Master Classification and Feedforward Networks [2024]
Master Classification and Feedforward Networks [2024], available at $54.99, has an average rating of 5, with 15 lectures, based on 16 reviews, and has 25 subscribers.
You will learn about Master Classification and Supervised Learning both in theory and practice Master Classification models from Logistic Regression and Linear Discriminant Analysis to the Gaussian Naïve Bayes Classifier model Use practical classification hands-on theory and learn to execute advanced Classification tasks with ease and confidence Use advanced Decision Tree, Random Forest, and Voting Classifier models Use Feedforward Multilayer Artificial Neural Networks and advanced Classifier model Structures Use effective augmented decision surfaces graphs and other graphing tools to judge Classifier performance Use the Scikit-learn library for Classification supported by Matplotlib, Seaborn, Pandas, and Python Cloud computing: Use the Anaconda Cloud Notebook. Learn to use Cloud Computing resources This course is ideal for individuals who are Anyone who wants to learn to master Classification and Supervised Learning or Anyone who wants to learn to master Classification and Supervised Learning and knows Data Science or Machine Learning or Anyone who wants to learn advanced Classification skills It is particularly useful for Anyone who wants to learn to master Classification and Supervised Learning or Anyone who wants to learn to master Classification and Supervised Learning and knows Data Science or Machine Learning or Anyone who wants to learn advanced Classification skills.
Enroll now: Master Classification and Feedforward Networks [2024]
Summary
Title: Master Classification and Feedforward Networks [2024]
Price: $54.99
Average Rating: 5
Number of Lectures: 15
Number of Published Lectures: 15
Number of Curriculum Items: 15
Number of Published Curriculum Objects: 15
Original Price: $39.99
Quality Status: approved
Status: Live
What You Will Learn
- Master Classification and Supervised Learning both in theory and practice
- Master Classification models from Logistic Regression and Linear Discriminant Analysis to the Gaussian Naïve Bayes Classifier model
- Use practical classification hands-on theory and learn to execute advanced Classification tasks with ease and confidence
- Use advanced Decision Tree, Random Forest, and Voting Classifier models
- Use Feedforward Multilayer Artificial Neural Networks and advanced Classifier model Structures
- Use effective augmented decision surfaces graphs and other graphing tools to judge Classifier performance
- Use the Scikit-learn library for Classification supported by Matplotlib, Seaborn, Pandas, and Python
- Cloud computing: Use the Anaconda Cloud Notebook. Learn to use Cloud Computing resources
Who Should Attend
- Anyone who wants to learn to master Classification and Supervised Learning
- Anyone who wants to learn to master Classification and Supervised Learning and knows Data Science or Machine Learning
- Anyone who wants to learn advanced Classification skills
Target Audiences
- Anyone who wants to learn to master Classification and Supervised Learning
- Anyone who wants to learn to master Classification and Supervised Learning and knows Data Science or Machine Learning
- Anyone who wants to learn advanced Classification skills
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. 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 models
You 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 performance
You will learn to:
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Master Classification and Supervised Learning both in theory and practice
-
Master Classification models from Logistic Regression and Linear Discriminant Analysis to the Gaussian Naïve Bayes Classifier model
-
Use practical classification hands-on theory and learn to execute advanced Classification tasks with ease and confidence
-
Use advanced Decision Tree, Random Forest, and Voting Classifier models
-
Use Feedforward Multilayer Artificial Neural Networks and advanced Classifier model Structures
-
Use effective augmented decision surfaces graphs and other graphing tools to judge Classifier performance
-
Use the Scikit-learn library for Classification supported by Matplotlib, Seaborn, Pandas, and Python
-
Cloud computing: Use the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). Learn to use Cloud Computing resources.
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Option: To use the Anaconda Distribution (for Windows, Mac, Linux)
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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.
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And much more…
This course is an excellent way to learn to master Classification, feedforward Networks, and Supervised Learning for Classification
This course is designed for everyone who wants to
-
learn to master Classification and Supervised Learning
-
learn to master Classification and Supervised Learning and knows Data Science or Machine Learning
-
learn advanced Classification skills
This 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:
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Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommended
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Basic Python and Pandas skills
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Access to a computer with an internet connection
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The course only uses costless software
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Walk-you-through installation and setup videos for Cloud computing and Windows 10/11 is included
Enroll now to receive 5+ hours of video tutorials with manually edited English captions, and a certificate of completion after completing the course!
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Lecture 2: Setup of the Anaconda Cloud Notebook
Lecture 3: Download and installation of the Anaconda Distribution (optional)
Lecture 4: The Conda Package Management System (optional)
Chapter 2: Master Classification and Supervised Learning
Lecture 1: Classification and Supervised Learning, overview
Lecture 2: Logistic Regression Classifier
Lecture 3: The Naive Bayes Classifier
Lecture 4: K-Nearest Neighbor Classifier (KNN) [Extra Video]
Lecture 5: The Decision Tree Classifier
Lecture 6: The Random Forest Classifier
Lecture 7: Linear Discriminant Analysis (LDA) [Extra Video]
Lecture 8: The Voting Classifier
Chapter 3: Advanced Machine Learning Classification Models
Lecture 1: Section Overview
Lecture 2: Artificial Neural Networks, Feedforward Networks, and the Multi-Layer Perceptron
Lecture 3: Feedforward Multi-Layer Perceptrons for Classification tasks
Instructors
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Henrik Johansson
Instructor at Udemy
Rating Distribution
- 1 stars: 0 votes
- 2 stars: 0 votes
- 3 stars: 0 votes
- 4 stars: 0 votes
- 5 stars: 16 votes
Frequently Asked Questions
How long do I have access to the course materials?
You can view and review the lecture materials indefinitely, like an on-demand channel.
Can I take my courses with me wherever I go?
Definitely! If you have an internet connection, courses on Udemy are available on any device at any time. If you don’t have an internet connection, some instructors also let their students download course lectures. That’s up to the instructor though, so make sure you get on their good side!
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