
This video provides an overview of the course contents, a course description, a presentation of the instructor, and background information about the course
This video describes the setup procedures for using the Anaconda Cloud Notebook
Using Anaconda Cloud Notebook requires internet access and an email address
Note: Anaconda often updates its resources and user interface plus utilizes anti-drone technology. This may cause minor deviations from graphics and procedures in the video
This video describes the procedures to download and install the Anaconda Distribution for use with this course
Download requires internet access
Video is optional
Note: Anaconda often updates its resources and this may cause minor differences in graphics and procedures
This video describes the Conda Package Management System
Conda requires internet access
Video is optional
Note: Conda is a speedily developing environment and this may cause minor differences in graphics and procedures
An overview of the Classification section of the video course. A description of the Classification theory and process
Learn to use the Logistic Regression Classifier with a practical example, learn to create advanced decision surface plots, use exploratory seaborn pair plots, and learn to create useful classification reports and much more…
Learn to use the Naive Bayes Classifier. Learn some about Bayes theorem, conditional probability, model extrapolations, data quality effect on accuracy, practical modeling theory and more…
Learn to use K-Nearest Neighbor Classifier (KNN). Learn to use heuristics and graphs to determine a useful number of neighbors and learn practical hands-on classification skills for datasets with complex data structures
Learn to use the Decision Tree Classifier. Learn to Visualize Decision trees and to create corresponding Decision Surfaces.
Learn some tricks to enhance Decision Tree Classifiers performance and more...
Learn to use the Random Forest Classifier. Learn some theory about Random Forest Classifiers and importances. Learn to extract Decision Trees from a Random Forest and learn to graph importances and decision surfaces
Learn to use Linear Discriminant Analysis (LDA). Learn to use permutation importances for feature selection to overcome the complexity of environments with many features.
Learn to use ROC-curves, DET-curves, Precision-Recall graphs, and more…
Learn to use the Voting Classifier Ensemble. Learn to use the Voting Classifier as a tool to create almost arbitrary decision surfaces, Classification models, and more...
Overview of the section Advanced Machine Learning Classification Models
This video provides concepts and definitions for Artificial Neural Networks (ANN), Feedforward Networks, and Multi-Layer Perceptrons
Learn to use Feedforward Multi-Layer Perceptrons for classification tasks. Some discussions about theory and practical applications
Learn to use the advanced XGBoost Classifier. Introduction to grid search optimization with XGBoost
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 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:
Master Classification and Supervised Learning both in theory and practice
Master Classification models from Logistic Regression and Linear Discriminant Analysis to the XGBoost Classifier, and 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.
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 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:
Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommended
Basic Python and Pandas skills
Access to a computer with an internet connection
The course only uses costless software
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!