
Course introduction
This video describes the setup procedures to use Anaconda Cloud Notebook
Using Anaconda Cloud Notebook requires internet access
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
Overview of the section Advanced Models for Regression and Supervised Learning
Learn concepts and theory for Artificial Neural Networks (ANN), Feedforward Networks, and Multi-Layer Perceptrons
Learn to use non-linear Feedforward Multi-Layer Perceptrons to predict values with some practical adjustments for enhanced extraordinary Prediction performance
Learn Decision Tree Regression theory and to implement and regularize Decision Tree Regression models with Scikit-learn. Learn to prepare a dataset for use with Decision Tree Regression models and how to plot Decision Tree graphs and the output of Decision Tree Regression models
Learn to use Random Forest Regression / Ensembles for Prediction and Regularization. Learn to use importances for model creation and feature selection. Learn how importances change over different subsets of a dataset
Learn to use the Voting Ensemble Regression model for prediction. Learn to use Voting Regression to augment and modify standard Regression models for extended functionality and advanced prediction
Learn to use eXtreme Gradient Boosting Regression (XGBoost)
Overview of the section Advanced Models for Classification and Supervised Learning
Learn concepts and theory 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 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 the Voting Classifier Ensemble. Learn to use the Voting Classifier as a tool to create almost arbitrary decision surfaces, Classification models, and more...
Learn to use the advanced XGBoost Classifier. Introduction to grid search optimization with XGBoost
Welcome to the course Advanced Machine Learning Methods and Techniques!
Machine Learning is expanding and developing on a massive and global scale. Everywhere in society, there is a movement to implement and use Machine Learning Methods and Techniques to develop and optimize all aspects of our lives, businesses, societies, governments, and states.
This course will teach you a useful selection of Advanced Machine Learning methods and techniques, which will give you an excellent foundation for Machine Learning jobs and studies. This course has exclusive content that will teach you many new things about Machine Learning methods and techniques.
This is a two-in-one master class video course which will teach you to advanced Regression, Prediction, and Classification.
You will learn advanced Regression, Regression analysis, Prediction and supervised learning. This course will teach you to use advanced feedforward neural networks and Decision tree regression ensemble models such as the XGBoost regression model.
You will learn advanced Classification and supervised learning. You will learn to use advanced feedforward neural networks and Decision tree classifier ensembles such as the XGBoost Classifier model.
You will learn
Knowledge about Advanced Machine Learning methods, techniques, theory, best practices, and tasks
Deep hands-on knowledge of Advanced Machine Learning and know how to handle Machine Learning tasks with confidence
Advanced ensemble models such as the XGBoost models for prediction and classification
Detailed and deep Master knowledge of Regression, Regression analysis, Prediction, Classification, and Supervised Learning
Hands-on knowledge of Scikit-learn, Matplotlib, Seaborn, and some other Python libraries
Advanced knowledge of A.I. prediction/classification models and automatic model creation
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 includes
an easy-to-follow guide for using the Anaconda Cloud Notebook (Cloud-based Jupyter Notebook). You may learn to use Cloud Computing resources in this course
an easy-to-follow optional guide for downloading, installing, and setting up the Anaconda Distribution, which makes anyone able to install a Python Data Science environment useful for this course or for any Machine Learning or coding task
a large collection of unique content, and this course will teach you many new things that only can be learned from this course on Udemy
A compact course structure built on a proven and professional framework for learning.
This course is an excellent way to learn advanced Regression, Prediction, and Classification! These are the most important and useful tools for modeling, AI, and forecasting.
Is this course for you?
This course is an excellent choice for
Anyone who wants to learn Advanced Machine Learning Methods and Techniques
Anyone who wants to study at the University level and want to learn Advanced Machine Learning skills that they will have use for in their entire career!
This course is the 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 Advanced Regression, Prediction, and classification.
Course requirements
The four ways of counting (+-*/)
Some real Experience with Data Science, Data Analysis, or Machine Learning
Python and preferably Pandas knowledge
Everyday experience using a computer with either Windows, MacOS, iOS, Android, ChromeOS, or Linux is recommended
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 10+ hours of video tutorials with manually edited English captions, and a certificate of completion after completing the course!