve Bayes Classifier modelUse practical classification hands-on theory and learn to execute advanced Classification tasks with ease and confidenceUse advanced Decision Tree, Random Forest, and Voting Classifier modelsUse Feedforward Multilayer Artificial Neural Networks and advanced Classifier model StructuresUse effective augmented decision surfaces graphs and other graphing tools to judge Classifier performanceUse the Scikit-learn library for Classification supported by Matplotlib, Seaborn, Pandas, and PythonCloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud Computing resources.

Option: To use the Anaconda Distribution (for Windows, Mac, Linux)Option: Use Python environment fundamentals with the Conda package management system and command line installing/updating of libraries and packages - golden nuggets to improve your quality of work life. And much more…This course is an excellent way to learn to master Classification, feedforward Networks, and Supervised Learning for ClassificationThis course is designed for everyone who wants tolearn to master Classification and Supervised Learninglearn to master Classification and Supervised Learning and knows Data Science or Machine Learninglearn advanced Classification skillsThis course is a course we ourselves would want to be able to enroll in if we could time-travel and become new students. In our opinion, this course is the best course to learn to master Classification.

Course requirements:Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommendedBasic Python and Pandas skillsAccess to a computer with an internet connectionThe course only uses costless softwareWalk-you-through installation and setup videos for Cloud computing and Windows 10/11 is includedEnroll now to receive 5+ hours of video tutorials with manually edited English captions, and a certificate of completion after completing the course.

Provider
Udemy
Estimated Duration
10-20 hours
Language
English
Category
Technology & Programming

Topics Covered

Data Science & AI

Course Details

Format
Online, Self-Paced
Access
Lifetime
Certificate
Upon Completion
Support
Q&A Forum
Course Details
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This course includes:

Lifetime access to course content
Access on mobile and desktop
Certificate of completion
Downloadable resources

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