Advanced Machine Learning Methods And Techniques

Advanced Machine Learning Methods And Techniques
Published 3/2025
MP4 | Video: h264, 1920×1080 | Audio: AAC, 44.1 KHz

Learn Advanced Machine Learning Methods and Techniques for Data Analysis, Data Science, and Machine Learning

What you’ll 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

And much more.

Requirements

The four ways of counting (+-*/)

Some Experience with Data Science, or 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
Description

Overview

Section 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)

Section 2: Advanced Models for Regression and Supervised Learning

Lecture 5 Overview

Lecture 7 Feedforward Multi-Layer Perceptrons for Prediction

Lecture 8 Decision Tree Regression model

Lecture 9 Random Forest Regression

Lecture 10 Voting Regression

Lecture 11 eXtreme Gradient Boosting Regression (XGBoost)

Section 3: Advanced Models for Classification and Supervised Learning

Lecture 12 Overview

Lecture 14 Feedforward Multi-Layer Perceptrons for Classification

Lecture 15 Decision Tree Classifier

Lecture 16 Random Forest Classifier

Lecture 17 Voting Classifier

Lecture 18 eXtreme Gradient Boosting Classifier (XGBoost)

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!